We measure how AI impacts your delivery speed and quality. Then we work alongside your team to compound the gains.
Built by technology operators who scaled VC-backed companies and shipped software used by Fortune 100s. Product, engineering, and security in one team.
Your engineers use Copilot, Cursor, Claude Code every day. Your AI budget is climbing. Engineering is 40–60% of your company's spend. Whether any of it is moving product velocity is usually a guess.
The metrics available count inputs: tokens, seats, license usage. They don't measure outcomes. They can't answer what matters: is it working, and where?
of organizations use AI tools
capture meaningful enterprise value from it
Source: McKinsey, 2025 Global Survey of 2,000 executives across industries.
We work alongside your engineering leadership. We set up our measurement system to capture productivity baseline and AI adoption. We partner with you to move the metrics that matter: velocity, quality, AI leverage. On day 91, the measurement keeps running.
GitHub and Slack connected. Measurement system live. Baseline capture begins.
Signal flows to dashboards. Productivity and AI adoption become visible. Trends emerge.
Better AI leverage. Data-backed calls. Velocity that sustains.
Grounded in DORA, SPACE, and DX Core 4. Instrumented via GitHub and Slack, so engineers keep working the way they already do. Together they cover delivery, quality, developer experience, AI impact, and business outcomes.
Team-level delivery signal, never individual.
Auto-classified by Claude. Rising ratio means quality is slipping.
Leading indicator for friction, before delivery metrics break.
Three questions at CR merge. Hours saved, not tokens counted.
Engineering output anchored to business outcomes.
The measurement shows what's happening. The engagement is where we partner with your leadership on what to do about it: AI adoption, security posture, team structure, architecture.
Copilot, Cursor, Claude Code, agentic tools. Where AI accelerates and where it doesn't.
Secrets hygiene, code provenance, shadow AI risk, prompt and agentic data handling.
Protecting velocity from silent quality erosion as AI adoption scales.
Hiring priorities, team structure, and architecture calls as they surface.
Senior operators with experience in environments from 10 to 1,000 engineers, across VC, PE, and public markets. We shipped first lines of code for multiple startups as well as software used by Fortune 100 companies.
We built Klymb AI because we know what it's like to make scaling decisions without the data you wish you had.
The five-metric system synthesizes a decade of engineering productivity research from DORA, SPACE, and DX Core 4. Our contribution: making it installable for scaleups in 90 days, with an AI impact layer none of the original frameworks anticipated.
90 days from now, you'll have early wins, the evidence behind them, and the muscle to keep going.