Build production-grade AI applications.
We design, build and run data science, generative AI and agentic AI applications on AWS, Azure and Google Cloud — from first prototype to a secure, monitored product your users rely on.
- Connect datadocs · CRM · tickets
- Build RAG + toolsretrieval · function calling
- Evaluateaccuracy · safety · latency
- DeployAWS · Azure · GCP
- Monitortraces · cost · feedback
Illustrative release pipeline — not client data
Three kinds of AI products. Any major cloud.
Every engagement ends with a working application in your cloud account — not a slide deck.
Production-grade from day one.
Evaluated before it ships
Every model and LLM feature is tested against a baseline and an evaluation set built from your real data. You see the numbers before anything goes live.
You own everything
Delivered into your repos and your cloud. No licence that ties you to us.
Exit after any stage
Stop after discovery, prototype or deployment and keep everything produced up to that point.
Secure on your cloud
Deployed inside your AWS, Azure or GCP account, under your identity, network and data policies. Where data must move, an NDA and a written processing agreement come first.
Four stages. Each with an exit.
A clear checkpoint at the end of every stage, so you’re never locked into the next one.
Frame the problem
We map the use case, audit your data and agree the metric that defines success — and say plainly if AI isn’t the right tool.
Exit with: data audit + success metricProve it works
A working application on your real data in four to six weeks, measured against a baseline or evaluation set.
Exit with: working prototype + eval resultsShip to production
Production deployment on AWS, Azure or GCP — with authentication, CI/CD, infrastructure as code and cost controls.
Exit with: production system + CI/CDKeep it honest
Monitoring for quality, drift, latency and cost; continuous improvements; and a handover so your team can run it without us.
Exit with: monitoring + full handoverFits around the team you already have.
Handover and documentation are part of every engagement — whichever model you choose.From 4-week prototypes to long-term product teams
Discovery engagements are fixed-price and scoped in days. Build projects are quoted after discovery, because an accurate number depends on the state of your data. We’ll give you a range in the first call.
A prototype against your real data usually lands in four to six weeks. Production deployment depends on your infrastructure and approval process, and we scope it once the prototype proves the approach.
Whichever you already run on. We build on AWS, Microsoft Azure and Google Cloud, using their native AI services — such as Amazon Bedrock, Azure OpenAI and Vertex AI — where they fit, or open-source models when you need more control.
Often, yes. We can lead delivery, embed alongside your engineers, or act purely as reviewers on work already underway.
You do. All code, models, prompts and documentation are delivered into your repositories and your cloud account. There is no licensing arrangement that keeps you tied to us.
We work inside your environment wherever possible. Where data must move, it’s covered by an NDA and a written processing agreement before anything is transferred.
Tell us what you want to build.
A short brief is enough: the application you have in mind, the data you already hold, your cloud platform and any deadline. We’ll come back with a scoping call — or an honest referral elsewhere.