AI Integration
We add AI where it saves real time — summarising, drafting, extracting and searching — by integrating the OpenAI, Claude and Gemini APIs into your web and mobile apps. Scoped to a clear job, checked before it reaches users, and with costs you can predict.
- OpenAI
- Claude
- Google Gemini
- Node.js
- TypeScript
- Zod
OpenAI
Claude
Google Gemini
Node.js
TypeScript
Zodconst Summary = z.object({ headline: z.string(), actions: z.array(z.string()).max(5),});export async function summarise(notes: string) { const reply = await ai.generate({ prompt: notes, schema: Summary }); return Summary.parse(reply);}The problem
Most businesses we talk to have tried a chatbot. Few have something their team uses every day.
Demos that don't survive real data
It looked great in a trial, then gave confident wrong answers on actual customer records.
No clear job to do
AI was added because competitors had it, not because a task needed it.
Unpredictable costs
Usage-based billing with no limits, caching or monitoring in place.
Data concerns
Nobody is sure what's being sent to the model or how it's stored.
We start from one well-defined task, build the feature around it, and measure whether it actually saves time.
Overview
We're software engineers who integrate AI, not an AI research lab. That means AI arrives as a feature inside a working product: a button that summarises a long enquiry, a search box that understands what customers mean, a form that fills itself from an uploaded document.
We work with the OpenAI, Anthropic Claude and Google Gemini APIs and choose a model for each task based on quality, speed and cost. Every response is validated against a schema before it's used, and sensitive data is kept out of prompts unless it's genuinely required.
AI tools are also part of how we work. Our engineers use Claude Code and Cursor day to day, which keeps delivery quick — with every change still reviewed by a person before it ships.
Built for
- Teams handling large volumes of text or documents
- Customer support and sales operations
- Products wanting smarter search
- Businesses exploring AI with a defined budget
Choose this when…
- A task involves reading, summarising or drafting text
- Information arrives in unstructured documents or emails
- Customers struggle to find things with keyword search
- You have a working app and want to add AI to it
What we provide
Summaries & drafting
Condense long messages, notes and documents; draft replies for a person to approve.
Document data extraction
Pull fields from invoices, order forms and PDFs into structured records.
Smart search
Search that understands intent and synonyms, not just exact keywords.
Assistants grounded in your content
Answer questions from your own product and policy information, with sources.
Content assistance
Product descriptions and translations generated in your tone for review.
Classification & routing
Tag and route enquiries, tickets and feedback to the right person.
Stack
We pick the model per task and keep the integration swappable, so you're not locked to one provider.
Model APIs
Application layer
How we build
Use cases
Enquiry triage
Summarise and categorise incoming enquiries so the right person replies first.
Order form intake
Turn emailed or photographed order forms into draft orders.
Catalogue enrichment
Consistent product descriptions and attributes across hundreds of SKUs.
Translation
Draft translations of product and marketing content for export markets.
Product & help search
Customers find the right item even when they don't know its name.
Report summaries
Plain-language highlights from sales and operations data.
Outcomes
01
Time back on repetitive reading and writing
Staff review AI drafts instead of starting from blank.
02
Answers people can check
Grounded responses with sources, validated before display.
03
Predictable running costs
Caching, limits and model choice keep spend in a known range.
04
No lock-in
Provider-agnostic code means you can switch models as they improve.
Got a task you suspect AI could handle?
Describe it and share an example. We'll tell you honestly whether AI fits — and roughly what it would cost to run.
Process
01
Pick the task
Find one job where AI saves measurable time.
02
Prototype
Test models on your real (anonymised) examples.
03
Integrate
Build the feature with validation and fallbacks.
04
Evaluate
Check accuracy, cost and speed against agreed targets.
05
Release
Roll out to a small group, then everyone.
06
Tune
Improve prompts and models based on real usage.
Why us
Task first, model second
We start from the job to be done, not the technology.
Careful with data
Server-side keys, minimal data in prompts, clear retention choices.
Honest about limits
If AI isn't the right tool for a task, we'll say so.
We build the whole feature
UI, backend and integration from one team.
FAQ
Do you build custom AI models?
No. We integrate proven models from OpenAI, Anthropic (Claude) and Google (Gemini) into your applications. For business features, that's faster, cheaper and more reliable than training your own.
Is our data safe when using AI APIs?
API calls run on the server, never from the browser. We send only what each task needs, remove personal details where possible, and use provider settings that exclude your data from model training.
What does an AI feature cost to run?
It depends on the model and volume. We estimate per-use costs during prototyping and add caching and limits so monthly spend stays predictable.
What if the AI gets something wrong?
Outputs are validated against a schema, high-impact actions require human approval, and the feature fails gracefully back to the normal workflow.
Do you use AI in your own development work?
Yes — our engineers use Claude Code and Cursor. It speeds up routine work, and every change is still reviewed by a person before it's merged.
Related services
API Development & Integrations
Secure REST APIs, webhooks and third-party integrations — payments, SMS, email, auth and your existing business systems.
Node.js Development
Real-time, event-driven Node.js backends — live tracking, chat, notifications and APIs that stay fast under load.
Full Stack Development
One team for the interface, the backend, the database and the deployment — from MVP to a platform that scales.
Custom Web Engineering
Enterprise-grade web platforms engineered on Next.js and React for speed, scale, and search visibility.
AI Integration
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