AI copilots
In-product assistants that help users do their job faster, embedded where the work already happens.
AI Software
AI features users trust, not demos that impress once.
A demo takes a weekend. Software that thousands of people rely on takes architecture, evaluation and discipline. We build the second kind, and we use AI to get there faster.
The difference
Production AI is mostly engineering.
The gap between a compelling AI prototype and a dependable product is wider than it looks. Prototypes have no failure modes, no cost ceiling, no latency budget and no unhappy users. Production has all four, plus the ones you haven't thought of.
We build AI features the way we'd build any critical system: grounded in your data, bounded in what they can do, evaluated against real cases, instrumented so you can see what's happening, and shipped with the ability to roll back. The model is one component, the engineering around it is the product.
What we build
AI software, end to end.
From a single feature inside an existing product to a platform built around AI.
AI assistants
Conversational systems with real context, grounded in your data, aware of permissions, honest when they don't know.
RAG systems
Retrieval that actually retrieves. Chunking, ranking, evaluation and citation so answers are traceable and correct.
AI search
Semantic and hybrid search that understands intent, and returns results your users trust.
Recommendation systems
Relevance engines tuned to your business goals, not just click-through rate.
AI-powered SaaS
New products where AI is the core value, not a feature bolted onto a traditional app.
Custom LLM applications
Fine-tuning, evaluation harnesses and orchestration for cases where off-the-shelf models aren't enough.
Model & cost engineering
Latency, caching, routing and unit economics, so the feature stays viable at real usage.
Evaluation & observability
Test sets, regression checks, logging and feedback loops. You can prove it works, and catch it when it doesn't.
What shipping actually looks like.
Beyond the feature itself, you get the engineering that keeps it reliable.
- AI features integrated into your existing product
- Grounded answers with sources, not confident guesses
- Evaluation suites that catch regressions before users do
- Latency and cost budgets that hold at scale
- Access control that respects your existing permissions
- Observability into every model call and failure
- Fallback behaviour when the model can't help
- Documentation your team can maintain
How it runs
The same five steps, every time.
You always know what's happening, what it costs, and what it's expected to change.
- 01
Discover
We map how the business actually runs, systems, data, workflows and the people inside them.
- 02
Identify
We surface the high-value opportunities where AI and automation change the outcome.
- 03
Prioritize
Impact, complexity, cost and risk, ranked, so you build the right thing first.
- 04
Build
We design and ship the agents, automation and software that deliver it.
- 05
Improve
We measure what changed and continuously improve the system in production.
Proof
Where this has already worked.
Real engagements, real outcomes, described honestly.
We migrated the application to a Next.js and Node.js architecture on AWS, rebuilt the database layer and shipped a companion mobile app.
Faster page loads, materially stronger organic search performance, and a platform the team can keep extending.
FAQ
Questions, answered honestly.
No sales spin. This is what people ask before they start working with us.
Not sure where to start?
Begin with an AI Opportunity Audit. Two weeks, a ranked plan, and an honest answer about what's worth building.