AI Adoption Intelligence for Engineering Leaders
Teamtrics reads the conversation between your developers and Claude Code. See whose ideas are landing, whose prompts are sharp, and who needs coaching — in real time.
Engineering teams using Teamtrics report up to 20× development output in 6 months.
Intent: “Refactor auth middleware to use JWT, remove session state”
AI read: ✓ Understood — JWT migration, stateless session removal
Intent: “fix the bug”
AI read: ⚠ Ambiguous — clarification required
The problem
Seat licenses and API bills are growing. But without telemetry, you can't distinguish the developer generating 80% AI-assisted code from the one asking one question a week.
The developer who explores the codebase with Claude, then goes off to write the code alone, is using a search engine with extra steps. The one who delegates precisely, iterates on AI output, and ships features with AI as a true co-author — that's the 10× multiplier you're paying for. Your dashboard can't tell them apart.
Without structured adoption metrics and intent quality data, you're telling a story about vibes — not outcomes. Your CFO wants numbers. Your board wants proof.
What real adoption looks like
Anyone can open Claude.
That’s not adoption.
AI adoption isn’t measured in logins, sessions, or seats purchased. It’s measured in the quality of work that could not have existed without AI — features shipped faster, architectures thought through more rigorously, debugging loops cut from hours to minutes.
The developer who asks Claude “what does this function do?” is using a tool. The developer who says “here’s the spec, here’s the constraint, here’s what I’ve already tried — let’s build this together” is working with a partner. The output difference is not 10%. It’s an order of magnitude.
Anyone can hand someone a tool. Teaching them to use it as a force multiplier takes expertise — and visibility. That’s what Teamtrics gives you.
The differentiator
Teamtrics doesn’t just count sessions. It reads intent.
Every Claude Code session generates a rich intent log: what the developer wanted, what tools were used, what the AI produced, and how closely the output matched the original goal. Teamtrics surfaces this data as actionable management intelligence — so you can coach the qualityof your team’s AI partnership, not just the quantity.
See prompt clarity scores over time. Identify developers who communicate vague, underspecified ideas — and coach them to precision before it becomes a shipped bug.
Track intent-to-output alignment per session. When AI drift is high, it surfaces in the timeline before it becomes a production issue.
Mastery isn't just about using AI more — it's about using it better. See whether each developer's prompt quality, session richness, and output alignment are trending up week over week.
Intent quality — real examples from the activity feed
Developer asked
“Extract the payment processing logic from OrderService into a standalone PaymentGateway class with a clear interface, keeping existing tests green”
AI understood
✓ Refactor: extract PaymentGateway, preserve interface contracts, maintain test coverage
Developer asked
“make the checkout faster”
AI understood
⚠ Ambiguous — attempted generic performance pass; may not address root cause
“Know not just what your developers are building — but whether they’re truly partnering with AI to build it.”
The solution
Teamtrics instruments Claude Code usage through a single copy-paste prompt — no scripts, no installs. A marker file in each repo tells the system exactly which folders to track. Every session, every commit, every AI-attributed line flows into dashboards built for engineering leaders — not developers.
Every developer advances through 5 phases with clear graduation criteria. Know exactly who’s plateauing at Explorer and needs a targeted nudge to unlock Practitioner-level habits.
Example: team currently at 62% average mastery
Transformation Score
74/100
Thriving
AI Adoption Rate
78%
↑ 12pp this month
Velocity Index
+23%
vs. prior 30 days
Cost per Feature
$42
Down from $89
AI Leverage
67%
avg AI-attributed lines
Mastery Index
0.62
Practitioner avg
Features
Every session, commit, PR, and tool call flows into a rich timeline — automatically, from the moment a repo is marked for tracking. Filter by developer, project, or task type. Read AI-generated narrative summaries — or drill into raw intent logs for granular coaching intelligence.
Five phases from Curious to Master, with graduation criteria for each. Know who's plateauing at Explorer and needs a targeted nudge to unlock Practitioner-level habits — before they fall further behind.
One-click shareable dashboards with PIN protection and expiry. Your CTO, board, or investor sees a live, auto-updating view — no exports, no slides, no weekly digest to assemble by hand.
Tie AI spend directly to features shipped. Track Cost per Feature, velocity delta between AI-heavy and manual developers, and AI-attributed line trends. Build the ROI business case in under five minutes.
Zero-friction setup
Copy a single prompt. That’s it. Drop a marker file into any repo you want tracked — Teamtrics automatically detects it and starts capturing sessions for that folder. No scripts, no installs, no changes to developer workflow.
ROI calculator
Adjust the sliders. See your projected ROI.
Annual value recovered
$599k
Monthly savings
$50k
Hours recovered / mo
461 hrs
Est. features / yr
+138
Based on METR/SWE-bench 2025: Claude AI agents complete coding tasks 1.7–7.8× faster than humans (2.5× used here — conservative Claude Sonnet midpoint). 40% of dev hours are AI-acceleratable. 1.3× fully-loaded cost. 40 hrs/feature. Targeting 80% team adoption.
Research-backed
METR (2025, SWE-bench): Claude agents complete verified coding tasks 1.7–7.8× faster than human developers. At a conservative 2.5×, adopted developers reclaim ~38 hours/month on coding tasks.
The payback math
At $20/month per seat, Teamtrics pays for itself if it lifts your team’s effective adoption by just 1 percentage point. Every point above that is pure recovered capacity.
Social proof
“Before Teamtrics, I had no idea which developers were actually using Claude Code and which were just paying for the seat. Now I can coach each person on exactly what they need to unlock the next phase.”
CTO
Series B SaaS · 50 developers
“The intent log is unlike anything I've seen. I can see when a developer is communicating vague ideas to the AI — and the output quality scores tell me exactly where to invest coaching time.”
VP Engineering
Enterprise Software · 200+ developers
“We went from 20% to 74% AI adoption in 4 months. Having the mastery journey visible made it a game — developers competed to phase up. The velocity numbers we showed the board closed our Series C.”
Head of Engineering
AI-first Startup · 30 developers
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