AI integration works best when it targets a specific, well-understood problem: a support queue that eats every morning, documents someone retypes by hand, a report that takes half a day to assemble, or a product feature your competitors cannot easily copy. We work with two kinds of clients — established businesses that want repetitive work automated, and founders building products where AI is the core of the offer. In both cases the engineering is the same: connect a capable model to your data and your systems, and put sensible guardrails around it.

Much of our recent product work has AI at the centre. We built Refine AI, a multi-tenant compliance platform that ingests company policies, rewrites them into SOC 2 and ISO 27001-aligned versions, and auto-drafts security questionnaire answers using RAG-powered vector search. For Tretech, an EdTech platform, we combined OCR document extraction with Google Gemini to turn uploaded study material into an AI tutor, generated exams, and learning games. SurvaIQ assembles property valuation reports for UK surveyors from live government data and an AI report service, and our AI accessibility platform scans WordPress and Shopify sites and uses GPT-4.1 to generate code-level fixes for WCAG violations.

The outcome to expect is specific: a workflow that used to consume hours runs largely on its own, with a person reviewing the edge cases instead of doing all the work. We are deliberate about the unglamorous parts — grounding answers in your actual data so the system does not invent facts, building review queues for low-confidence output, and defining exactly when the AI must hand off to a human. That work is what separates automation your team trusts from a demo that gets quietly abandoned.

What our AI Integration & Automation services include

Build intelligent AI-powered applications and automation systems that improve business efficiency and decision-making.

AI-powered web & mobile applications
AI chatbots and assistants
Machine learning integrations

Workflow automation
AI content and data processing systems
Business intelligence dashboards

Grounded in your data

Every AI system we ship retrieves answers from your documents, database, or product data rather than the model's general knowledge. That is the difference between an assistant that quotes your actual refund policy and one that confidently invents it — and it is non-negotiable in our builds.

Human-in-the-loop by design

We define what the AI must never handle alone — billing disputes, legal questions, low-confidence extractions — and build fast, visible escalation to a person. Early on, the system drafts and a human approves, so you gather accuracy data without risking a customer-facing failure.

Integration over research

Most business AI is integration engineering, not model training. We build on foundation models like OpenAI GPT and Google Gemini through their APIs, and spend the effort connecting them cleanly to your CRM, database, and internal tools — which is where these projects succeed or fail.

Pilot before you commit

Not every process deserves custom AI, and we will say so when an off-the-shelf tool covers your need. We usually recommend starting with a small pilot — one process, one success metric, a decision date — so you prove value before committing serious budget.

Discovery & Planning

We understand your requirements, define the scope, and map out a clear roadmap for development and delivery.

Design & Development

Our team designs and builds your solution using modern technologies with clean architecture and best practices.

Testing & Deployment

We rigorously test, optimize performance, and deploy to production with cloud-ready infrastructure and ongoing support.

FAQ's

Common questions about AI Integration & Automation

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What does an AI integration project cost?

It depends on three things: the scope of the workflow being automated, how many systems the AI needs to connect to, and how much human review the output requires early on. A narrow pilot with one process and one integration costs a fraction of a full product build. Send us a description of the workflow and we scope and quote within 2-3 business days.

How long does an AI project take?

A focused pilot typically runs 4-8 weeks from kickoff to a working system with a human in the loop. A production AI product — multi-tenant, billed, customer-facing — usually takes 3-6 months depending on scope. We recommend starting with the pilot: it answers whether the approach works on your data before you commit to the larger build.

Do you train custom machine learning models?

Rarely, because most business problems do not need it. We build on foundation models such as OpenAI GPT and Google Gemini through their APIs, combined with retrieval (RAG) over your own data. You get frontier-model quality without training anything yourself, and the budget goes into integration and guardrails — which is where the value is.

How do you stop the AI from making things up?

By grounding every answer in your real data through retrieval, attaching confidence scores to generated output, routing low-confidence results to a human review queue, and scoping the system narrowly. We used exactly this pattern in Refine AI, where auto-drafted compliance answers carry confidence scores and human-review flags.

Can you add AI features to our existing product?

Yes — most of the AI work we do is added to existing systems rather than built from scratch. We work inside your current codebase and stack, add the AI layer behind clean APIs, and keep every change reviewable by your team.

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AI Integration & Automation

What our AI Integration & Automation services include