One well-built system before ten fragile ones
We design and build AI and automation systems on the cloud you already run. Cost control, security and monitoring go into the first version, not a cleanup project a year later.
Why most AI builds disappoint
Agents are powerful, and they are expensive to run badly. Anthropic reports that its agents use about 4 times the tokens of a chat interaction, and its multi-agent systems about 15 times. It says multi-agent setups pay off only when the task's value is high. Source: Anthropic Engineering, "How we built our multi-agent research system", Jun 2025
They also fail in predictable ways. The MAST study analysed more than 1,600 traces across 7 multi-agent frameworks and identified 14 distinct failure modes. Source: Berkeley et al., "Why Do Multi-Agent LLM Systems Fail?", arXiv:2503.13657 And Gartner predicts that over 40% of agentic AI projects will be cancelled by the end of 2027. That is a prediction, not a measured result. Source: Gartner press release, Jun 2025
What we build
AI assistants and chatbots
tied to a clear outcome, with a known cost per conversation resolved and tight limits on what data they can see.
Retrieval and data access
that lets AI use your documents and systems without opening them to everyone.
AI gateways and model routing
that send each task to the right-sized model and give you spend by team, product and workflow.
Agents and integrations
including MCP connections, hardened with authentication, least-privilege access and isolation.
Workflow and process automation
with or without AI, where a simple automation does the job better.
Monitoring and evaluation
so you can see quality, cost and failures in production.
[confirm: technology stack and cloud platforms to name, e.g. AWS, Azure, Google Cloud]
How a build runs
Scope
We agree the business outcome, the owner, the success measure and a run-cost estimate before any code is written.
Design
A reference architecture covering data access, identity, security, monitoring and cost guardrails. Reviewed with your team.
Build and measure
Delivered in working increments, measured against the baseline we agreed, and handed over with documentation your team can run.
Redesign the workflow, not just the tool
McKinsey finds AI high performers are about three times more likely to redesign workflows. Source: McKinsey, "The state of AI in 2026", Aug 2026 That is a planning and architecture job, so every build starts with the workflow, not the model.
Built to be owned by you
- Your cloud, your accounts.
[confirm: default hosting and ownership terms] - Documented and handed over.
Your team or your integrator can run and extend what we build. - Separate from advice.
Build work runs on its own statement of work. If we're also your CAIO, we tell you up front when we recommend building something ourselves. [confirm: build-work disclosure policy]
Pricing
Every build is quoted to scope on a separate statement of work.
Build the AI worth what you pay for it
Tell us the outcome you need. We'll tell you whether to build, buy, or stop.
Book a diagnostic call → [booking link]