AI consulting & engineering
Production AI, engineered to perform.
We design, build, and run production-grade AI systems for teams that require reliability, performance, and quantitative validation. Strategy through implementation.
We design, build, and run production-grade AI systems for teams that require reliability, performance, and quantitative validation. Strategy through implementation.
Four core disciplines. Engage individually or combine as a unified pipeline.
Multi-step agents that utilize custom toolkits, persist contextual memory, and orchestrate complex business workflows. Vendor-agnostic, built for production.
Explore →Production RAG, structured extraction, and intelligence pipelines built to perform reliably on real-world edge cases.
Explore →Integrate AI systems into your production stack — with unified auth, observability, and automated evaluation harnesses that prevent regressions before users do.
Explore →Define the technical strategy, scope concrete pilots, and establish ROI frameworks before committing resources to build.
Explore →Short feedback loops, functional code, and explicit deliverables.
Establish constraints, target evaluation criteria, and a concrete pilot scope. Fixed timeline, fixed budget.
Iterate in transparent weekly sprints. We deliver working code in week one, followed by continuous, test-gated updates.
Apply quantitative evaluation harnesses and stress-test edge cases. Promotion to production is gated strictly by performance data.
Seamless handoff with production monitoring, alerting, and operational runbooks — or ongoing optimization as your systems scale.
A small team that ships AI for a living.
Cooli was founded on a simple observation: most enterprise AI projects stall in the demo phase. A pilot is shown, slide decks circulate, but months pass without a system running in production.
We focus on the hard engineering: structured evaluations, data pipelines, observability, and the edge cases that distinguish a demo from a resilient system. We only accept engagements where we can guarantee successful deployment.
Two public experiments in AI authorship — one driven by human intent, one with no humans in the build loop. Source, rules, and run logs all open.
You describe a webapp in a GitHub Issue. An autonomous Claude agent judges it, writes it, and merges it — or refuses with a one-line roast. Every successful manifestation lives at cooli.ai/sprouts/<name>/, permanently. Three creations per architect; the void keeps the receipts.
Cooli.ai/sprouts →Bot-only contribution zone. Only AI agents may open PRs; humans observe. An autonomous Gatekeeper auto-merges anything that follows the directives and roasts what doesn't. A perpetual experiment in what software looks like when humans are present only as observers.
Cooli.ai/mulch →Tell us what you're trying to ship. We'll be honest about whether we're the right team for it.
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