Depth over demos
A demo proves a model can. Depth proves a system will — at your latency, your cost, your load.
AI Product Engineering & Research Consultancy
Depth is thedifference.
Anyone can call an API. We build the whole product on top of it — and work the seven layers underneath, where latency, cost and ownership are actually decided.
↓Scroll to descend
From first prototype to a shipped product — and every layer beneath it.
Product engineering◆Zero to one◆RAG◆Multi-agent◆Fine-tuning◆Quantisation◆Distillation◆Inference◆Evaluation◆On-prem◆GPU◆Serving◆Small models◆Research◆Shipped products◆
Most AI work stops at the demo. A prompt, an API key, something that impresses in a meeting and never becomes a product. We build the whole thing — and go down every layer beneath it, because the layers nobody shows you decide whether a product is fast, affordable, and actually yours.
Own the lower layers
The layers nobody shows you decide whether a system is fast, affordable, and actually yours.
Measure or don't claim
Every improvement we report has an evaluation behind it that you can run yourself.
Leave nothing undocumented
We are consultants. The engagement ends. The system should not notice.
The stack
We work the whole stack.
Seven layers between a question and an answer. Most consultancies rent you the top one. Open any layer to see what we actually do down there.
The part everyone sees — and the part we ship, rather than hand off. Web frontends, backend services and APIs, mobile apps, and the AI surfaces layered over them: whatever shape the product needs, built by one team instead of split across three. And built so that a probabilistic system still feels dependable — streaming, citations, graceful failure, and the affordances that let a person stay in control.
- Web & mobile apps
- Backend & APIs
- AI-native interfaces
- Human-in-the-loop
- Trust & citations
Explore the full stack↗↗Seven layers, one system
Capabilities
Seven ways in.
Engagements are shaped around what you actually need built — a whole product, or the one layer your problem lives in. Not around a package we happen to sell.
Applications → Models
AI Strategy
Where to apply AI, what to build, and what to buy.
- /Opportunity mapping
- /Build vs. buy analysis
- /Model & vendor selection
- /Infrastructure economics
Applications → Inference & Serving
AI Product Development
Zero to a product in people's hands — not a pilot that stalls.
- /Zero-to-one builds
- /Prototype to production
- /Product engineering
- /Launch & iteration
Applications → Retrieval
AI Systems Engineering
End-to-end design and construction of production AI systems.
- /RAG architecture
- /Multi-agent systems
- /Evaluation frameworks
- /Observability
Training & Tuning
Model Optimization
Fine-tuning, distillation, and small language models.
- /LoRA & preference tuning
- /Distillation
- /Small language models
- /Task-specific evals
Inference & Serving → Silicon
Inference Infrastructure
Serving stacks engineered for throughput and cost.
- /Quantisation
- /Continuous batching
- /GPU deployment
- /Cost per token
Models → Silicon
Enterprise AI
AI that survives procurement, security review, and scale.
- /On-prem & VPC
- /Data residency
- /Security review support
- /Audit trails
Any layer
Research & Prototyping
When the answer isn't in a paper yet.
- /Applied research sprints
- /Feasibility spikes
- /De-risking prototypes
- /Written findings
Approach
How an engagement runs.
- Step 01
Discovery
We start with the constraint, not the technology. What must be true for this to be worth doing — and what happens if it isn't.
Problem brief & success criteria - Step 02
Architecture
The system drawn before it is built. Layer by layer, with the trade-offs written down where they can be argued with.
Architecture & decision record - Step 03
Prototype
The riskiest assumption, built first and measured against a real evaluation set. Fast enough to be wrong cheaply.
Working prototype & eval harness - Step 04
Build
The prototype becomes a product. The whole thing — application, services, data layer — built out to the standard a real user base demands rather than the standard a demo survives.
Complete, working product - Step 05
Optimization
Down the stack. Quality, latency and cost pushed until the curve flattens and further effort stops paying.
Benchmarked performance envelope - Step 06
Production Deployment
Shipped with the unglamorous parts intact: monitoring, rollback, on-call notes and a team that can operate it without us.
Live system & runbook - Step 07
Continuous Improvement
Models move, data drifts, costs change. A cadence of evaluation and tuning that keeps the system from quietly decaying.
Ongoing eval & tuning cadence
Work
Depth, in practice.
Three engagements, anonymised. Detailed case studies are available on request.
Next step
Let's build AI systems that last.
A first conversation costs nothing and usually saves a quarter. Bring the constraint you're stuck on — we'll tell you which layer it lives in.
Typical reply within one working day.