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Building a new way for businesses to operate.

Infrastructure, a platform and a workforce of agents, built for one business and run on its own data.

Build the land your AI will live on.

A better AI model arrives every few months, and you can swap one in within an afternoon. What you can’t swap is the land it works on: your machines, your data and your rules.

A farmer with no land grows nothing.

Works with open models, licensed models and models trained on your own data. What you can swap grows on top. What Zelleo builds lies underneath.

  1. The farmer
    Your AI model

    Changes every few months. Swap it in an afternoon.

  2. The tools
    Agents

    An AI set up for one job, with rules and a person in charge. Our six products are where they do that work.

  3. The plug
    Socket

    Lets any AI model you approve onto your land, with the same permissions as the person using it.

  4. The field
    Land

    Where your AI works and your data lives: one record of your projects, enquiries, files, meetings, quotes and decisions.

  5. The soil
    Infrastructure

    The machines in your building, sized for your workload, where the agents grow and stay.

01One ecosystem

Not three products
  1. Infrastructure

    The AI and data infrastructure the business runs on, installed on its own premises. The data the agents learn from never leaves it, and nothing irrelevant gets in.

    Infrastructure
  2. Platform

    The software layer: one organised record of the business, and the six products your teams work in. Permissions are set once and everything obeys them.

    Platform
  3. Agentic Workforce

    Agents that observe, analyse, propose and execute, each scoped to one job, and one agent that represents the business to other businesses’ agents.

    Agentic Workforce

Bought separately, these three take years to connect. Zelleo builds them as one system for a single business.

02Why it matters

The data under the AI

Most AI projects don’t fail because the AI is weak. They fail because the data under them is a mess, scattered across tools that were never meant to talk to each other.

An agent can only act on what it can safely reach. Putting that record in order, on infrastructure you own, is the part that keeps paying back long after the model has been replaced.

  • 60%of AI projects will be abandoned if the data under them isn’t ready for AI.Gartner, Feb 2025 ↗
  • 95%of generative AI pilots stall before they scale. The gap is integration, not model quality.MIT NANDA, 2025 ↗
  • 97%of organisations with an AI-related breach had no AI access controls in place.IBM, 2025 ↗

Real estate and construction operations.

A development project settles its material price months in advance, then loses money to waste, rework and delay that no budget line owns. Those losses are coordination problems, and coordination is what agents are for.

Zelleo reads the construction schedule and acts on it before anyone on site notices a shortfall: computing what is needed, asking your suppliers, and matching the freight in the same step.

  • 40–60%of project cost is materials
  • 5%goes on rework, 12–15% on a bad job
  • 2 dayslate on one truck can cost a week
Pulse · Enquiries and sales

Answer every enquiry in minutes.

Nobody who asks you for a price gets forgotten, and you can see exactly how long it takes to turn a “hello” into a “yes”.

Pulse brings enquiries from every channel into one inbox, drafts a reply within minutes, scores how likely each one is to buy and shows where enquiries drop out. A person approves every quote.

The real problem

You don’t have a sales problem. You have a response-time problem you can’t see, because enquiries arrive in five places and are tracked in none.

Interactive demo · sample data
Conversations {{ pulse.inboxCount }}
{{ c.name }} {{ c.time }}
{{ c.meta }}
Hot Window {{ c.clock }}
Score {{ c.score }}
{{ pulse.leadInitials }}
{{ pulse.leadName }} {{ pulse.leadMeta }}
Score {{ pulse.score }}
{{ m.text }}
{{ m.text }}
{{ m.text }}
Sales agent · approved playbook v4
Draft quote · Q-1042 Unit B-1402 · cost sheet for approval
{{ pulse.draftState }}
The sales agent drafted this quote. It is not sent until you approve it.
Intent Score
{{ pulse.score }}

{{ pulse.scoreNote }}

Next best action

{{ pulse.nextAction }}

Stage
{{ s.name }}
Ghost Rate 0% · Time-to-Yes {{ pulse.tty }}
Leak Map

Where 128 active leads stand today, and how many advance to the next stage.

Overall win rate
16.4%
{{ f.name }} {{ f.count }} {{ f.conv }}
Lead sources · 1,284 leads, 90 days
Web form 44% · Website chat 27% · Property portals 18% · Email 11%
Enquiry → qualified
3.2 days, down from 5.1
Why leads drop
No budget given: 9 · Went quiet: 14 · Lost on price: 7

How it works

Step 1Capture everything

Chat, WhatsApp, email, web form, card scan and event QR land in one inbox.

Step 2Reply in the Hot Window

First replies follow your approved playbook within 5 minutes, and every response time is logged.

Step 3Score the intent

A 0–100 Intent Score tells your team who to call first. 70+ means need, budget and timeline are confirmed.

Step 4Approve and win

A person approves every quote. Won deals hand over to Crew with full context.

Measure your Ghost Rate (enquiries with no reply) for one week. The target is zero.

Pulse answers every enquiry in minutes and shows you where the rest leak out.

Logged and reversible

Every agent action on an enquiry is logged, and status changes can be undone.

Quotes wait for approval

Quotes, and any reply outside the approved playbook, stay drafts until a person approves them.

Models on your servers

On a Ground deployment, the model that scores your enquiries runs on your own servers.

Illustrative figures

Every figure in the demo comes from a sample data set, not a customer result.

Crew · Teams, files and tasks

Work together. Keep every file sealed.

A shared office where every drawer has a lock and a logbook, and a helpful assistant reminds people what’s due.

Crew is where your teams share files, run projects and track work. Every file is sealed to the people who should see it, and an AI keeps the work moving.

The real problem

Most AI risk is an access problem, not a model problem. If a file doesn’t know who may open it, any AI you plug in inherits the mess.

Interactive demo · sample dataSales agentProjects agentFinance agent
Vault Room · Tower B 4 files · 2 people outside the company
{{ f.cls }}
Ask across your files

Answers only ever cite files you are allowed to open.

A 14-day grace period, then 0.5% of the package value per week, capped at 5%. Agreed in the 12 Sep addendum.

Source · Kestrel addendum v3.pdf

1 file was not used because it is sealed to Finance.

Work done

Every task, file seal and approval this team completed, by day. Click a square.

