AI systems that earn their place in production
Most AI projects stall between the demo and the deployment. We specialise in the half nobody posts about — grounding, evaluation, latency and cost — so your system is still trustworthy on its thousandth conversation.
- 6 yrs
- Building AI systems
- 60+
- Products shipped
- 94%
- Client retention
Trusted by teams shipping real systems
AI capability, engineered for the second day
We build the systems, and the measurement that proves they work. Start with the practice closest to your problem.
RAG & Knowledge Systems
Turn scattered documents into a knowledge layer your AI can answer from — accurately, with sources.
AI Agents & Automation
Agents that use real tools, take real actions, and know their limits.
LLM Integration
Add language model capability to an existing product without destabilising it.
Computer Vision
Extract structure and meaning from images, documents and video streams.
MLOps & Model Infrastructure
The deployment, monitoring and evaluation layer that keeps AI systems honest over time.
All services
Strategy, product, enterprise software, security, design and marketing.
The problems that stall AI projects
Every one of these is something we have been called in to fix after someone else shipped it.
Your AI demo won't survive production
A notebook that impresses in a meeting falls over at 500 concurrent users. We build for the second day, not the demo.
The model confidently makes things up
Hallucination is a retrieval problem before it is a model problem. We fix the grounding layer first.
Nobody can explain what the system did
Every answer we ship can be traced to the passage it came from, with scores you can inspect.
Costs scale faster than usage
Careful caching, right-sized models and tight context budgets keep inference spend predictable.
Your data is scattered and unstructured
PDFs, wikis, ticket histories, spreadsheets. We turn messy sources into a queryable knowledge layer.
Internal teams are stretched thin
We embed alongside your engineers, ship with them, and hand over something they can own.
Results, with the numbers attached
Each of these started as a system that was not working well enough.
Cutting support volume by half with grounded retrieval
A national operator's support assistant answered 19% of queries correctly. Rebuilding the retrieval layer took it to 54% without changing the model.
Clinical documentation support that clinicians actually trust
Structured note drafting deployed entirely inside the hospital VPC, cutting documentation time 41% with every output clinician-reviewed.
Automating KYC review without losing the audit trail
Document extraction and verification cut manual KYC review 62% while making every decision reproducible for regulators.
Context matters more than code
The same architecture behaves very differently under HIPAA than under an adtech margin target.
Telecom
Deflect support volume, retain subscribers and make sense of network data at carrier scale.
Healthcare
Clinical and administrative AI built for environments where a wrong answer is not an inconvenience.
Fintech
Risk, compliance and customer operations where every decision needs an audit trail.
Advertising Software
Creative generation, campaign intelligence and reporting at platform scale.
Ecommerce
Discovery, support and merchandising that behave well on a catalogue of any size.
Education
Learning tools that support understanding instead of outsourcing it.
HR Tech
Hiring and people operations automation built to withstand a bias audit.
Four phases, and you can stop after any of them
Every phase produces something useful on its own. No phase depends on you committing to the next.
Discovery
1–2 weeksWe map the problem, the data, and the constraints. You get a written technical assessment with a recommended architecture and a cost model — useful even if you stop there.
- Technical assessment
- Architecture proposal
- Cost + latency model
- Risk register
Prototype
2–4 weeksA working slice of the real system against your real data. Not a mockup. We measure retrieval quality and answer accuracy before committing to a full build.
- Working prototype
- Evaluation harness
- Quality baseline
- Go / no-go recommendation
Build
6–16 weeksTwo-week increments, demoed live. Your team has repository access from day one. Every increment is deployable, tested and documented.
- Production system
- Test suite
- CI/CD pipeline
- Runbooks
Operate & hand over
OngoingMonitoring, evaluation dashboards and on-call during stabilisation. We train your engineers to own it, then step back to whatever support level you want.
- Observability stack
- Eval dashboards
- Team enablement
- Support agreement
Thirty people, organised around ownership
Engineering
Backend, data and ML engineers who ship to production.
AI Research
Retrieval quality, evaluation and model selection.
Design
Product and interface design for complex tooling.
Delivery
Engagement management and client communication.
What clients say when the project is over
“They rebuilt our retrieval layer and our support deflection rate went from 19% to 54% in six weeks. The difference was grounding, not a bigger model.”
“The only vendor that showed us their evaluation numbers before asking for a build budget. That bought a lot of trust.”
“We kept the team on after launch. They write the kind of code our own engineers wanted to inherit.”
Let's talk about what you're building
Tell us the problem. We'll tell you honestly whether AI is the right tool, and what it would take.