Production AI, engineered end to end, six eval-gated service lines.
The same playbook, tuned to the constraints of the sectors we ship into most.
Proof, not promises, selected case studies and recognition.
A transparent, 3-phase playbook from first audit to embedded team.
The senior team behind the work, and how to reach us.
From feasibility
to continuous operations.
Every engagement runs on the same playbook: discover the business case, co-create the solution, then build, ship, and review against the numbers we agreed up front.
Discover & diagnose
We start with your goals, not your tech stack: the business problem, why now, and what it costs to do nothing. Output, a written engagement memo, a build-vs-buy analysis, the success metrics we agree up front, and a benchmarked model recommendation.
- →Business discovery
- →Build-vs-buy memo
- →Success metrics (KPIs)
- →Model benchmark
Advise & co-create
We design the AI architecture (RAG, agents, fine-tuning) with your team, set up the eval harness, choose the vector store, and map the risks (budget, integration, security, adoption) before any production code is cut. You approve the plan, and the phased roadmap, before we commit to it.
- →Solution co-design
- →Eval harness
- →Risk & mitigation map
- →Phased roadmap
Build, ship & review
Demoable increments every Friday, continuous eval runs, red-team passes, and a clean handover at production. After go-live we run an executive business review against the KPIs we agreed, and line up the next phase of the roadmap.
- →Two-week sprints
- →Continuous evals
- →MLOps handover
- →Executive review
What we believe.
A pretty demo without an eval is theatre. We attach an eval harness before model selection and treat regressions as bugs.
No bench-warmers learning on your dime. Every engineer in your engagement is 5+ years deep in shipping production ML and AI.
We say no to AI builds where rules will work, where data isn’t ready, or where the risk profile doesn’t justify the autonomy.
Eval runs, drift monitors, audit trails, version pinning, the things that turn an AI demo into a deployable product.
The shape of an engagement, hour by hour.
Sync with your PM to scope the week’s tickets.
Engineers and designers in deep work.
Slack walkthrough of work in progress.
Automated checks, manual QA, accessibility.
Live demo of the increment + retro.
Let's talk about
what you're building.
30 minutes, one of our seniors, no slide deck. By the end of the call you'll know whether we're the right team, and if not, who is.