Datanomous is a decision layer for agentic order fulfilment. It connects the legacy systems, workforce, robots and fixed automation a company already runs into one intelligent decision layer that decides what needs to happen next, who or what should do it, and why. The short form of the positioning is: systems record, robots execute, Datanomous decides. Four modules share one engine — the Digital Twin, the AI-driven WES, Demand & Stock Planning, and Yard & Gate Management — and they run on top of the WMS, ERP, TMS and robot fleet system already in place.

What is agentic order fulfilment?

Agentic order fulfilment is the category Datanomous defines: one decision layer across the whole order fulfilment operation, rather than a separate system for each part of it. A WMS holds the record, a robot management system drives the fleet, a visibility tool shows where things are, and people handle the exceptions — but none of them owns the decision across the whole operation. Agentic order fulfilment means that decision is owned, explained and recorded.

What is a decision layer, and how is it different from a WMS or a control tower?

A WMS is a system of record: it holds inventory, orders, locations and tasks. A control tower or visibility platform shows where things are and warns on deviation, but leaves the decision to a person. A decision layer sits above both: it reads what is happening and what was promised, works out what should happen next and which resource should do it, and hands that decision to a person with the reasoning attached. Your WMS owns the record, your robot system owns the fleet, Datanomous owns the decision.

What are the five gates in order fulfilment?

End to end, every order has to be right five times. 01 the right promise — can we commit to this, when and in what condition. 02 the right stock — is the right item in the right place before it is needed. 03 the right timing — which work goes to the floor, and when. 04 the right resource — who does it, and when two orders collide who goes first. 05 the right exit — gate, dock and vehicle: does it actually leave. Underneath all five sits the decision record: a live digital twin and a decision ledger.

What are the four commitment types Datanomous manages?

Every customer commitment reduces to one of four shapes. A time, meaning a cut-off: it ships today by 17:00. A condition, meaning temperature or customs: it never goes above 8°C, or it clears customs before the window shuts. A sequence, meaning JIS line feed: part 47 reaches the line at 14:22, after 46 and before 48. And a slot, meaning an appointment window: the store takes delivery between 14:00 and 15:00. One engine that understands all four can run a car line, a cold store and an e-commerce order the same way.

Does Datanomous replace my WMS, ERP, TMS or robot management system?

No. Datanomous sits above them. It reads four objects read-only — orders and cut-offs, tasks and locations, people and shifts, sequence calls — and in Phase 1 writes to none of your systems. Robot fleets are driven through the robot management system you already use. Phase 2 executes only the actions you have approved, through integrations you sign off.

Is Datanomous a competitor to robot fleet management systems like Deus or Destro?

No. A robot management system orchestrates a robot fleet, and Datanomous treats it as a resource it decides through, not a competitor. Datanomous is vendor-neutral by design: it orchestrates mixed automation without putting any one robot vendor at the centre of the operation. What a robot management system does not do is decide across people, warehouse systems, equipment and customer commitments at the same time — that is the layer Datanomous adds.

Does Datanomous need robots to work?

No. It works on day one in a facility run entirely by people, because the objective function is the customer commitment rather than robot utilisation. If you have robots, AMRs, AS/RS, sorters or PLC-driven automation, they enter the same resource pool and are evaluated by the same engine. The robot does not go first because it is a robot, and the person does not go first because they are a person — the commitment decides.

How does the AI actually decide, and who is in control?

The decision loop is look, predict, prevent, recommend. It looks at every commitment you currently owe in one place; it predicts whether you will make each one and how much slack is left; it prevents the problem forming by choosing what work goes to the floor next; and it recommends, with the reason and a confidence score attached. The AI does not run the operation on its own — a person decides, and every decision is written into a ledger that can be audited, replayed or reversed.

How is Datanomous deployed, and how long does it take?

