AI integration your
engineers can just 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.
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. Your engineers own the call site, and nothing else.
The alternative is hiring for it. That is a fair option when the capability is permanent, central, and singular — but one senior ML engineer covers one specialism, and most products need several. Ferrous Labs applies The Science of AI Engineering™, which produces the architecture and integration plan first, so your engineering team can sanity-check the approach before any committed build. See how we delivered vector retrieval 10× cheaper for ExtremeReach →
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.
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.
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.
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 Architecture R&D Sprint, 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 →
Go deeper on this.
AI Agent Development UK: What It Involves and What It Costs
Read the guideProductise Consulting Services: The Repeatability Map
Read the guideTalk to a co-founder.
Or take the diagnostic.
Best for technical buyers with a defined ML problem — or take the 5-minute diagnostic to see what fits.