Service
Custom AI Development
When off-the-shelf stops short, we build the system your business actually needs.
Custom AI development is for problems no SaaS product solves: extracting decisions from your specific documents, answering questions from your knowledge base, scoring your applications with your criteria. We design, build and deploy these systems — NLP pipelines, RAG chatbots, ML scoring models — and you own the result outright.
Built for the problems SaaS can’t reach
Every mid-sized business now has a drawer full of AI subscriptions — and a set of problems none of them touch, because the value lives in the company’s own documents, data and decision rules. A generic contract tool doesn’t know your firm’s risk positions. A generic chatbot doesn’t know your product documentation. That gap between what off-the-shelf does and what your operation needs is exactly where custom AI development pays.
CortexIntel designs and ships these systems for clients in the UK and India: 50+ production deployments to date, delivered by the same senior team from first workshop to handover.
What we build
NLP document processing. Pipelines that read your contracts, claims, applications or reports and produce structured output your systems can act on. For a London law firm we built a contract-review system that flags non-standard clauses against the firm’s own playbook — cutting review time 85% while keeping every judgement call with a solicitor.
Knowledge chatbots and RAG systems. Retrieval-augmented generation over your documentation, policies and history — with citations on every answer and a refusal path when the source material doesn’t support a response. Deployed for customer-facing support and internal expert-knowledge search.
Machine-learning scoring and decision support. Where the task is ranking or prediction rather than language — lead scoring, application triage, demand forecasting — we build classical ML where it wins and LLM-based reasoning where it doesn’t, and we show you the evaluation numbers behind the choice. Our education client’s admissions system tripled enquiry-to-enrolment conversion on this foundation.
Integration layer included. A model without plumbing is a demo. Every build includes the connectors, queues, review interfaces and monitoring that make it a system your operations team can live with.
How we work
The engagement follows our standard 90-day arc — discovery, strategy, build, scale — with two habits that matter more in custom work than anywhere else. We define the evaluation set in week one: a few hundred real examples, labelled with your experts, that every model iteration is scored against. And we ship thin slices weekly, so accuracy curves are visible to you from the second sprint, not asserted at the end.
Security is not a phase; it is the default. Data-residency requirements decide model hosting. Access follows least privilege. Prompts and outputs are logged for audit. This is the Security First posture that comes from ten years in cybersecurity before we wrote our first model pipeline.
Ownership, without the ransom
You receive the source code, prompts, evaluation harness, infrastructure definitions and documentation. Run it yourself, extend it in-house, or keep us on a support retainer — your call. We win repeat work by being good, not by holding systems hostage.
If you have a problem that smells custom — a document type nobody’s tool reads properly, a decision your team makes a hundred times a day — book a strategy call and bring three real examples. We’ll tell you in 30 minutes whether it’s a build, a buy, or not worth doing.
Custom AI Development: common questions
When does custom development beat an off-the-shelf AI tool?
When the value depends on your data and your rules: contract review against your risk playbook, admissions scoring with your criteria, a chatbot answering from your documentation. Generic tools plateau at generic accuracy; custom systems are tuned and measured on your cases. If a £50-a-month SaaS genuinely covers the need, we will tell you so in discovery.
Which models and platforms do you build on?
We are vendor-neutral: commercial frontier models where quality justifies cost, open-weight models where data residency or unit economics demand them, and classical ML where a large language model is overkill. Every build abstracts the model layer so you can switch providers without a rewrite.
How do you stop a chatbot inventing answers?
Retrieval-augmented generation with citations: the system answers only from your indexed documents, links the source passage for every claim, and declines when retrieval confidence is low. We measure hallucination rate against a held-out test set before go-live and monitor it in production.