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Six capabilities · one team

Everything between the model and the user.

Anyone can call a model. What breaks in production is the layer around it — the provider that rate-limits at 4am, the schema that drifts, the permission nobody scoped. That layer is what we build.

Artificial Intelligence
01 / 06

Artificial Intelligence

Agents that finish work rather than demo it — reading the ticket, checking the system, drafting the reply, escalating where being wrong is expensive.

Delivered as a scoped system with an accuracy bar agreed in writing, a runbook your team can follow, and full source in your own repository from the first week.

SCOPE

Retrieval over your own documents

SCOPE

Evaluation suites and accuracy thresholds

SCOPE

Fine-tuning and open-weight deployment

SCOPE

Human-in-the-loop escalation

Cloud
02 / 06

Cloud

Infrastructure that survives a bad Tuesday. Autoscaling, multi-region failover and cost controls that stop a runaway job becoming a five-figure invoice.

Delivered as a scoped system with an accuracy bar agreed in writing, a runbook your team can follow, and full source in your own repository from the first week.

SCOPE

AWS · GCP · Azure · bare metal

SCOPE

Kubernetes and serverless GPU

SCOPE

Infrastructure as code, reviewed like code

SCOPE

Spend alerting and circuit breakers

Cybersecurity
03 / 06

Cybersecurity

Prompt injection, exfiltration through a model, over-permissioned agents — the attack surface AI adds is new, and most teams have not mapped it yet.

Delivered as a scoped system with an accuracy bar agreed in writing, a runbook your team can follow, and full source in your own repository from the first week.

SCOPE

AI-specific threat modelling

SCOPE

Secrets, key rotation and scoped access

SCOPE

Audit trails an auditor can read

SCOPE

PDPA and GDPR posture

Data
04 / 06

Data

A retrieval layer is only as honest as the index behind it. We build the pipelines, the versioning and the freshness checks that stop a model confidently quoting last quarter.

Delivered as a scoped system with an accuracy bar agreed in writing, a runbook your team can follow, and full source in your own repository from the first week.

SCOPE

Pipelines, warehousing and lineage

SCOPE

Vector stores and hybrid retrieval

SCOPE

Quality monitoring and drift alerts

SCOPE

Real-time and batch, same contract

Software
05 / 06

Software

The application around the model. Web, mobile and internal tools built by the same team that built the inference layer, so the seams are ours to answer for.

Delivered as a scoped system with an accuracy bar agreed in writing, a runbook your team can follow, and full source in your own repository from the first week.

SCOPE

Full-stack product engineering

SCOPE

API design and versioning

SCOPE

Design systems and front-end

SCOPE

Handover with a tested runbook

Products
06 / 06

Products

Our own tools, available to you directly — a routing gateway, an evaluation harness and a monitoring console we built because we needed them ourselves.

Delivered as a scoped system with an accuracy bar agreed in writing, a runbook your team can follow, and full source in your own repository from the first week.

SCOPE

Unified model gateway

SCOPE

Evaluation and regression harness

SCOPE

Cost attribution console

SCOPE

Self-host or managed

Effort and price

Indicative bands.

Ranges from engagements we have actually closed, published so a finance conversation can happen before an engineering one. The binding figure arrives at the end of the discovery week.

Typical buildIndicative bandMonthly runMain risk
Artificial Intelligence5–8 weeks USD 28k–60kUSD 3k–8kNo agreed definition of a correct answer
Cloud2–6 weeks USD 14k–40kUSD 1k–4kMigration scope creeping past the brief
Cybersecurity3–5 weeks USD 12k–32kIncluded in runFindings nobody is resourced to fix
Data3–8 weeks USD 15k–45kUSD 1k–4kTurning into a full data programme
Software6–12 weeks USD 30k–90kUSD 2k–6kRequirements arriving after the build starts
ProductsSame week No build feeUSD 500–5kPaying for a console nobody opens

Bands assume one system, not a per-seat licence, and they exclude the model spend itself — that goes on your own provider account, so the number you see there is the number they charged. Platform pricing is separate and starts where the routing saves more than it costs.

Questions

Asked on almost every first call.

Self-hosted or managed — which should we pick?

Self-host if a payload leaving your network is a compliance problem, or if you already run Kubernetes and would rather own the uptime. Managed if you want the routing table maintained for you as provider prices move. The code is identical; only who runs it changes.

What happens when a provider has an outage?

The request moves to the next entry in your fallback list mid-flight, and the caller sees a normal response with a different resolved model in the trace. You get an alert; your users get nothing unusual.

If every provider on a route is down, the gateway returns a structured error rather than a timeout, so your application can degrade on purpose instead of hanging.

Can we bring our own provider keys?

Yes, and most teams do. Your keys stay in your secret manager and are used at request time. You keep the direct billing relationship and we never hold a credential that can spend your money.

How do you decide which model wins a route?

Three signals, weighted by you: measured accuracy on your own evaluation set, p95 latency against the budget for that route, and cost per completed task including retries. Any model can be pinned by hand, and pinning is logged so nobody quietly overrides the data.

Do you take on work outside the platform?

Yes — cloud, security, data and software engagements that stand on their own. We take roughly six to eight a year and turn down anything where we would be the third vendor in a room with no technical owner.