Less More
{{ crew.graphDay }} {{ crew.graphDetail }}
{{ s.label }}
{{ s.value }}
{{ s.note }}
Flow Rate
{{ crew.flowRate }}%

Work finished on time versus work planned, this sprint.

Drag report

{{ crew.drag }}

Access Trail

{{ crew.selName }}

{{ e.what }}
{{ e.who }}

How it works

Step 1Seal at upload

Each file learns who may open it. The seal travels when it is moved or shared.

Step 2Open Vault Rooms

One room per client, deal or project. Outsiders see only their room.

Step 3Log every touch

The Access Trail records who opened, edited, downloaded or shared, when and from where.

Step 4Let Nudges move work

Flow Board tasks and gentle reminders keep the team on time. The Drag Report shows where work is stuck.

Ask: “If an employee’s AI assistant read our files today, what could it see?” Crew’s answer is: only what that employee could.

Crew makes access the default, so adding AI doesn’t add risk.

Encrypted at rest

Role-based access, watermarking on sensitive downloads and expiry links.

Classification

Public, internal, confidential or restricted — suggested by AI, confirmed by a person.

Tasks arrive on their own

From Boardroom’s Action Catch and from Pulse when a deal is won.

Answers respect access

File questions are answered only from files you may open. In a Ground deployment, the model runs on your own servers.

Boardroom · Meetings

Every word spoken, kept, timed and linked.

A meeting room with a perfect memory. Nothing said is lost, and everything said can be traced back.

Boardroom runs your meetings from start to finish — finds the time, records and transcribes, timestamps every point, links it to the right documents, and turns promises into tasks.

The real problem

Meetings are where decisions are made and where records go to die. The fix is not fewer meetings. It is a record nobody has to write.

Interactive demo · sample data
Free Window · five calendars
Seat Map

{{ board.seatNote }}

{{ p.initials }}
{{ p.name }}
{{ p.org }}
{{ p.role }}
Spoken Ledger · live
{{ l.initials }}
{{ l.who }} {{ l.at }}

{{ l.text }}

Action Catch · {{ l.action }} Thread-Back · {{ l.doc }}
00:00{{ board.pinCount }} Time-Pins dropped14:00
Documents in the room
{{ d.name }}
{{ d.note }}
Consent

Everyone in this room accepted recording. The ledger is sealed to the five attendees.

Summary and decisions

Kestrel Builders accepted the revised delay terms in principle, subject to a 5% cap. BOQ rates hold until 31 Dec. A site walk was set for Thursday 14:00.

Decision · 5% delay-penalty cap agreed · Time-Pin 06:12
Decision · BOQ rates held to 31 Dec · Time-Pin 09:48
Action Catch
{{ a.title }}
{{ a.owner }} · {{ a.due }}
{{ a.state }}

How it works

Step 1Capture

Free Window finds a slot across time zones and calendars. Live transcription records who said what, in the languages your teams use.

Step 2Pin

Time-Pins stamp every point, decision and promise. Click one to hear the moment.

Step 3Catch

Action Catch turns “I’ll send the revised terms by Friday” into a Crew task with an owner and a date.

Step 4Thread back

Everything files to the Land Record. Thread-Back links a sentence to the contract or email it concerns.

Pick one weekly client call. Run it through Boardroom for a month and count promises that became tasks.

Boardroom turns every call into a searchable record and every promise into a task.

Margin · Quoting and pricing

Price every quote against your target.

Before anyone sends a price, Margin asks “does this help us hit the number?” and suggests the bid that does.

Margin links your sales targets to every quote you send. The Bid Engine builds bids, checks each one against what you need to hit this month, and matches quotes to targets automatically.

The real problem

Pricing is fast only when costs, rate sheets and past wins are in one place. If they live in four spreadsheets, no AI can bid for you.

Interactive demo · sample data · estimates are illustrative
Bid Engine

Façade maintenance · Tower B · 12-month service contract

Labour$4,120
Materials$ 880
Access equipment$ 340
Supervision and risk$ 860
Cost floor$6,200
{{ mg.priceLabel }} {{ mg.bandNote }}

Win Band is the price range that has usually won this kind of contract without leaving money on the table.

Target Lock · October Facilities team · Q4 contracts
Won $112kThis bid {{ mg.bidValue }}Target $180k
Gap-to-Target {{ mg.gapLabel }}
Quote match

{{ mg.matchText }}

Floor Guard · needs Finance Steward
Forecast

{{ mg.forecast }}

How it works

Step 1Lock the target

Target Lock ties every quote to a live target by month, team, product or region.

Step 2Build the bid

The Bid Engine drafts from costs, rate sheets, past wins and capacity.

Step 3Match the quote

Quote Match shows whether each quote closes the Gap-to-Target or widens it.

Step 4Guard the floor

Floor Guard blocks any price below margin without a named approver.

Join the early-access list.

Margin checks every quote against the number you need, before you send it.

Council · Decision review

A second look at every decision.

Your best manager, checking every decision at once, explaining each call — with a human still holding the final say.

Council creates Stewards: agents that apply the limits your managers sign off, built from their policies, past approvals and stated priorities. They approve the routine ones inside limits and escalate the rest, with the reason.

The real problem

Most governance runs through one central team, which becomes a bottleneck. Zelleo’s answer is Fork & Federate: central standards, local judgement.

Interactive demo · sample data
Decision feed

Four decisions waiting on the Sales Head Steward.

{{ council.selTitle }}

{{ council.selMeta }}

{{ council.verdictLabel }}

{{ council.reason }}

Decision Trace
{{ t.what }}
{{ t.who }}
Clone Charter

Signed off by Sarah Malik, Sales Head. Change a limit and re-run the Second Look.

May approve discounts up to {{ council.discount }}%
May release payments up to $30k
Must escalate Structural changes · anything over limit

{{ council.whatChanged }}

Fork and federate

Central standards, local judgement. One Steward copies out to every project and reports into one trace.

{{ b.name }}

How it works

Step 1Write the Clone Charter

A manager signs what the Steward may approve, its limits and what it must escalate.

Step 2Run a Second Look

The Steward reviews decisions as they happen, in every team.

