Know what to refactor next.
Stop guessing before planning.

Faultlines turns your git history into a live feature map — with bug hotspots, test coverage per user flow, and the AI-context your agents actually need. One scan. No Jira hygiene.

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Free tier · runs locally · code never leaves your machine

The hidden tax on every engineering team

You can't plan a quarter you can't see.

Every sprint, EMs and tech leads burn hours guessing which parts of the codebase are rotting, which user journeys are uncovered, and which “small refactor” is actually a three-week hole. Jira and dashboards don't tell you. The code does — if you can read all of it.

01 · Invisible debt
You ship features blind to which ones are rotting.
Bug hotspots, churn, and stale ownership hide in 10K files of git history. Without a feature-level view, every refactor proposal is “trust me, bro.”
02 · Coverage theater
80% line coverage. 0% confidence.
Line coverage tells you what executed, not what matters. Which user flows are actually tested? Login? Checkout? Forgot-password? No tool answers that — until now.
03 · AI agents flying blind
Cursor & Claude have no map of your codebase.
Agents grep, guess, and burn tokens on dead ends because nothing tells them “these 14 files = Billing, 78% covered, last 3 PRs were bug fixes.” Context is the new bottleneck.

Faultlines reads what your team already wrote — the git history — and turns it into the map you wish you had on day one.

One scan. Three powerful answers.

Built for engineering leaders
and the AI agents on their team.

Faultlines turns git history into a feature map, scores each feature on bug density & coverage, and exposes it all through MCP so humans and AI agents work from the same source of truth.

Feature map from git history
Auto-clusters thousands of files into named product features — Billing, Auth, Checkout — in minutes. No Jira hygiene, no manual tagging. 85.8% avg recall on customer-facing features across 17 production OSS repos.
Health & coverage per user flow
Bug-ratio, churn, freshness, and test-coverage scored at the symbol level — then rolled up per feature and per flow. Know which journeys are tested, which are rotting, where to invest this quarter.
MCP-native for AI agents
11 read-only MCP tools that Cursor, Claude, and Copilot call directly. Your agent stops grepping and starts shipping with real context: “Billing touches 14 files, 78% covered, last 3 PRs fixed regressions.”

Not a replacement for Sourcegraph.
Not a clone of CodeScene.

Faultlines lives in a specific slice: feature-level, history-based, git-native. Here's where that slice overlaps and doesn't overlap with the tools you probably already pay for.

CapabilityFaultlinesCodeRabbitSourcegraphCodeScene
Feature-level clustering with business namesyes(LLM from git history)no(PR-scoped only)no(file/symbol search)partial(manual or heuristic)
User-flow detection from codelogin, forgot-password, create-org — named by intentyes(Haiku 90%+ accuracy)nonono
Symbol-line scoped scoringfunction-level blame, not just file-levelyes(line-range blame index)nopartial(precise code intel)no(file-level only)
Bug-ratio / hotspot per featurechurn weighting, bus factor, age decayyes(recent bugs weigh 2×)nonoyes(10-year leader)
Test coverage scoped to a featureaveraged over symbol line ranges, not filesyesnonono
PR impact in business language“this PR touches Auth (78% covered) and Billing (45% covered)”yes(feature- & flow-scoped)partial(file-level summaries)nono
MCP-native for AI agentsread-only tools an AI agent calls directlyyes(11 tools)noMCP server(beta)
Runs locally, no source uploadyes(CLI; dashboard sees results only)SaaS(PR diffs sent)on-prem tieron-prem tier
Open-source CLIMITnoOSS server(Apache 2.0)no
Total cost — 10-dev teamhow the per-org vs per-seat math actually pencils out$29/mo(flat per-org)$120–240/mo($12–24 × 10)$190–490/mo($19–49 × 10)$190–360/mo(per author)
Side-by-side vs the tools you already use
Privacy & security · by default

We hold ciphertext and a wrapped key.
Never your code.

A short-lived worker clones, scans, encrypts the feature map with AES-256-GCM, then wipes itself. The master key never leaves AWS KMS — not even for us.

Full security & subprocessors
Encrypted feature map
AES-256-GCM in our database
Wrapped data key
scoped to {org, scan} — useless alone
AWS KMS master key
hardware-backed, can't export
Never stored
your source code, diffs, or plaintext map

Map your codebase.
Know what to refactor next.

Scan your first repo in minutes — feature map, risk hotspots, coverage by flow. Free tier · runs locally · OSS.

Try free