AI engineering, end-to-end.
Applied LLM systems, AI-native applications across web, mobile, and desktop, retrieval and ML infrastructure. We do the engineering, not the slide deck.
- 01Discovery sprint
One to two weeks. Senior engineers map the problem, the data, and the surfaces — you leave with a scoped plan, not a deck.
- 02Build partnership
A small senior team ships in production from day one. Evals, observability, and rollback are launch criteria.
- 03Operate & evolve
We stay on the system we built — measuring, retraining, hardening — so it keeps earning its place in production.
Where the work tends to fall.
Eight overlapping practices that map to the engagements we run. Most builds touch three or four.
Applied LLM & Agents
Retrieval pipelines, agent architectures, and evaluation harnesses built to ship — not to demo.
- Custom LLM routing
- RAG over private data
- Tool-using agents
- Eval & guardrails
AI-Native Web
High-performance Next.js and React applications where AI is a first-class primitive — streaming UIs, real-time inference, edge-aware rendering.
- Next.js · React
- Streaming UIs
- Edge inference
- Realtime
AI-Native Mobile
Mobile experiences designed around model latency, on-device intelligence, and offline-first behaviour.
- React Native
- Native iOS / Android
- On-device models
- Offline-first
AI-Native Desktop
Desktop apps where AI does heavy local work — file systems, local models, native integrations. Built with Tauri or Electron when the platform calls for it.
- Tauri · Electron
- Local model inference
- Native APIs
- Auto-updates
Knowledge & Retrieval
Hybrid lexical and vector retrieval, document parsing for messy real-world inputs, and continuous quality monitoring on top.
- Hybrid retrieval
- Reranking
- Document parsing
- Drift monitoring
ML Infrastructure
Inference serving, eval pipelines as build gates, model registries, and the operational story behind every model in production.
- Inference serving
- Eval pipelines
- Cost & latency obs.
- Rollout discipline
Security & Compliance
Audit-grade trails, PII handling, prompt-injection defenses, and frameworks for regulated data — HIPAA, GDPR, SOC 2.
- AI guardrails
- PII handling
- Audit trails
- Regulated data
Cloud & DevOps
Modern cloud infrastructure tuned for AI workloads — GPU schedulers, queue-aware autoscaling, observability across the stack.
- AWS · GCP · Azure
- K8s · Terraform
- GPU scheduling
- Observability
How an engagement runs.
Four phases. Each one with a defined exit, so there is no drift between scope and reality.
Discovery
We pressure-test the problem with senior engineers — architecture, constraints, and what good looks like in production.
Design & Architecture
Detailed system design, data and inference flows, eval criteria, and the interface that ties them together.
Build
Senior engineers do the work end-to-end. Short feedback loops, evals on the build path, no junior handoffs.
Ship & Operate
Deploy with observability and rollback. Stay close through stabilisation, then transition to long-term support.
Tools we reach for.
Modern, well-supported tooling — chosen for the problem, not for novelty.
Frontend
Backend
Mobile
AI & ML
Data
Cloud
Engagement models, not packages.
We shape the engagement around the work, not the other way around. Pricing is scoped after a short discovery, never before.
Discovery sprint
2 – 3 weeksSenior engineers and a design lead spend two to three weeks pressure-testing the problem. You leave with an architecture, a build plan, and a fixed proposal for the engagement that follows.
Build partnership
8 – 16 weeksA focused team builds and ships the system end-to-end. Senior leads stay on the work the entire way — no junior handoffs after the sales call.
Embedded AI team
OngoingA senior AI engineering pod embedded with your team for the long term. Best when you have a roadmap and need durable AI capacity inside it.
AI audit & strategy
1 – 2 weeksIndependent audit of an existing AI system or product surface. We tell you what is real, what is fragile, and where the next quarter of effort actually pays off.