Step 3Record the Decision Trace

Inputs, rule applied and approver are saved for every call.

Step 4Keep the Override Lane open

A human can overrule any Steward, any time, and it is logged.

Draft one Clone Charter for one routine approval and see how much of it a Steward could clear.

Council copies your best judgement to every team and logs every call.

Safeguards we publish

Stewards act only within their Clone Charter. High-impact decisions always need a human. Anyone affected by a decision can see that an agent reviewed it, and a person can reverse any Steward decision through the Override Lane.

Verdict · AI testing

Test your AI the way you test your people.

Before a new AI gets a job it sits your company’s exam. After it is hired, it keeps getting checked.

Verdict scores every AI working on your land, on your own tasks, against your own standards. Its Judges can be copied to any team and tuned locally.

The real problem

If you can’t score an AI on your own work, you can’t tell a good swap from a bad one.

Interactive demo · sample data
Model Gate Task: draft buyer replies
Current
zl-ops-8b
{{ vd.curScore }}
$0.004 / task · 410 ms
Candidate
zl-ops-8b-forge-r4
{{ vd.candScore }}
$0.003 / task · 380 ms

{{ vd.gateNote }}

Golden Set {{ vd.progressLabel }}
{{ f.text }}

Your own test questions with known good answers, per workflow. Failed answers feed Forge.

Scorebook
8 weeksJudge: reply accuracy
Drift alarm

Reply accuracy fell 6 points over two weeks on Tower B. The old price list is still being cited. Verdict has paused promotion and opened a Forge ticket.

Judge Fork
{{ j.name }}
{{ j.rule }}
{{ j.score }}

How it works

Step 1Build the Golden Set

Your own questions with known good answers, per workflow.

Step 2Fork a Judge per team

Headquarters sets the minimum bar. Each team adds its own checks (Judge Fork).

Step 3Gate every model

The Model Gate: a new model must pass the Golden Set, or the old one stays.

Step 4Watch for drift

The Drift Alarm fires when live scores slip. The Scorebook shows trends leadership can read.

Write ten Golden Set questions for your most-used AI task and score today’s model on them.

Verdict makes “is this AI any good?” a number you can check.

Forge · Model training

Your own AI model, at a spend you choose.

A factory for making your company’s own AI. You set the budget, and Forge works out the best model you can get for it.

Forge cleans your data, picks the cheapest way to reach the quality you need, trains while your hardware is idle, tests the result with Verdict, and puts it to work in a Plot.

The real problem

Training starts with cleaning. Most teams budget for the training and discover they should have budgeted for the data.

Interactive demo · estimates are illustrative
{{ forge.stepTitle }}

{{ forge.stepBody }}

{{ forge.budgetLabel }} {{ forge.gpuLabel }}
Night Shift · Training hours used
00:00{{ forge.nightNote }}24:00
Forge Plan · ranked for this budget
{{ p.name }} {{ p.chip }}

{{ p.note }}

Quality {{ p.quality }} {{ p.gpu }} hours {{ p.time }}
Ship to Plot

{{ forge.shipNote }}

How it works

Step 1Pick the job

One workflow, such as drafting buyer replies.

Step 2Refine the data

The Data Refinery cleans, de-duplicates, labels and strips personal data from the Land Record.

Step 3Plan the spend

A Forge Plan prices the options (cheap and fast, balanced, best) before anything trains. The Spend Dial caps the budget.

Step 4Train on Night Shift

Training uses hours your agents are not using, so you don’t buy extra capacity.

Step 5Test and ship

The model passes the Verdict Model Gate, then Ship-to-Plot. The model weights stay on your Ground.

Ask for a Forge Plan on one workflow. It’s an estimate, not a commitment.

Forge turns your cleaned data into your own model, inside a budget you set.

Land and Socket · The platform layer

One organised record. Controlled access for every model.

Land organises your records, policies and permissions. Socket is how an approved AI model uses them, with the permissions of the person it acts for.

Land sits between your infrastructure and your agents. Models, runtimes and agents connect to it, while your records, permissions and audit trail stay in one place.

Interactive demo · sample data
Socket · connect an approved model

Each AI only sees what the person using it is allowed to see.

{{ p.name }}
{{ p.via }}
Permissions in force

{{ land.permission }}

{{ land.answer }}

Access Trail
{{ t.what }}
{{ t.who }}
Model Swap · routing table Requests route by cost, speed, sensitivity and Verdict score
TaskModelCostLatencyGate
{{ r.task }} {{ r.cost }} {{ r.latency }} {{ r.gate }}

How it works

Step 1Build the Land Record

One shared memory of your customers, files, meetings, deals and decisions.

Step 2Fence it into Plots

A separate space per team or workflow, with its own models, data access and rules.

Step 3Open the Socket

Any approved AI plugs in with the permissions of the person using it.

Step 4Swap models freely

Change the AI model without moving your data, permissions or logs.

Your data, any assistant, same permissions.

Agentic Workforce

A workforce of agents, built for one business.

Not one assistant. A hierarchy of specialised agents, each with its own scope, permissions and limit on what it may decide alone.

They are grown inside your own infrastructure, on your own data. That is what makes them useful, and it is what keeps them contained.

See the hierarchy

01The hierarchy

One business · many agents

Work moves down the hierarchy and decisions move back up it. Each level sees only what its job needs, and one agent faces outward on behalf of the whole business.

Outward facing Representative agent

Speaks for the business to other businesses’ agents: asks, quotes, and commits within a mandate you set.

  1. Observe

    Reads what the business records: orders, schedules, documents, messages.

  2. Analyse

    Makes sense of it against policy and history, and finds the exception that matters.

  3. Propose

    Recommends an action to a person, with the reasoning attached.

  4. Execute

    Carries out what it is authorised to, and records what it did.

  5. Oversee

    Watches the other agents. It reports and never decides on the business.

Your own infrastructure One record of the business, on hardware you own. The agents grow here and cannot reach past it.

02The roster

Scoped to human permissions

Agents mirror roles rather than objects. One agent per record collapses at twelve thousand part numbers; one agent for the whole company cannot be given sensible permissions.

