Custom AI platforms

Agent systems built around your exceptions.

Off-the-shelf AI handles the average case. We design around the exceptions your business runs on: your taxonomy, approval chains and regulatory limits.

Schedule call Read the method

An aisle of server racks running towards a lit doorway

Service overview

A custom AI platform is the layer between a model and your business: the orchestration that decides what happens in what order, the tools that let it read and write your systems, the approval gates where a person must sign off, and the audit store that records why each decision was made.

We build that layer around one workflow first, usually the one costing you the most in manual handling, then reuse the same platform for the next. By the third workflow most of the infrastructure already exists, which is where the compounding comes from.

The model itself is a component we swap as the frontier moves. What stays is the tooling, the evals and the operational knowledge encoded in them, all of which lives in your repository.

Talk to us

Not sure what this would take?

Every build is different. Tell us the workflow and we will give you a shape, a rough number and an honest read on whether it is worth doing.

Schedule call

What we build

Document and intake automation

Extraction and classification on scans, email threads and spreadsheets that should not exist, with confidence scores routing the unclear ones to a person.

Multi-step agent workflows

Agents that read a case, gather what they need across systems, decide, and write back, stopping at the gates you define.

Retrieval over your own corpus

Contracts, tickets, drawings and warehouse tables, permission-aware and cited so every answer can be checked.

Triage and routing

Inbound work classified, prioritised and assigned, with the reasoning attached so the operator can override in one click.

Decision support

Ranked recommendations against your own historical outcomes, for pricing, credit, scheduling or risk.

Reporting and summarisation

Narrative summaries built from your live data, produced on a schedule and traceable back to source rows.

Customer-facing assistants

Support and sales assistants grounded in your own policies and catalogue, with handover to a human built in rather than bolted on.

Multilingual and voice

Arabic and English across text and voice, including transcription, summarisation and call-quality review.

Compliance and risk screening

Policy checks, KYC review and anomaly detection where every flag needs a reason a regulator will accept.

Forecasting and planning

Demand, capacity and scheduling models fed by your live operational data rather than last quarter’s export.

Engineering copilots

Internal assistants over your own codebase, runbooks and tickets, so institutional knowledge stops walking out the door.

Agent platform foundations

The shared orchestration, tool registry and eval harness the next ten workflows are built on.

Why Momentem for this

About the company →

platforms we run on this stack ourselves

We are not describing a methodology. Gridwork, Onshow and Paradiem run on the same orchestration, tool layer and eval harness we would build for you.

Designed around your exceptions

The process is mapped by watching real cases, so the 12% nobody documented is in scope from week one.

Scored before it ships

A hundred decided cases from your own history become the eval set. Nothing goes live until it clears your real baseline.

Reversible by design

Every automated write has a rollback path and a kill switch your team controls, or it gets an approval gate instead.

Model-agnostic

Models are swapped as the frontier moves. The tooling, evals and operational knowledge stay yours.

Costed per run

Token and tool spend traced per case, with budgets and caching, so unit economics are visible from week one.

Handed over, not hosted

Prompts, tools and evals versioned in your repository, with paired on-call until your engineers own it.

Reusable by the second workflow

The orchestration, tool layer and eval harness carry over, which is why build two costs a fraction of build one.

How this compares

Momentem

Large consultancy

Off-the-shelf tool

Exception handling

Mapped from real cases in week one

Often deferred to a later phase

Whatever the vendor anticipated

Quality evidence

Eval set from your own history, yours to rerun

Varies by team; often a demo

Vendor benchmarks

Where it runs

Your cloud, on-premise or air-gapped

Usually their preferred cloud

Their cloud

Who owns the code

You, from the first commit

Negotiated; sometimes shared

Nothing to own

Cost after go-live

Optional support you can end

Change requests

Per-seat licence, indefinitely

Team on the ground

Named senior engineers who have shipped this before

Pyramid; seniors on some days

No team

Based on publicly available information and our own experience of comparable engagements. Generalisations rather than claims about any specific provider. There are good exceptions in every column, and the point is where each model is structurally strong.

The stack we work in.

Models

Frontier APIs or open weights, picked per task and swapped as the frontier moves.

Serving

Inference inside your own cloud account or on your own hardware, sized to the load it will carry.

Engineering

Your conventions and your repository, reviewable by your engineers from the very first commit.

Business systems

Where the output has to land for any of it to count, written back reversibly and with an audit record.

How the build runs.

Week 0

Scope

Sessions with the people doing the work. We leave with a written target and a data map.

Week 1 to 2

Prove

A thin slice against your real records, scored on a test set drawn from your own history.

Week 3 to 12

Deploy

Integrations, approval gates, audit logging. Live on one team with a rollback switch.

Ongoing

Hand over

Runbooks, paired on-call and a decision log, until your team changes it without us.

An engineer silhouetted in front of a bank of four monitors

Bring us the workflow that costs you most.

Forty-five minutes with the people who would build it. No pitch, and a straight answer on whether it is worth building.

Schedule call

Questions

All questions

Whichever suits the task and is available in your environment: hosted frontier models through your own cloud account, or self-hosted open weights. We benchmark two or three against your eval set and pick on evidence, not preference.

A scored eval set drawn from your own decided cases, with exceptions tagged separately so you can see performance on the hard ones. It runs on every change and is handed to your team to own.

That is the normal case, and part of what the Audit measures. Sometimes the answer is that cleaning one field is worth more than any model, and we will say so.

Yes. We integrate rather than replace, and expose your systems as governed tools so future agents plug in instead of being rebuilt.

Depends on the exception rate. Most workflows we see land between 60% and 90% fully automated, with the rest escalated to a person with full context.