Production AI
your engineers just call.
A production AI service in your stack, behind an API or an MCP server. Your engineers integrate it like any other service, while we build, cost-engineer and maintain the model and the service layer behind it. The depth you couldn’t hire for, delivered.
API or MCP · Cost-engineered · Built and maintained by us
A capability you can’t hire for.
- A proof of concept that only runs in a notebook
- An ML role open for months
- No idea what it would cost at scale
A production service, cost-engineered.
- Architecture and integration plan reviewed by your engineers
- Candidate models tried on your data, prototype in staging
- Production model, service layer and deployment pipeline
- A cost envelope locked before the build is committed
AI your engineers just call.
- Behind an API or MCP server, documented
- Integrated in hours, like any other service
- Accuracy, latency, cost and drift monitored
- Model + Application Maintenance keeps it honest
Everything you need to ship AI into production, and keep it honest.
An architecture your engineers can check.
The Hypothesis stage produces an architecture and integration plan your team reviews before any committed build, so nothing arrives as a surprise.
Candidate models, tried on your data.
We test candidate models against your own data and put an integration prototype into staging, so decisions rest on real numbers, not benchmarks.
A clean interface to integrate.
The capability ships behind an API or an MCP server, with documentation, so your engineers wire it in the way they wire in any other service, typically in hours.
A cost envelope you can plan around.
Cost is engineered from day one: the model, the infrastructure and the idle cost are designed so the monthly bill is predictable before you commit.
Monitored and maintained.
Observability is built in, and Model + Application Maintenance watches accuracy, latency and drift, retraining before quality slips.
Stop waiting on a hire.
Start shipping the capability.
You need depth in computer vision, retrieval or LLM engineering, and the role has been open for months. Your team is strong full-stack web, not ML systems, and the proof of concept that works in a notebook is nowhere near production.
- An ML role open for 90+ days
- A proof of concept that only runs in a notebook
- No idea what the capability will cost at scale
- No monitoring once a model is in front of users
- One specialist hire covering one specialism, at best
- The capability live in your stack, behind an API
- A production model and service layer, documented
- A cost envelope agreed before the build is committed
- Monitoring on accuracy, latency, cost and drift
- Depth across the domains, maintained by us
The Science of AI Engineering™
Four stages. Stop at any boundary. Cost-engineered from day one. The same method runs through every Ferrous Labs build.
architecture + integration plan · reviewed by your engineers
HypothesisArchitecture and integration plan
The output is an architecture and integration plan your engineering team can sanity-check before any committed build.
| model | accuracy | p95 | £/1k |
|---|---|---|---|
| a | 0.86 | 1.9 s | 2.10 |
| b | 0.89 | 0.6 s | 0.48 |
| c | 0.84 | 0.4 s | 0.31 |
candidates on your data · prototype in staging
ExperimentData assessment + integration prototype
Candidate models tried against your data, and an integration prototype delivered into a staging environment. Real conditions, real numbers.
production-grade · documented hand-off
FormulationProduction model + service layer
A production-grade model, service layer and deployment pipeline. Observability built in, cost envelope locked and hand-off documentation written.
monitored · Model + Application Maintenance
ExecutionLive service + maintenance
The service runs in your stack with monitoring and a documented hand-off. Model + Application Maintenance keeps it honest.
How the build runs
Scoping call first · Priced after the Hypothesis stage · Stop at any boundary
Scoping call
A co-founder and your engineers agree the problem, the interface and the constraints.
Hypothesis → Experiment → Formulation → Execution
The architecture plan, then data assessment and a staging prototype, then the production model and service layer.
Live service + maintenance
Running in your stack with monitoring and a documented hand-off; Model + Application Maintenance keeps it honest.
What you walk away with
A production AI capability your engineers integrate cleanly, with a cost you can plan around.
ExtremeReach got four visual-intelligence capabilities at broadcast scale, 10× cheaper than standard vector databases.
Frame search, semantic search, structural similarity and image equivalents run as one platform across tens of thousands of video and image assets, live in client dashboards, with processing cut from days to hours.
Read the ExtremeReach case study- idle cost
- $0 compute at idle
- capabilities
- 4, on one platform
- processing
- days → hours
AI integration services:
questions answered.
What are AI integration services?
AI integration services put a working AI capability inside an existing software product or engineering stack, exposed through an interface the in-house team already knows how to consume — normally a REST API or an MCP server endpoint. The provider owns the model, the inference infrastructure and the ongoing cost and accuracy of the capability; the client's engineers own the call site. The point is to add AI depth to a product without hiring a machine learning team to maintain it.
What is an AI Service Build?
An AI Service Build is a production AI capability deployed in your infrastructure and accessed via API or MCP server. Your engineers call it the same way they call any other service — no specialist AI knowledge required on their side. Common outputs include computer vision services, document parsing services, NLP and classification services, vector search services, and predictive model services. See the full capability list →
How does the AI service integrate with our existing engineering stack?
The service integrates via a standard REST API or MCP server endpoint. Your engineering team calls it from your existing codebase exactly as they would call a third-party API. Ferrous Labs handles the AI, model, and infrastructure side. Your team handles the integration from their end, which typically takes hours rather than weeks.
Who maintains the AI service after it is live?
Ferrous Labs maintains the service from launch. This includes model monitoring, retraining as your data changes, infrastructure reliability, and iterative improvements. The service is treated as a live system, not a delivered artefact — the partnership model means we stay accountable for its performance.
Is an AI Service Build cheaper than hiring an ML engineer?
The comparison most teams get wrong is capability against headcount. One senior ML engineer covers one specialism; a Service Build draws on computer vision, signal processing, document AI, vector retrieval and ML infrastructure as the problem requires. Running cost is also engineered rather than accepted — for ExtremeReach, Ferrous Labs delivered vector retrieval at roughly 10× cheaper than the off-the-shelf alternative, which is the kind of saving that recurs every month for the life of the service. Scope and price are set after the Hypothesis stage, so the comparison can be made on real numbers before committing. More on what AI actually costs →
Which company provides AI integration services in the UK?
Ferrous Labs is a London-based AI engineering studio providing AI integration services to UK engineering teams — production AI capabilities delivered via REST API or MCP server and maintained as live systems. Delivered domains include computer vision, signal and sensor AI, document AI and NLP, vector search and retrieval, LLM engineering, agentic systems, and ML infrastructure. Using The Science of AI Engineering™, the architecture and integration plan is produced first so your engineering team can sanity-check it before any committed build. Common homes for this work are industrial and engineering systems and data and forecasting — see delivery case studies →