AgentWhat it ownsMay act aloneEscalates when
AllocatorWhich supplier or site a requirement goes toReserve and release stock, quote above your floorA price falls below the floor, or a credit limit trips
PromiserThe delivery window it will defendCommit a window inside its calibrated bandThe band is wider than you can tolerate
PlannerKeeping the schedule feasiblePropose a sequence, flag a shortageA work order would be committed, or overtime is involved
ExpediterCommitments drifting towards breachRe-route, reassign a vehicle, warn a counterpartyA breach is certain, or a penalty clause triggers
ConciergeQuestions, drafts and document bundlesAnswer, draft, assemble, translateA request would need it to act rather than explain
StewardThe other fiveNothing on the business itselfAlways: it reports, it does not decide

If two agents disagree, it goes to a person. Disagreement is rare, specific, and usually a real business tension somebody should know about.

03Decisions by risk

Thresholds you set

Every action is classified before an agent touches it. The agent handles what is routine and confident; a person is pulled in as the stakes rise.

  1. Low risk The agent decides

    Routine, reversible and inside the threshold. It acts and records what it did.

    Reserving stock · reassigning a vehicle · chasing a document
  2. Medium risk The agent proposes

    It prepares the action and the evidence. A person approves or declines, with a reason that is kept.

    Accepting a slot · re-sequencing work · raising a requisition
  3. High risk A person decides

    The agent may assemble the case but never the decision. These stay with a named person by design.

    Pricing below the floor · overtime · payments · sharing data outside

How an action earns more autonomy. It moves up only when its agreement with human decisions clears a stated bar, measured by running it in shadow beside your team rather than asserted in a meeting.

04Why they stay contained

The usual fear, answered

Most agent products are a wrapper around someone else’s model, reaching out to data and services you do not control. The worry is reasonable, and the answer is architectural rather than reassuring words.

  • Built per business

    Agents are configured around one company’s records, policies and roles. There is no shared tenant to leak across.

  • Grown on your data

    They learn from the company’s own records, so nothing irrelevant is pulled in and nothing is sent out to be understood.

  • Permissions, not prompts

    What an agent may read is enforced when data is fetched, not by an instruction a model could be talked out of.

05Between businesses

Once every business has agents, the slow part is no longer the work. It is the asking: the calls, the quotes, the chasing between firms.

Each business’s representative agent can hold that conversation directly with another’s, inside mandates both sides have signed. An enquiry that takes three days of phone calls takes minutes, and both firms keep one record of what was agreed.

  1. 01A buyer’s agent asks for a grade, a quantity and a window.
  2. 02Your agent checks stock, commitments and your floor price.
  3. 03It answers, or escalates to you if the value is above your threshold.
  4. 04Both sides countersign, and hold the same record of the commitment.

Agent-to-agent exchange is a direction we are building towards on published standards, not a capability we are claiming today. Nothing an unknown agent sends can bind your business.

Platform

The software your business actually runs on.

One organised record of the business, and six products that work on it.

Projects, documents, transactions, policies and permissions in one place, with controlled access for every model and agent that touches them.

See the six products

01The six products

Each has an interactive demo

Each is built for one job, and all six share one record of your business. A deal in Pulse, a task in Crew, a promise made in Boardroom and a target in Margin are part of one picture rather than six.

  1. PulseEnquiries that go cold before anyone replies.One inbox for every channel, a reply inside minutes from an approved playbook, intent scored, and quotes drafted for a person to approve.Explore demo
  2. CrewFiles and tasks nobody can account for.Each file sealed to the people who should see it, every open, edit and share logged, and a room per project or partner.Explore demo
  3. BoardroomDecisions made in meetings and lost after them.The meeting recorded and transcribed, the moments that matter pinned to the documents they concern, and commitments turned into tasks with owners and dates.Explore demo
  4. Margin Quotes sent without checking the target.Each quote checked against the number you need to hit, drafted from your own costs and past results, with nothing below the floor sent without a named approver.Preview demo
  5. CouncilRoutine approvals queued behind busy people.The limits your managers sign off, applied to routine decisions as they happen, with everything outside them escalated with its reason and traced.Explore demo
  6. VerdictNo way to tell if a model is good at your work.Every model tested on your own tasks before it is used, a gate that stops it going live until it passes, and a drift alarm when live scores slip.Explore demo

02One record underneath

Land and Socket

Land is the record: projects, units, contracts, schedules, suppliers, buyers, documents, policies and permissions, held in one place.

Socket is the controlled connection an approved AI model uses to reach it. A model connecting through Socket inherits the permissions of the person it acts for, rather than the permissions of whoever installed it. An employee’s assistant sees what that employee can see; a partner’s agent sees one room and nothing else.

  • Permissions once

    Access is defined in the platform and every product and agent works inside it.

  • Plots per team

    A separate space for a team or workflow, with its own data access, models and rules.

  • Any approved model

    Open-weight, licensed or trained on your own data. Changing it moves no records, rules or logs.

  • One audit trail

    What was proposed, approved, by whom and why, beside the records it changed.

03Your own model, if you need one

Most organisations do not need to train a model. A good general model with excellent context beats a mediocre custom one with poor context nearly every time.

Where it does make sense, Forge prepares the data first, prices the options against a budget you cap before a single hour is spent, trains on capacity your agents are not using, and ships only after the result passes your own test set.

  1. Refine the dataCleaned, de-duplicated and stripped of personal data before training.
  2. Plan the spendOptions priced against a budget you cap, before anything runs.
  3. Train on idle hoursTraining claims capacity the working day is not using.
  4. Test, then shipIt must pass your own test set, and its weights stay with you.
Real Estate Operational Efficiency

Your price is fixed.
Your waste isn’t.

Zelleo reads your construction schedule and acts on it before anyone on site notices a shortfall.

01The problem

After the price is agreed

You negotiated the material rate months ago and it is already as low as it goes. What is still open is everything that happens after it: the waste, the rework and the delay that no budget line owns.

  • 40–60%of project cost is materials. That rate was settled months ago.
  • 5%goes on rework, 12–15% on a bad job. It appears in no line item.
  • 2 dayslate on one truck can cost a week, when the pour window is missed and the crew waits.

