AI consulting
Strategy, architecture, vendor decisions, implementation plans, and production risk reviews for companies that need AI to create revenue instead of slideware.
We build the systemsbehind AI-native companies.
We are an AI production lab. We develop proprietary models, fine-tune on your domain, build agentic systems, and engineer cloud-native inference infrastructure — then ship it all to production under one senior engineering org.
Models in prod. Not pilots in slides.
We train models, write platforms, and harden cloud stacks — then deploy them with observability, evals, and ownership your team keeps. The deliverable is running infrastructure, not a roadmap PDF.
The production gap
Most companies rent intelligence — API wrappers, generic copilots, fragmented SaaS — and rent infrastructure they never fully control. The result is a stack that demos well and fails under load, cost, or the first serious security review.
We close that gap. Proprietary models trained on your domain. Agentic systems in your repository. Cloud infrastructure you own and can audit. One engineering standard from training job to edge deployment.
Your models. Your code. Your cloud. Running in production.
Inference & models
From fine-tuning to production inference
Most teams stop at a working model in a notebook. Production starts when that model is fine-tuned on your data, evaluated against real failure modes, and deployed on infrastructure that survives load, drift, and the first incident review.
We engineer the full path — training and fine-tuning pipelines, retrieval and guardrail layers, agent orchestration, and inference serving built for throughput, latency, and cost. The same team owns the weights, the eval harness, and the GPU path they run on.
That is what we mean by an AI production lab: not a demo model or a rented API. Proprietary intelligence you control, running on infrastructure you can audit, with agents and workflows wired into how your company actually operates.
Train it. Evaluate it. Serve it. Own it.
What we build
Full-stack engineering from model to metal
Six engineering lanes — consulting, models, cloud, agents, data, inference — owned and shipped by one production team so nothing sits in a silo.
Proprietary AI models
Fine-tuned and domain-specific models trained on your data — with eval harnesses, versioning, and production deployment. Not a chatbot skin. A model layer your company actually owns.
Cloud infrastructure
Multi-region, containerised, infrastructure-as-code. Cloudflare, Kubernetes, Postgres, edge — architected for uptime, security reviews, and spend that scales with value, not chaos.
Agentic systems
Agents orchestrated over your stack — tool use, RAG, human-in-the-loop, audit logs. Intelligence embedded in workflows, not pasted on top of them.
Data & ML pipelines
Ingestion, feature stores, training jobs, and inference paths wired end-to-end. The data plane that feeds your models and your product from the same source of truth.
Inference engineering
Serving paths built for throughput, latency, and cost — batching, quantization, autoscaling, and observability on the inference layer. Models that hold under real traffic, not just benchmark scores.
How engagements work
Three ways to work with us
Most companies start with one and expand once trust is real. Every engagement begins with a written scope, a fixed price or rate, and a named owner.
Technical audit
Deep review of architecture, models, cloud, and delivery. You get a hard map of what to rebuild, what to keep, and what to kill — before capital goes into the wrong layer.
Build sprint
One production outcome: a deployed model, a platform, an agentic workflow, or an infra migration — scoped, priced, and shipped with tests and observability included.
Embedded lab
We operate as your in-house production team — roadmap execution, model iteration, infra ownership, and senior architecture on call. Scale without hiring a full org overnight.
Where we usually find you
We were built for this
Seven signals your stack is outgrowing rented tools and borrowed intelligence. If you recognise more than one, we should talk engineering.
- Your AI still lives in demos, not in production.
- You run on models and APIs you do not control.
- Your AI workflows are duct-taped to off-the-shelf tools.
- Cloud cost scales faster than engineering output.
- No single team owns models, code, and infra together.
- You need a production lab, not another agency deck.
- You are shipping features without shipping infrastructure.
Production record
Production outcomes across recent builds — models deployed, platforms shipped, infra hardened. Details on a call; client names stay under NDA.
40+
Models & agents in production
30+
Custom platforms shipped
$1M+
Cloud spend optimised
99.9%
Uptime on managed stacks
How we work
Architect. Model. Build. Operate.
How we move from architecture to trained models to deployed systems — same discipline whether the lane is inference, platform, or cloud.
Stage 01
Architect
We map your product, data, and infra into a single technical blueprint — model boundaries, service contracts, cloud topology, and what ships first. No ambiguity before a line of code moves.
Stage 02
Model
We train and fine-tune models on your domain — eval suites, retrieval layers, guardrails, and deployment targets. Intelligence you own, not a wrapper on someone else's API.
Stage 03
Build
Agentic workflows, APIs, and production systems in your stack. Senior engineers shipping to production with CI, observability, and load paths that survive real traffic.
Stage 04
Operate
Cloud infra that stays up, scales, and stays auditable. Runbooks, monitoring, cost control, and handover your team can run without us in the room.
How we think
How we engineer under pressure
Production or nothing
Staging is not a deliverable. We ship with monitoring, rollback paths, and load in mind — the same bar we hold for our own systems.
Own the intelligence
Your models, your weights, your evals, your deployment. We build capability inside your boundary — not dependency on a black-box vendor.
Infra is product
Cloud, security, and observability are designed alongside features. If it cannot survive an incident review, it does not ship.
Engineering over theatre
No buzzword layers. Clear architecture, readable code, measurable outcomes. What we build has to survive the engineers who inherit it.
Engineering standard
Modern technology. Silicon Valley network. Production results.
Our clients rarely ask which framework we use. They ask whether the system ships, holds under load, and stays owned by their team when we are gone.
We work from a Silicon Valley network — operators, researchers, and founders already shipping the next generation of AI in production. That keeps us on the most modern technologies available, without turning your project into a vendor catalogue.
The deliverable is not a stack slide. It is proprietary models, agentic systems, and cloud infrastructure that lands in production — engineered for outcomes, not buzzwords.
Build what you own. Ship it for real.
Tell us what you need in production — a model, a platform, an agentic system, or the cloud underneath. We scope it like engineers and ship it like owners.