AI-Native Development
Your AI development partner for production delivery
We add AI to the product you already run, one feature at a time: LLM features, search over your own data and agents that use your APIs. Each one is scoped, scored against an eval set and monitored in production, so AI becomes part of your product rather than a prototype beside it.
Organisations we've built for
Best for
When AI-native development is the right move
Your product is live and has users. Now AI has to become part of it, without breaking what works.
Product features
Your customers expect AI features, and your roadmap has no room to learn how to build them.
We ship summaries, drafting, search and recommendations into your existing stack, behind feature flags, each with its own eval set.
Decisions in your product
Triage, routing or review steps in your product still wait for a person.
Agents take the repetitive decisions within set limits, hand uncertain cases to a person and log every step.
Unstructured data
Your product holds documents, tickets and emails that nobody can query.
We turn them into data your product can search and act on, with retrieval tuned to your own content.
Ongoing delivery
One AI feature went live, and now its quality drifts and nobody looks after it.
A steady delivery stream: regression checks on every release, monitoring in production and priorities set with your product owner.
What you get
What we add to your product
AI that fits your architecture, your data rules and your release process.
LLM integration
Model calls, prompts and system messages kept in code, versioned and tested like the rest of your product.
Retrieval over your data
Vector search and retrieval (RAG) tuned to your content, so answers come from your data and show their sources.
Agents and tool use
Workflows where the AI calls your APIs within set limits, with a fallback when it can't decide.
Data readiness
Unstructured data cleaned, labelled and instrumented, so the model gets what it needs.
Every feature includes
An eval set and regression checks on every release
Fallbacks, with a person in the loop where it matters
PII filters, permissions and audit logs
Documentation your engineers can build on
How we deliver
How AI work stays predictable
The same three steps as every project we run, repeated for each feature.
01
Plan
Acceptance criteria for the feature, its failure modes mapped early, and a list of what we won't do. We agree which data the model may see.
02
Thin slice
The feature working behind a flag on your real stack, scored against its eval set, with a demo every week.
03
Launch and handover
Monitoring for drift, fallbacks and escalation paths, and documentation, so your engineers can run and change it.
Example projects
What adding AI to a product looks like
Three illustrative examples from sectors we haven't shown yet: the feature, how it fits the product and how we'd measure it. They aren't client projects; client case studies are added as clients approve them.
Example · Property management software
Triage for tenant repair requests
The problem
Tenants report repairs through the app, and property managers read every request to decide who handles it and how urgent it is.
What we'd add
A triage feature that sorts each request, sets the urgency, suggests the contractor and passes unclear cases to a person, behind a flag first.
How we'd measure it
Correct routing on a labelled sample, time to first response, and the share of requests handed to a person.
Example · B2B e-commerce
Search that understands a parts catalogue
The problem
Buyers search a catalogue of spare parts by description or an old part number, and too many searches find nothing.
What we'd add
Search over the catalogue, data sheets and cross-references, with answers that show the matching parts and why they match.
How we'd measure it
Searches with no result, clicks on the right part from the first page of results, and answer accuracy on an eval set.
Example · Legal software
Clause extraction in a document management product
The problem
Users of a document management product for law firms look through long contracts by hand for terms, deadlines and liability caps.
What we'd add
A feature that extracts those clauses with a link to the exact passage, in the customer's own environment, with lawyers checking the result.
How we'd measure it
Clauses found and correctly extracted on a reviewed sample, review time per contract, and corrections per document.
AI strategy
What to build first, and what the AI Act asks of it
Engineering guidance, not legal advice. We give your product and legal teams a clear picture, and they make the call.
Where to start.
We go through your product and data with you and rank the possible AI features by value, effort and risk, so the first one is worth shipping.
Each feature mapped to the Act.
We note which of the AI Act's categories each feature falls into and which duties follow, such as telling users they're dealing with an AI.
Compliance built in.
Logging, human oversight, AI notices and documentation are part of the feature from the first release, not added before an audit.
Security and data
Your data and your users stay protected
What the model may see, where it runs and how users are told, agreed in writing before the first feature.
No training on your data.
We only use AI tools on your code and data under business terms that exclude training, and only with your approval.
Your permissions apply.
The AI only sees what the person asking may see, and model providers and regions are agreed with you, with EU hosting where you need it.
Users know it's AI.
AI features say so, AI output is marked where the Act requires it, and every model call is logged.
Sectors and technologies
Where we've built, and what we build with
Sectors
Our engineering team has built production systems for hospitals, banks, cooperatives and a national geospatial institute, so AI goes into products that already handle sensitive data.
Healthcare
Financial services
Logistics & mobility
Public sector & geospatial
Technologies
The tools we use most when we add AI to a product. The model, the hosting and the region are agreed with you first.
Large language models via business APIs
Retrieval (RAG) and vector databases
Agents and tool calling
Evals and regression checks
Python
TypeScript
PostgreSQL
AWS
FAQ
Questions about AI-native development
Related services
Often combined with
Build
AI App Development
A new AI tool built around one costly workflow, for your staff or your customers.
Build
SaaS Platform Development
Multi-tenant platforms with auth, billing and admin built in, so v2 doesn't need a rewrite.
Data
Data Foundations & Analytics
One source of truth your reports and AI can rely on: pipelines, data models and dashboards.