Standard wastage allowances in rate analysis are the floor, not the total: roughly 10% on sand and site-mixed concrete, 8% on bricks, 5% on cement and 3% on steel (CPWD DSR). Rework figures: Construction Industry Institute RS-153.

02Where it goes wrong

Five bottlenecks
  1. The shortfall is found on site

    Nobody knows the material is short until the work in front of it stops. Every other cost starts here.

  2. Three to five days to confirm one order

    A manager calls a dealer, the dealer rings three yards, each quotes blind. The answer arrives after the window that needed it.

  3. Nobody re-sequences the freight

    A two-day slip is not matched by a change to the dependent trades, so the slip compounds into a week.

  4. Approvals wait for a person to be free

    Prices, discounts and substitutions queue behind whoever can sign, in a chain that moves in days while the site moves in hours.

  5. The record is in a messaging thread

    What was promised and what arrived live in chat, so a dispute takes two clerks days to reconstruct.

03What Zelleo does

Order against the schedule

We order against your programme rather than against a shortfall. What actually arrived goes back into the schedule, so next month’s forecast is better than this one’s.

  1. Reads your scheduleThe programme you already keep. No new system for your team to adopt.
  2. Computes the needA date moves, and it works out which material and quantity that changes.
  3. Asks your suppliersYour panel, your rates, your approvals. Directly, and all at once.
  4. Books the freightThe truck is matched to the accepted slot in the same step.

Eight steps, and your team touches two: updating the schedule, and working. The middle band has no phone calls in it.

04You keep the limits

Written once, reviewed as exceptions

Agents negotiate inside limits you write once. Anything outside them stops and waits for a person, so you review the exceptions rather than every order.

  • A price ceiling per grade
  • An approved supplier list
  • A maximum quantity per order
  • A minimum notice window

Stays exactly as it is

  • Your suppliers and negotiated rates
  • Your QA, grades and approvals
  • Your credit terms and payment cycle
  • Your right to override any allocation
  • Your dealer relationships

All Zelleo adds

  • Allocation across the panel you already have
  • The record of what was promised, and what arrived

No buying, reselling, lending or stock of our own. We never become your supplier.

05The pilot

One project · one trade · ninety days
  1. Days 1–14BaselineFive waste numbers from your last project, set with your own quantity surveyor in one afternoon.
  2. Days 15–60Run liveOne trade on one site. Your panel, your limits, with your usual process running beside it.
  3. Days 61–90CompareThe same five numbers, measured by the same surveyor, against your own baseline.
For suppliers and yards

Fewer calls. More loads.

When a builder’s schedule moves, your yard is asked directly, with the grade, quantity, delivery window and site access already attached. You accept, counter or decline against rules you wrote once.

  • No bidding war
  • No broker
  • No exclusivity

Price sits inside a floor you set and is one of six inputs in the allocation. Lose a load and we tell you which input lost it.

Infrastructure

Your data stays
in your company.

Zelleo provides the data infrastructure your business runs on, installed on your own premises, whatever your scale.

01The soil, not the farmer

Ask where your data is. The answer should be a place you can point to.

A model can be swapped in an afternoon. The record underneath it, and the place that record lives, is what you keep. We build that part, so your agents grow in your business rather than somewhere you rent by the hour.

  • Nothing sensitive leaves

    The platform, the agents and the records they read all stay on infrastructure you own.

  • No junk reaches the agents

    An agent is only as good as what it reads. Here it reads one organised record of your business and nothing else.

  • The agents cannot reach out

    They are grown inside your infrastructure and scoped to it. There is no general route out for them to fetch from or leak to.

  • Residency you can show

    Where your data sits is a place you can walk into, which is an easier answer to give a client or a regulator.

02What you get from owning it

The value, not the parts list
  • Your context, kept

    Every enquiry, document and decision accumulates in one place. A company that started recording a year ago has a year of its own history to work from. One starting today has a day.

  • Your models stay yours

    A model trained on your data keeps its weights with you. Leaving a vendor does not cost you the work that went into it.

  • It keeps running

    Operations that depend on a link to someone else’s building stop when that link does. Yours keeps working.

  • Cost you can predict

    Capacity you own is bought once rather than metered forever, and the bill does not grow every time the business uses the system more.

  • One answer in procurement

    Data residency, access control and audit stop being a questionnaire and become a deployment you can describe.

  • Ready on arrival

    It is set up for your business before it reaches you: your records, permissions, approval rules and boundaries already in place.

03Sized to your business

A site office to a group

You tell us how many people use it, how many sites you run and what work it has to carry. We size it from that, not from a price list.

  1. SiteA branch, yard or site office. Small enough to sit in the corner of a room, and it keeps working when the connection does not.
  2. OfficeA single office or department, carrying that team’s agents and document work.
  3. HeadquartersA head office with several sites reporting into it, running many agents at once.
  4. GroupA large multi-project organisation, with room to train on your own data alongside the live work.
  5. Dedicated roomNo suitable space on site? A self-contained room is delivered to you, ready to switch on.

04Where it can run

On site · hybrid · hosted
  • In your building

    Everything on your premises: the platform, the agents and your records. Nothing sensitive is sent out.

  • Hybrid

    Sensitive work stays with you, with extra capacity in a location you choose for the peaks.

  • Hosted

    Dedicated infrastructure in a facility or region you choose, without a server room of your own.

Where you require it, the systems holding your data run with no outbound route at all. That is a boundary agreed and written down with you, not a property of the hardware on its own.

← Real Estate Operational Efficiency Case study

Where the money actually goes on a project.

A development project settles its material price months in advance, then loses margin to things no budget line owns.

The allowance you already budget

Every rate analysis carries a wastage factor, and everyone in the room agrees it is normal. In Indian practice those allowances run to roughly 10% on river sand, M-sand and site-mixed concrete, 8% on clay bricks, 5% on cement and 3% on TMT steel, following CPWD DSR.

The point is not that these numbers are wrong. It is that they are the floor. Rework sits on top of them, running at about 5% of project cost on average and 12–15% on a bad job, according to the Construction Industry Institute’s RS-153. Materials are 40–60% of project cost to begin with, so a few points of avoidable loss on top of an already-negotiated rate is a larger number than most price renegotiations will ever recover.

