NRD Insight reads every change your team ships — then answers in whichever language you speak. Plain English for the boardroom. Quoted evidence for the engineering review. Same truth, two views.
14 days free · up to 5 repos · nothing analyzed until you say so.
Your code never leaves AWS and is never used to train models.
So the person paying for the team can't read the tool that measures it. NRD Insight ships two purpose-built views over the same analysis — pick yours.
The Executive view answers the three questions you actually have — how is the team doing, how is each person doing, and what should we do next — in language nobody has to translate for you.
The Technical view is the full instrument: 12 dimensions per pull request, 24 engineering principles checked, and a quote from the diff behind every score you can click into.
Install the GitHub App and choose exactly which repositories to analyze. Nothing is analyzed until you enable it, repo by repo. Revoke anytime in one click. Optionally connect Linear to tie code to the roadmap.
A sentence about your company — or just your website, which it will read for you. Every review afterwards weighs findings against what actually matters to your business, not a generic app's.
Executives land on the plain-English brief. Engineering leaders land on the full instrument. Switch anytime from your profile menu — same data underneath.
A written, plain-English read on team health, wins, risks and what to do next. Regenerated as the work changes, ready before you open it.
A principal-engineer pass posted straight onto the pull request: material findings with tradeoffs, never style nitpicks.
Per-person quality, business impact, trajectory and growth — with the evidence, so 1:1s and reviews write themselves.
Did the change do what the ticket asked? Linked automatically from branches and commits, even when nobody tagged the issue.
A recurring whole-repository read: layering, debt hotspots, dependency rot, security architecture — with a health score you can track.
Every weakness becomes a hands-on program generated from that developer's real pull requests, runnable in a sandbox.
Team-level rollups for leads, and a deliberate rule: praise is public, comparison is private. Members never see a leaderboard.
A short weekly email so the story reaches you even in the weeks nobody opens a dashboard.
Every plan includes AI credits, with a hard cap you set. Analysis pauses rather than surprising you with an invoice.
The rubric is fixed, versioned, and visible to your whole team — the same bar for everyone. Trivial changes (lockfiles, typos) are filtered out before they cost you anything. And we never rank on lines of code.
We ask for a lot of trust — read access to your source code. Here is exactly how we honor it.
Analysis runs on Amazon Bedrock inside our AWS environment. Diffs are stored encrypted (KMS) in S3, transit is TLS everywhere, and your code is never used to train any model.
The GitHub App requests read-only access to contents, pull requests, and metadata — nothing else. You choose the repos. Uninstalling revokes everything instantly.
Every row of your data is isolated with Postgres row-level security enforced at the database layer — not just application filters. Your data is invisible to any other customer, by construction.
Every score traces to a stored model response and a quoted diff. When a number surprises you, click through to the exact evidence — no black boxes.
Runs entirely on AWS infrastructure holding SOC 2, ISO 27001, and PCI DSS attestations. Payments are handled by Stripe — card data never touches our servers.
Hard monthly AI-spend caps you configure. Export or delete your data on request. A DPA is available for teams that need one.
A seat is a developer whose code we analyzed that month — executives, managers, and stakeholders are always free. Every plan includes AI-analysis credits; heavy months can opt in to overage at $0.05/credit, and you can cap spend so an invoice never surprises you.
That's what the Executive view is for. It's a written brief — a headline, a health grade, what's behind it, who needs support, and three to five things you could do about it — deliberately written without engineering jargon. It says "their changes often skip safety checks", not "low error-handling score". If a number appears, it's measured from the work, never invented by the AI.
No. Never. Analysis runs on Amazon Bedrock inside AWS, whose terms prohibit using your inputs to train models. Diffs are processed to produce your scores, stored encrypted for your own audit trail, and used for nothing else.
The fastest way to make engineers hate measurement is to measure motion — commit counts, lines of code. We deliberately never rank on volume. Engineers get the same evidence leaders see, their own private growth view, and training built from their own code. The rubric is fixed and visible: the same bar for everyone, with receipts. Praise is public; comparison is private.
Read-only: repository contents, pull requests, and metadata on only the repositories you enable. No write access of any kind, except posting a review comment on a pull request if you turn that on. Uninstalling the GitHub App severs access immediately.
Connecting takes minutes and the first analysis lands overnight. Want to see it before connecting anything? Load the sample team on signup and walk the whole product with realistic data in about two minutes.
Only a developer whose code we analyzed in that billing month. Everyone who just views dashboards — executives, managers, stakeholders — is free, and your invoice lists exactly which developers were counted.
All of them. The rubric measures engineering judgment — correctness, testing, error handling, design — which the AI evaluates in any language or framework, informed by each repository's own conventions.
Trivial changes are filtered before any AI runs, every plan includes a generous credit allowance, and when it's exhausted analysis pauses (cheap git metrics keep flowing) until you opt in to overage or your period renews. You are structurally protected from surprise bills.
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