AIDevImpact gives engineering leadership the answer — measuring actual AI effectiveness across your organization by dev activity, by team, by model, and telling you exactly where to invest, course-correct, or double down.
Every engineering organization is going all-in on AI. Copilot licenses, Cursor seats, Claude Code subscriptions, API budgets, internal tooling — it's the largest developer productivity investment since cloud migration. But when the CFO asks "is it working?" the honest answer is: you don't know. Vendor benchmarks don't reflect your codebase. Developer surveys measure sentiment, not effectiveness. The board wants ROI data. You have vibes.
The data you need already exists. Every AI-assisted conversation your team has contains rich signal about what works, what doesn't, and where the gaps are. That signal is currently lost — scattered across thousands of isolated sessions that nobody analyzes.
AI makes developers faster. But faster at what? You can't tell if the code, architecture, and decisions coming out of AI-assisted work are better, worse, or the same.
AI might nail your debugging but silently produce bad architecture advice. You only hear about the wins. The failures are invisible until they become incidents.
Some teams use AI like a senior collaborator. Others paste code and accept the first response uncritically. The gap between best and worst is enormous — and invisible.
You're paying for Copilot, Cursor, Claude Code, ChatGPT — maybe all of them. Which ones actually deliver value for your team's specific codebases and workflows? Today that's a guess.
More seats? Fewer tools? Training on AI usage? Model switching for certain tasks? Human oversight where AI is unreliable? You need data to decide, not opinions.
Integrates with Copilot, Cursor, Claude Code, and any AI tool your team uses. Zero developer workflow change. Conversations are de-identified before they leave your infrastructure.
A multi-agent evaluation pipeline classifies each conversation by dev activity, then evaluates both the AI's process quality and output quality across 17 calibrated dimensions.
Individual scores aggregate into organizational intelligence. Effectiveness by tool, model, dev activity, team, and time period. Trends that are invisible at the individual level become clear and actionable.
An advisory layer translates patterns into decisions: where to invest, which teams need AI enablement training, which models to use for which tasks, and where human oversight is needed.
Know which tools actually work for which scenarios in your environment — measured against a consistent, calibrated scale across your entire org.
See which teams use AI like power users and which would benefit from enablement — with specific recommendations on what to improve.
Surface hidden risks: conversations where AI outputs look correct but the reasoning was flawed, indicating fragile results that won't hold at scale.
Walk into a budget meeting with evidence, not anecdotes. Justify tooling spend, identify cost optimization opportunities, and prove ROI to the board.
Everything in the dashboard is available programmatically. Integrate AI effectiveness data into your existing BI tools, sprint reviews, or internal dashboards.
Conversations are de-identified before they leave your infrastructure. The platform never sees raw code, PII, or credentials. De-identified conversations are not stored. Security is built into the architecture, not bolted on as a policy.
We're working with some engineering leaders before general availability. Join the waitlist to get early access and a free AI effectiveness assessment for your codebases and teams.
We'll reach out when we're ready for your cohort.