None of this appears as a line item, and nobody owns it. That is what makes it survive year after year.

Five bottlenecks

Each of the costs above traces back to the same thing: finding out late. These are the five places where a project loses the time it needed.

  1. The shortfall is found on site

    Nobody knows the material is short until the work in front of it stops. Every other cost on this page starts here.

  2. Three to five days to confirm one order

    A manager calls a dealer, the dealer rings three yards, each quotes blind. The answer arrives after the window that needed it, and the yard that lost learns nothing about why.

  3. Nobody re-sequences the freight

    A two-day slip on one delivery is not matched by a change to the dependent trades. The pour window is missed, the next is seven days out, and the crew is paid to wait.

  4. Approvals wait for a person to be free

    Prices, discounts and substitutions queue behind whoever can sign, in a chain that moves in days while the site moves in hours.

  5. The record is in a messaging thread

    What was promised, what arrived and who agreed to it live in chat. A dispute then takes two clerks several days to reconstruct from evidence nobody filed.

Two worked scenarios

Illustrative.

Scenario · Structural steel

A two-day slip that would have cost a week

Situation
Rebar fixing for a podium slab is sequenced behind a steel delivery. The supplier’s dispatch slips by two days.
Today
The site learns of it on the morning of the pour. The window is missed, the next is seven days out, and the crew is paid to wait.
With Zelleo
The slip is detected against the programme. The agent re-asks the panel for the quantity in the new window, proposes a re-sequence of two dependent activities, and the project manager approves. The pour moves by half a day.
Measured by
Slip per pour window, and crew idle hours against the baseline.
Scenario · Aggregates

Ordering against the programme, not the gap

Situation
A high-reorder trade with several yards on the approved panel and a price ceiling per grade.
Today
A shortfall is spotted on site, a dealer is called, three yards quote blind, and the order lands three to five days later at whatever the spread allows.
With Zelleo
The requirement is computed from the programme and sent to the panel at once. Offers inside your ceiling are accepted automatically; anything outside it stops and waits for a person.
Measured by
Waste above the allowance, and time from requirement to confirmed order.

The supplier’s side

The same coordination gap costs the yard at the other end of the call. Up to 40% of truck time in India runs empty, on NITI Aayog and RMI figures, and the driver, the diesel and the loan are paid either way. Confirming a single order takes three to five days, and a yard that quotes blind and loses is told nothing about why.

When a builder’s schedule moves, Zelleo asks the yard directly, with the grade, quantity, delivery window and site access already attached. The yard accepts, counters or declines against rules it wrote once, and the freight attaches to the accepted slot in the same step. Price sits inside a floor the yard sets and is one of six inputs in the allocation, alongside distance, stock on hand, the window promised, the grade required and committed load. Four of those six improve by operating well rather than by discounting.

A dealer who carries credit, breaks bulk, holds stock and absorbs quality disputes is doing real work and keeps their margin. What the system removes is the hand paid purely for the introduction.

The ninety-day pilot

One project, one trade, measured against your own last project rather than our forecast.

  1. Days 1–14BaselineFive waste numbers from your last project, set with your own quantity surveyor in one afternoon. Nothing else changes.
  2. Days 15–60Run liveOne trade on one site. Your panel, your limits, allocation on every load, with your usual process running beside it.
  3. Days 61–90CompareThe same five numbers, measured by the same surveyor, against the baseline you gave on day fourteen.
MeasureTypical todayPilot targetStatus
Rework5% of project cost3%To be measured in the pilot
Waste above the allowance8% of ordered quantity5%To be measured in the pilot
Inbound freight running empty40% of truck time32%To be measured in the pilot
Slip per pour window2 daysHalf a dayTo be measured in the pilot
Materials as a share of cost40–60%—Established
Rework, worst case12–15% of project cost—Established · CII RS-153

We choose a high-reorder trade deliberately. Ninety days of fixtures gives four data points; ninety days of steel or aggregates gives forty.

What we believe

Construction procurement did not get slow because the people in it are slow. It got slow because the chain is human at every link, and a human chain cannot re-sequence three suppliers and a transporter in an afternoon. Every attempt to fix that with another dashboard has added a screen for someone to check rather than removing the checking.

What changes now is that software can hold a negotiation rather than record one. A schedule becomes a demand signal, an agent can ask a panel at once instead of a dealer calling three yards in sequence, and the answer arrives before the shortfall rather than after it.

We think the firms that adopt this first will not describe it as AI. They will describe it as having stopped losing the week.

Stated plainly. Zelleo is at pilot stage. The established rows above are third-party figures with their sources named; the targets are what a pilot is designed to settle on your project, measured by your own surveyor.

← Agentic Workforce Case study

The agentic internet, and the cost of asking.

Most of what a business spends on coordination is spent on asking other businesses questions and waiting for answers.

Where this is going

The first wave of business AI was a box you type into. It was useful, and it was limited in a predictable way: the model knew a great deal about the world and almost nothing about your company, so a person had to carry context to it every single time.

The second wave is agents that hold the context themselves because they live where the records are. Once that is true, the interesting question stops being what a model knows and becomes what an agent is allowed to do.

That is why we think the shape of business software is about to change rather than improve. A system designed around agents has fewer screens, not better ones. Work arrives as a decision with its evidence attached, and the human contribution is approving, declining or correcting it with a reason. The direction of travel is the fewest possible tabs and buttons.

Between businesses

Inside one company, agents are an efficiency story. Between companies, they are something larger.

Every business will have a top-level agent that represents it to the outside: one that can be asked for availability, quote a price inside a floor, commit a delivery window and sign what it agreed to. When both sides of a transaction have one, the asking collapses. An enquiry that takes three days of phone calls takes minutes, and both firms hold the same record of what was promised.

  1. 01A buyer’s agent asks for a grade, a quantity and a window.
  2. 02Your agent checks stock, committed load and your floor price.
  3. 03It answers, or escalates to you if the value is above your threshold.
  4. 04Both sides countersign, and hold the same record of the commitment.

Two things make this safe rather than alarming. The first is tiering: an unknown agent can ask a question and get an honest, non-binding answer, and nothing it sends can bind your business. Only a counterparty you have a contract with can receive a binding commitment, and only one you have granted a mandate to can commit money, within a limit and an expiry you can revoke.

