AI investment is easy.
Getting results isn’t.
We work inside your team to fix the system slowing you down and ship AI that actually works.
Why AI Fails
AI isn’t failing because of the technology.
It’s failing because teams bolt it onto broken systems — instead of fixing the system and embedding AI where it actually compounds. Delayed feedback loops, fragmented ownership, and work that never makes it to production don’t get fixed by AI. They get amplified.
That's why most efforts stall at demos instead of delivering real results.
The Work
We fix the system first. Then we make AI work.
We've built and shipped AI systems — we know what works, what doesn't, and where the leverage actually is. We embed with your team, fix the foundations holding you back, and put AI where it compounds.
AI Adoption
AI adoption is shallow — and it's not just a tooling problem
Most teams buy Copilot licenses and watch adoption plateau. A few power users stick with it — the rest drift back to old habits. Leadership has no visibility into what's working.
We fix what's actually slowing your team down first — broken review loops, unclear ownership, fragmented tooling. Then we embed AI where it compounds.
Engineering Velocity
Your engineering pipeline is slower than it should be
PRs sit in review for days. CI failures get investigated manually. Deploys are batched weekly because each one feels risky. A quarter of your engineers' time goes to glue work between tools.
We fix the pipeline first — then embed AI at the points that actually accelerate delivery. AI-assisted review, automated test generation, smarter CI triage. Smaller changes, shipped faster, with less risk.
Operational Knowledge
Your team solves the same incident twice.
An incident fires at 2am. The on-call engineer spends 30 minutes searching Slack and stale runbooks. After resolution, no one writes a post-incident review. The same incident repeats three months later.
We build AI-powered triage that surfaces past incidents, runbooks, and probable root cause the moment an alert fires — cutting resolution from hours to minutes. Postmortems get drafted automatically. Runbooks stay current. The knowledge compounds instead of disappearing.
Our Approach
No discovery workshops. No maturity models. We trace your actual workflows, fix what's broken, and embed AI where it compounds — then prove it with numbers.
Step 1
Map the system
We trace a PR from commit to production. We follow an incident from alert to resolution. We measure where time actually goes — not where people think it goes. You get a map with the bottlenecks and leverage points marked.
Step 2
Fix the foundations
Broken review loops, flaky CI, fragmented tooling, manual handoffs — these don't get fixed by AI. They get amplified. We fix them first. This is the step everyone else skips.
Step 3
Embed AI with your team
We target AI where the data says it'll compound — but we do it with your engineers, not to them. We pair in real workflows, build shared patterns for your codebase, and let skeptics see it work on their own code. The goal isn't 100% AI usage — it's engineers reaching for AI when it's the right tool.
Step 4
Continuous Optimization
DORA metrics. PR cycle time. Incident resolution. Adoption by workflow, not by seat count. The numbers tell us what's working, what to expand, and where to go next.