AI Integration

AI added to the software and systems you already run — not another tool on the side.

We put AI where your work actually happens: copilots inside your own product, retrieval-augmented answers over your documents and data, OCR and document pipelines, and speech-to-text where conversations carry the knowledge. LLM-agnostic by design, so the model serves the workflow — never the other way round.

How it works: your documents and data feed a private retrieval index; the AI layer powers copilots, APIs and pipelines inside your own tools — all of it inside your privacy boundary.

• Your existing systems gain AI features without a migration
• Answers and drafts grounded in your own data, not the open web
• One integration architecture instead of a sprawl of AI tools

What this usually includes

• Retrieval (RAG) over documents, wikis, and structured data
• In-product copilots and AI feature development
• OCR and document-understanding pipelines
• Speech-to-text and meeting-capture integrations

Best fit for

• Product teams that want AI features shipped properly
• Operations sitting on document mountains and buried knowledge
• Firms that want AI on their own data with privacy intact

A delivery approach grounded in operations, not hype

1. Audit the systems, data, and highest-value insertion points
2. Prototype on your real data and measure quality honestly
3. Harden for production: monitoring, fallbacks, cost control

What we can build for you

• Invoice line items lifted from scanned supplier PDFs and posted straight into your ERP
• A retrieval assistant for lawyers that answers from the matter file and cites the exact clause it relied on
• Ship a copilot inside your own SaaS product, one that drafts and summarises while customer data stays in your stack
• Clinician voice notes that land in the patient record as structured fields, not a transcript somebody has to retype
• Freight paperwork that files itself: CMR notes, customs forms and proof-of-delivery scans matched to the right shipment
• On the shop floor, machine-fault answers pulled from decades of maintenance manuals and handwritten engineer notes
• Caseworkers at public bodies ask which regulation applies; the assistant digs it out of statutes, circulars and internal guidance
• Supplier spec sheets in, consistent product copy out, across every storefront language
• Meeting capture that transcribes every sales call and logs the summary, objections and agreed next steps in your CRM
• Helpdesk replies drafted from your docs, past tickets and release notes, with sources attached so an agent can check before hitting send

Frequently asked questions

How much of our team's time will this take, and when does something actually go live?

We build against the APIs and databases you already run, so there's no re-platforming. The first release is deliberately narrow: one workflow, one document set, live in weeks rather than a multi-quarter programme. Then we widen it. From you we need a decision-maker and someone who knows the systems, not a dedicated squad.

Where does our data go, and will it end up training someone else's model?

Pipelines and retrieval indexes run on EU-hosted infrastructure, or inside your own cloud if you'd rather, always under a data processing agreement. We're not wedded to any one vendor, so we pick models whose API terms rule out training on your data, and where the material is too sensitive even for that, we run open-weight models privately. Nothing you hand us trains anything.

Our staff already use ChatGPT. Why is this different?

A public chatbot has never read your contracts, your past tickets or your part numbers, and it lives in another tab that people paste sensitive data into. We put answers inside the tools you already work in, grounded in your own documents, with citations you can check. There's no per-seat licence either, so adoption doesn't run a meter.

Be honest: when is AI integration the wrong answer?

When the output must be exactly right every time — payroll maths, regulatory calculations — plain code wins, and we'll say so up front. Retrieval is also only as good as your sources: if the wiki holds three contradictory versions of a policy, the assistant will surface the contradiction, not resolve it. When that happens, we fold a short content clean-up into the build.

Explore the connected parts of the offer

• Private & Self-Hosted AI — The full AI stack — models, retrieval, transcription — running on infrastructure you control.
• AI Automations — Use AI where it improves throughput, quality, and decision support.
• Software Development — Any software, any tool, any commercial format — built to own, white-labelled under your brand, or run as your product.

Discuss this service: [email protected]