The second is that the protocol is not ours. Agent-to-agent exchange is being standardised in public, and we would rather apply a standard that large platforms already support than invent one and ask an industry to adopt it.

This is a direction we are building towards, not a capability Zelleo is claiming today.

The roster

Inside the business, agents mirror roles rather than objects. One agent per object collapses at twelve thousand part numbers; one agent for the whole company cannot be given sensible permissions. Role agents sit where permissions, evaluation and accountability already line up with how the company works.

AgentWhat it ownsMay act aloneEscalates when
AllocatorWhich supplier or site a requirement goes toReserve and release stock, quote above your floorA price falls below the floor, or a credit limit trips
PromiserThe delivery window it will defend, and its uncertaintyCommit a window inside its calibrated bandThe band is wider than you can tolerate
PlannerKeeping the schedule feasiblePropose a sequence, flag a shortageA work order would be committed, or overtime is involved
ExpediterCommitments drifting towards breachRe-route, reassign a vehicle, warn a counterpartyA breach is certain, or a penalty clause triggers
ConciergeQuestions, drafts and document bundlesAnswer, draft, assemble, translateA request would need it to act rather than explain
StewardThe other fiveNothing on the business itselfAlways: it reports, it does not decide

Each agent can read only what the human role it mirrors can read, enforced when data is fetched rather than by instruction. If two agents disagree, it goes to a person: disagreement is rare, specific, and usually a real business tension somebody should know about.

When a model provider has an outage, agents fall back to the written rules and say so visibly. A business will forgive “we fell back to your rules and told you”. It will not forgive an error page.

Earning autonomy

Autonomy is set per action and per business, with a value threshold, and it moves in one direction only when the evidence supports it.

  1. ManualA person does it. The system records it.
  2. AssistedThe system drafts. A person decides.
  3. Semi-automatedThe agent proposes. A person approves.
  4. Highly automatedThe agent acts. A person is told and may reverse it.
  5. Fully autonomousThe agent acts inside a mandate. A person oversees.

Under every rung sit the same three safeguards: a new rule is simulated against the last month of real work before it fires on anything live, a review has a stated timeout, and there is always a named person to hand off to.

An action only moves up a rung when its agreement with human decisions clears a stated bar, with its confidence interval, measured by running it in shadow beside your team. Some actions never move: pricing below the floor, approving overtime, approving a payment or a warranty claim, and sharing data outside the firm are designed to stay with a person permanently.

The measure of whether any of this is working is simple and uncomfortable: the queue of things waiting for a human should get shorter quarter after quarter. A dashboard cannot make that claim about itself.

The five layers

Your systems stay yours. Zelleo reads from them, reasons over one shared model of the business, and changes state only through declared actions, which is what makes the same record serve as the audit trail.

SurfacesDecide · Oversee · Investigate · Compose
The six products
Agent planeRole agents, each scoped to a human role’s permissions
Agentic Workforce
OntologyThe only place state changes
Dataobjects · links · properties
Logicrules · models · constraints
Actionthe typed verbs
Platform
Ingestbatch · change capture · poll · parse · confirm
Platform
Systems of recordYours, and you keep them
ERP · Tally or SAPStock sheetsTelematicsMessaging and phoneConstruction programme
A2AThe firm boundary, where other businesses’ agents talk to yours.

The single architectural decision that cannot be retrofitted is that nothing changes state except through a declared action. That one rule is what makes autonomy auditable and reversible at the same time.

What we believe

People are right to be wary of agents. Most of what has been sold as one is a thin wrapper around someone else’s model, reaching out to data and services the buyer does not control, with a prompt as its only real boundary. A prompt is not a security model.

Our position is that agents become trustworthy through architecture rather than through assurances: built for one business, grown inside infrastructure that business owns, reading only what the role they mirror may read, and acting only through declared actions that can be audited and undone.

Get that right and the fear resolves itself, because the honest answer to “what could this thing do to us?” becomes a list you can read rather than a promise you have to take.

← Infrastructure Case study

Why the next decade of AI runs on private infrastructure.

Renting capacity made sense while AI was something you tried. It makes less sense once it is how the business runs.

The shift

For a decade the sensible default was to rent. Compute was elastic, the workload was spiky, and owning hardware meant owning a problem. That logic holds for experiments.

It holds less well for a workload that runs every hour of every working day, reads the most sensitive records a company has, and gets more valuable the longer it accumulates. At that point three things change. Capacity you rent forever costs more than capacity you buy once. Data residency stops being a procurement checkbox and becomes something a client or a regulator asks you to demonstrate. And an operation that depends on a link to someone else’s building stops when that link does.

None of this is an argument against public cloud. It is an argument that the part of your business that agents operate belongs somewhere you control.

The junk data problem

The quiet reason agents disappoint is rarely the model. It is what the model was allowed to read.

An agent that reaches out to a general corpus brings back material that is stale, irrelevant or simply not yours, and then reasons confidently over it. Worse, nobody can tell afterwards which part of the answer came from your business and which came from the open internet.

When agents live on your infrastructure, the question answers itself. They read one organised record of the business and nothing else. Information carries its age, and an agent refuses to commit against a feed that has gone stale rather than quietly using it, because in a system whose whole claim is that it knew before you did, a rotting inventory feed is not a monitoring problem. It is a failure that looks exactly like success.

What a boundary actually is

A dedicated server is a foundation for control. It is not, on its own, a guarantee, and we would rather say so than sell the comfortable version.

How well a system is protected depends on its whole configuration: network restrictions, identity and permissions, which tools an agent may call, how policy is enforced, what is logged and what is monitored. Those are decisions made per deployment, written down, and checked.

Where it is required, Ground is configured so that the systems holding your data have no outbound network route at all. We call that No-Egress. It is a deployment setting agreed with you, not a property of the hardware, and it has a real cost: a model that cannot reach out also cannot be updated from outside, so the update path and the support path have to be designed deliberately rather than assumed.

On site, hybrid or hosted

  • In your building

    Servers on your premises hold the platform, the agents and your records. Nothing sensitive is sent out.