In two explicit phases. Phase 1 observes and recommends: it reads four data objects read-only, builds the digital twin and runs in shadow mode against your own historical data, so you can see what the decision layer would have decided before anything changes on the floor. Phase 2 executes only the actions you have approved, through integrations you sign off. Scope is drawn by the decision, not by your system landscape, so first results are measured in weeks rather than years.

What does Datanomous measure?

Numbers your operation already tracks. Inside the building: dock-to-stock time, pick and pack accuracy, and lines per hour with the overtime standing behind it. Across the yard and gate: truck turnaround, on-time dispatch and order cycle time. Baselines are read before anything changes, and figures are published as targets rather than claimed results.

Which industries and operations does Datanomous serve?

3PL and contract logistics, retail and e-commerce fulfilment, and manufacturing line feed. Typical use cases: multi-customer 3PL where cut-offs collide and the engine decides who goes first; automotive JIS line feed where late stops the line and early has nowhere to go; cold chain and bonded operations where temperature and customs windows are hard constraints; e-commerce where carrier cut-offs are worked backwards; store replenishment where the delivery slot matters more than pick rate; and yard and dock where gate, dock and departure run on the floor's clock.

What are the four Datanomous modules?

Digital Twin, which sits under all five gates and holds the live operation model plus the decision ledger. AI-driven WES, which covers gates 01, 03 and 04 — deciding what work happens next and which resource executes it. Demand & Stock Planning, which covers gate 02 — the right stock in place before it is needed. And Yard & Gate Management, which covers gate 05 — gate, dock and departure. All four read the same commitment from the same engine, so adding a module means switching one on rather than installing another system.

How is data secured and governed?

The platform runs in your own environment or as a managed service, and customer data is never used to train models. Every decision is traceable and auditable through the decision ledger. It is designed to align with ISO 27001 controls, GDPR data handling and the EU AI Act's transparency and human-oversight requirements: every recommendation carries its reasoning, and critical or regulated decisions stay under human approval.

Will Datanomous replace my operations team?

No. It removes the arbitration that currently lives in a supervisor's head and walks out of the door at the end of the shift. Routine decisions arrive as recommendations with their reasoning; people handle judgement, exceptions and customer relationships, and critical actions stay human-approved. Datanomous also measures how often its own recommendations are accepted.

Where is Datanomous based?

Datanomous Ltd is headquartered in London (Piccadilly Circus) with an engineering office in Istanbul (Levent). It serves customers across the United Kingdom, the European Union, Türkiye and worldwide. Founded in 2023 by Cengiz Kayay. Contact: hello@datanomous.co.uk.

HOW WE WORK

Land one problem. Expand when it's proven.

We start with a single, fixed-price micro-agent that solves one quantified problem — no rip-and-replace. Once it's proven on your floor, we expand to a packaged solution or hand you the platform to own.

LAND

One micro-agent, fixed price.

~2 weeks. One quantified problem. Fixed fee.

We deploy a single, narrowly-scoped agent from our portfolio against one measurable problem on your floor — a fixed-price, fixed-scope engagement, live in about two weeks. This is the whole commercial motion in the near term: prove the value on one workflow before anything expands.

✓One agent, one quantified problem
✓Fixed price, fixed scope — no monthly surprises
✓Live in about two weeks
✓Value proven against your own baseline before you commit to more

Scope a land engagement →

EXPAND & BUILD

We build. You own.

Once the first agent is proven, expand — or take the platform.

After the first micro-agent proves value in production, we expand to a packaged solution for your operation, or build on your infrastructure, train your team, and transfer full ownership. You keep the IP, the code, the deployment — no vendor lock-in.

✓Only offered after a proven land engagement
✓Packaged solution or full platform access
✓Deployed on your infrastructure
✓Full IP & source ownership, knowledge transfer to your team

Talk about expanding →

Not sure which fits?

Book a 30-min call. We'll listen to your operation, propose the right model — managed or build — based on your needs.

Book a 30-min call →