Build 04 — AI Service Build

Get the AI capability
your engineers can call.

A production AI service in your stack — accessed via MCP server or API. Your engineers integrate it the way they integrate any other service. The depth you couldn't hire for, delivered.

Talk to engineering Get your Free AI Strategy

Who this is for

Built for engineering teams
that need depth they can't hire.

You've tried to hire and you can't.

The role has been open 90+ days. The candidates either don't exist at the seniority you need or won't leave their current employer for what you can pay.

Your team is full-stack web, not ML systems.

They are excellent engineers. ML, computer vision, signal processing, novel-model R&D, cost-engineered inference at scale — that's a different specialism.

You need this in production, not in a notebook.

A capability that runs reliably, integrates cleanly, and costs sensibly. Not a research artefact and not a demo.

What you walk away with

A production AI capability
your engineers integrate cleanly.

A working AI service.

Live in your infrastructure or ours. Accessed via MCP server or API. Documented, observable, callable.

A clean integration.

Designed to fit how your team already works — your authentication, your monitoring, your data flows. No rip-and-replace.

A cost envelope you can plan around.

Cost-engineered from day one. ExtremeReach's vector retrieval delivered at 10× cheaper than the off-the-shelf alternative. Yours will be sized to your scale.

A partner who stays.

Maintenance keeps the service running, the model fresh, and the infrastructure tuned. You don't get handed a zip file and abandoned.

What we ship in

Capability domains delivered
in past Service Builds.

If your problem is in any of these domains, we have delivered before. If it isn't, talk to us anyway — we will tell you honestly whether we are the right team.

Computer Vision — image and video understanding, custom CV models, broadcast-scale pipelines.

Signal & Sensor AI — time-series, sensor fusion, ADC capture, edge and industrial deployment.

Document AI & NLP — PDF ingestion, layout analysis, custom NER, summarisation.

Vector Search & Retrieval — embeddings, semantic search, retrieval at scale, cost-engineered alternatives.

LLM Engineering — RAG applications, fine-tuning, generative interfaces.

Agentic Systems — autonomous agents, governance, decision-making.

ML Systems & Infrastructure — distributed inference, cost engineering, MLOps, scaling.

See all capabilities

How we build this

The Science of AI Engineering™.

Modular sprints. Stop at any boundary. Cost-engineered from day one.

Stage 01 — Hypothesis

Architecture R&D Sprint

Output: an architecture and integration plan your engineering team can sanity-check before any committed build.

Stage 02 — Experiment

Data assessment + integration prototype

Candidate models tried against your data. Integration prototype delivered into a staging environment. Real conditions, real numbers.

Stage 03 — Formulation

Production model + service layer

Production-grade model, service layer, deployment pipeline. Observability built in. Cost envelope locked. Hand-off documentation.

Stage 04 — Execution

Live service + maintenance

Service running in your stack with monitoring and a documented hand-off. Model + Application Maintenance keeps it honest.

Ready to ship the capability?

Talk to engineering.
Or take the diagnostic.

Best for technical buyers with a defined ML problem — or take the 5-minute diagnostic to see what fits.