  • Hybrid

    Sensitive records and workloads stay on your own servers, with extra capacity in a location you choose for peaks.

  • Hosted

    The platform runs on dedicated infrastructure in a facility or region you choose, without a server room of your own.

It is sized to the business rather than sold as a specification: a site office that must keep working when the connection does not, a single office, a headquarters with sites reporting into it, a group large enough to train on its own data alongside live work, or a self-contained room delivered to you when there is no suitable space.

How it arrives

  1. Scoping sizes itA call sizes it from your people, your sites and the work it has to carry.
  2. Set it up for youYour records, permissions, approval rules and network boundaries are configured before it leaves us.
  3. Deliver and installIt arrives ready to plug in, with the agreed boundaries already in place.
  4. Run and supportMonitoring, updates and capacity planning against the rules documented at scoping.

Everything is confirmed at scoping before anything is built. We would rather size it conservatively and revise than deliver something wrong for the work.

What we believe

Most companies will not want to think about any of this, and they should not have to. What they want is for the system to work, for the data to stay where they said it would, and for the answer to “where is our data?” to be a room rather than a region name.

Our view is that the infrastructure question is not a technical preference but the thing that decides whether agents are safe to give real authority to. An agent that lives inside the business, on its own data, with no general route out, can be trusted with more than one that reaches across the internet to do its job. The infrastructure is not an upsell attached to the software. It is the reason the software can be trusted.

← Platform Case study

Why business software is about to lose its buttons.

Most enterprise software asks a person to carry information between screens. That job is about to move.

Fewer screens, not better ones

Business software has spent twenty years getting better at showing you things. Dashboards, modules, tabs, filters, reports. Every one of them is a place where a person has to go and look, and the work of running a company quietly became the work of operating the software that runs it.

A tile reading 94% has thrown away everything you would need in order to act on it. A person at 6:40 in the morning does not want a better chart; they want to know whether to call the second shift.

What agents change is who does the looking. The system surfaces the exception, assembles the evidence, proposes the step and asks a person only when the stakes justify it. The measure of good software stops being how much it shows you and becomes how little has to reach a screen for the right decision to get made.

One record underneath

None of that works on top of six disconnected products. It needs one record.

The platform holds projects, units, contracts, schedules, suppliers, buyers, documents, policies and permissions in one place, with the operations that are allowed on them and the security that governs them. A deal in Pulse, a task in Crew, a commitment made in Boardroom and an approval in Council are not four systems agreeing with each other. They are four views of the same object.

That has a consequence worth stating plainly: permissions are defined once and every product and agent works inside them. An agent needing a permission that no human role has is a design error, not a configuration option.

Land and Socket

Land is that record. Plots fence off a team or a workflow inside it, with their own data access, models and rules, so the sales team’s agents and the finance team’s agents are not quietly sharing a view.

Socket is the controlled connection an approved AI model uses to reach it. The important property is that a model connecting through Socket inherits the permissions of the person it is acting for, rather than the permissions of whoever installed it. An employee’s assistant sees what that employee can see. A partner’s agent sees one room and nothing else.

This is also what makes model choice real rather than rhetorical. Open-weight, licensed or trained on your own data, a model is something you can change without moving your records, your rules or your logs, because none of them live inside it.

When to train your own model

Most organisations do not need to. A good general model with excellent context beats a mediocre custom model with poor context almost every time, and the cost of finding that out the expensive way is high.

Where it does make sense, usually for larger organisations with a high-volume task and a lot of their own examples, Forge is deliberately unromantic about it. Training starts with cleaning: most teams budget for the training and discover they should have budgeted for the data. The Data Refinery de-duplicates, labels and strips personal data from the record first, then the options are priced against a budget you cap before a single hour is spent.

Training runs on capacity your agents are not using, so your working day is unaffected, and the result only goes live after it passes your own test set in Verdict. The weights stay on your infrastructure, which means leaving a vendor does not cost you the work that went into them.

Where the six products go

Pulse, Crew, Boardroom, Council and Verdict are available today, each with an interactive demo on this site that runs on sample data.

The direction for all six is the same: fewer surfaces over time, not more. As agents earn autonomy on an action, that action stops needing a screen at all, and what remains in the product is the decision queue, the record and the controls. If we do this well, the products get quieter every year.

What we believe

The companies that win the next decade will not be the ones with the most AI features. They will be the ones whose operational data is organised well enough that an agent can act on it safely.

That is an unglamorous thing to build and an easy thing to postpone, because the payoff arrives later than the work. But a model can be swapped in an afternoon, and the organised record underneath it cannot. We would rather sell the part that compounds.

Contact

Discuss your operation.

Tell us how your business runs today and where coordination takes the most time.

We read every enquiry and reply within one working day, with a view on where Zelleo fits and what a sensible first step would be.

contact@thezelleo.com

01What happens next

No commitment before a proposal
  1. We read itSomeone who understands the system reads your enquiry. It does not go into an automated queue.
  2. We replyWithin one working day: where Zelleo could help, what it would involve and any questions we have.
  3. You decideIf it fits, we scope it properly and send a costed proposal. There is no commitment before that.

We use your details only to reply to your enquiry. See our Privacy Policy.

Four camps, and where we sit.

Hardware makers sell you machines. Platforms sell you software. AI clouds rent you capacity in someone else’s building. Zelleo provides infrastructure, a business platform and business-specific agents as one system, deployed where you choose.

Hardware AI factories

Validated hardware designs with vendor AI software.

Left to you: use cases, data, business apps and governance.

Software platforms

Model serving and MLOps on hardware you buy.

Left to you: the hardware, the apps, deciding what matters.

AI clouds

In-country GPUs and tools to tune and serve models.

Left to you: your data still sits in someone else’s building, at ongoing rent.

Private AI boxes

A box that runs AI locally.

Left to you: usually one model or app, with no business-specific agents.

CapabilityAI factoryPlatformAI cloudAI boxZelleo
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You have clicked through most of this demo. Want to see it on your own numbers?

We can walk through a scenario based on your own operation, with someone from the team.

No data from this demo leaves the page. Sample data only.

Discuss your operation.

Tell us how your business runs today. We reply within one working day.

or write to contact@thezelleo.com