Specialised · under AI Development

AI Integration Services — Add AI to Software You Already Run

Most businesses do not need an AI product. They need two or three places in software they already run where a model removes real work — support triage, document extraction, search over their own documents. We scope those, build them behind an evaluation harness, and leave the rest alone.

Scope your project See AI Development →

What we build

RAG over your own documents, with citations back to the source
Support deflection with human handoff on anything uncertain
Document and invoice extraction into structured fields
Semantic search across internal knowledge
Drafting and summarisation inside existing workflows
PII redaction before anything reaches a model
Evaluation harness so quality is measured, not asserted
Guardrails and refusal behaviour defined up front
Cost and latency budgets per feature
Model-agnostic integration so you are not locked to one vendor

What you receive

A shortlist of where AI actually pays in your product, and where it does not
The integration itself, inside your existing application
Evaluation harness with a test set drawn from your real data
Guardrail and fallback behaviour, documented
Cost-per-request modelling before you commit to scale
Source code and runbooks

Why custom over off-the-shelf

The scoping is most of the value

The common failure is a chatbot bolted onto a homepage that answers nothing well. The useful version is narrow — one workflow, measurable deflection, a clean handoff to a person. We would rather tell you two of your five ideas are not worth building.

Without evaluations you cannot tell if it works

A demo always looks good. An evaluation harness built from your own data tells you the answer rate, the error modes and what a change actually did. That is the difference between shipping AI and showing it.

Your data should not leave without you deciding

PII redaction happens before anything reaches a model, retention is configured deliberately, and the integration stays model-agnostic so a vendor's pricing or policy change is a config edit rather than a rewrite.

Pricing and timeline

What it costs

Quoted in 48 hours

Itemised and fixed against a written scope, after one 30-minute call

Timeline

6 – 12 weeks

From kickoff to production

FAQ

Can you add AI to our existing website or app?

Yes, and that is normally the right shape — a narrow integration inside the product you already run, rather than a separate AI product. The first step is deciding which one or two workflows justify it.

Which parts of our product are worth adding AI to?

Usually the ones with high volume, tolerant accuracy requirements and an obvious human fallback: support triage, document extraction, internal search, drafting. Anything where a wrong answer is expensive and unrecoverable is a poor first candidate, and we will say so.

How do you stop it from making things up?

Grounding and measurement. Answers are retrieved from your own content and cite the source, the system is built to say it does not know, and an evaluation harness on your real data tracks the error rate over time. Uncertain cases hand off to a person by design.

Which model do you use?

Whichever fits the accuracy, latency and cost budget for that workflow — and the integration stays model-agnostic so it can be swapped. We model cost per request before you commit to scale, because that is what usually decides whether a feature survives contact with real traffic.

Do you have an AI case study?

Not a published one yet. The case studies on this site are web, mobile, SaaS, HRMS and field-sales engagements. We would rather point you at those and talk through AI work in detail on a call than show you a case study we cannot stand behind.

Ready to scope this?

Fixed-cost proposal and delivery plan within 48 hours of a 30-minute discovery call.

Get a proposal