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Built for engineering leaders who own outcomes.
AI shipped. Governance didn't.
Engineering already adopted AI. What's missing is the layer that turns its real signals (cost, attribution, risk, deploy readiness) into decisions leadership can defend.
Without it, leaders pay four silent costs:
AI cost is invoiced, not understood
Provider bills land monthly. Nobody knows which team consumed what, which agents drove which PRs, or whether spend tracked with output.
Quality stays a feeling, not a measurement
Teams self-report maturity. Leaders rely on intuition. Real signals from SonarCloud, Jira and the boards never reach the executive call.
Deploy readiness is an opinion, not a call
Critical bugs, incidents and hotfixes live in dashboards nobody reads. "Are we ready to ship?" gets answered in the standup, not with data.
Governance fragments across teams and tools
Each BU runs its own standard, its own AI providers, its own agents. No org-wide visibility, no way to scale governance, and no way to defend the decision in front of the board.
ScaleQuality closes the loop.
Maturity is the spine of the loop.
ScaleQuality scores where your engineering stands today. Then the loop moves you up, level by level.
12 domains · 30+ gates · 5 levels of maturity
The three questions every leader asks about their AI.
Cost, trust, exposure: measured per team, never per person. And when there's a gap, an agent opens the pull request to close it.
Am I getting my money's worth?
$19.6K
of your AI spend became code that stuck
Can I trust the code AI writes?
High
governed PRs · human-reviewed before merge
Is there shadow AI in my codebase?
2 repos
ungoverned AI detected
Gap detected? The loop closes it, and it's governed.
Dispatch
Agent · Code Health
Governed PR
PR #42 · never auto-merged
Proof
coverage 71% → 88% · durable
A fleet of governed agents
Three fronts, measured continuously.
Quality, deploy readiness and AI governance, connected to the same engineering signals, evaluated against the same evidence, and surfaced in the same boardroom-ready view.
Quality gates backed by real signals
Maturity measured against 30+ quality gates and the engineering signals your tools already emit. Every gap is translated into projected financial impact and a defensible priority for leadership and the board.
What you get
Evidence-based scoring · ROI projection · Aligned / Mismatch validation via SonarCloud
An objective deploy-readiness call
Know if a team or release is ready to ship, with auditable confidence based on connected data. 8 criteria across quality, board signals, risk and confidence converge into one executive call.
What you get
Ready / Ready with warnings / Not ready · 8 criteria · critical bugs, incidents and hotfixes from the board
Govern AI adoption without scaling risk
Measure if AI adoption is improving quality, or quietly raising cost, complexity and risk. Real cost via provider admin APIs, bottom-up attribution per team, and per-agent governance with autonomy, human review and guardrails.
What you get
Real cost · Bottom-up attribution · Per-agent autonomy, review % and guardrails
Connected to GitHub, GitLab, Bitbucket, Azure Repos, Jira, Azure Boards, SonarCloud, Anthropic and Cursor. Linear, Asana and Businessmap are next.
Connect. Measure. Close the gap. Prove.
Four steps, then it runs again. Every pass measures real signals, closes a real gap, and proves the impact in your own code.
Connect
Plug your repos, boards, SonarCloud and AI provider admin keys (Anthropic, Cursor). One admin key per team, bottom-up, isolated, encrypted at rest.
Measure
Maturity scored across 12 domains and 30+ gates. Production readiness against 8 criteria. AI cost, tokens and code durability captured continuously from real signals, never estimates.
Close the gap
When a gate fails, an expert agent is dispatched. It clones the repo, writes the fix, and opens a governed pull request. Human review always required, nothing auto-merges.
Prove
The agent re-measures. Coverage rises, the gate flips to met, maturity moves up. The impact is proven in your own code, not a number you typed in.
Then it measures again. The loop does not end at a dashboard.
Guided Demo
See ScaleQuality operating with your engineering data
Our team will walk you through a personalized demo tailored to your organization's engineering challenges.
Built secure from day one.
ScaleQuality measures your team, never your developers. Zero code retention, and our AI never trains on your code. Production-grade security on every layer, every action auditable.
Security Infrastructure
Fully implemented · Production ready
What you'll see in your first 7 days.
No setup theater. Real value from day one.
Start Free TrialDon't spend your engineering rebuilding AI governance.
Building the first dashboard is easy. Maintaining attribution, AI cost intelligence, code durability, risk models, agent guardrails, PR governance and audit trails is the real cost.
Build it in-house
- Your team builds and maintains the integrations, metrics, rules and dashboards
- Internal token and agent cost grows without predictability
- Metrics become BI without a methodology behind them
- Internal agents need prompting, evaluation and governance
- Hard to prove whether AI created value or just rework
With ScaleQuality
- A ready layer connected to the stack you already use
- An outcome-oriented model, with per-agent guardrails and caps
- Metrics tied to quality, risk, readiness and ROI
- Expert agents with a governed flow and evidence in every PR
- Measures durability, rework, shadow AI and trust
Build it, and you pull engineering off the core to maintain a layer ScaleQuality already ships as its core.
Keeping it true is a product, not a project. That product is ScaleQuality.
The question isn't whether you can build it.It's what it costs to keep flying blind.
Shadow AI you can't price
AI is already writing code in repos no one governs. You can't manage a risk you can't even see.
Rework you already pay for
A share of what your AI ships gets reverted or rewritten. That waste is on your invoice whether you measure it or not.
Decay you meet in production
Speed hides slow erosion. Without durability data, the bill arrives as incidents and on-call nights.
A board answer you can't defend
When leadership asks if AI made engineering better, 'it feels faster' is not evidence.
ScaleQuality turns each of these into a number from your own code. The quality intelligence layer for AI-driven engineering.
Simple, transparent pricing
Start with a 7-day free trial. No credit card required.
Team
Discovery: essentials only
- Evidence-based Quality Gates (12 domains, 30+ gates)
- ROI projection from technical gaps
- Source control, board and SonarCloud integrations
- Multi-Source Intelligence Engine™
- AI Advisor (50 queries/mo)
- 1 Business Unit · up to 3 Teams
Business
Operating quality at scale
- Everything in Team
- AI Footprint: every AI tool's cost, beyond just code
- Release Risk Intelligence (SonarCloud + financial impact)
- Tech Radar with ORG → BU → Team inheritance
- Execution Layer: drag & drop Gantt with Jira sync
- 1 Quality Agent: AI Policy enforcement
- Scaffolding automation
- Unlimited AI Advisor
Enterprise
Complete governance layer
Custom
- Everything in Business
- Full AI Engineering Governance (cost · efficiency · risk lenses)
- Autonomous Quality Agents (dispatch → PR, governed)
- Production Readiness (8 criteria, Ready / Warnings / Not ready)
- Bottom-up AI Attribution (real cost per team via repo activity)
- Multi-provider AI + BU and Org rollups
- SSO/SAML · MFA · RBAC · Audit log
- Dedicated support · priority integrations
Need more Business Units? Add extras for $29/BU/mo.
Not sure which plan is right for your Org?
What powers ScaleQuality
AI Engineering Governance
- Real cost, tokens and activity from Anthropic and Cursor admin APIs
- Per-agent autonomy, human-review rate and guardrails
- Three lenses on AI adoption: cost, efficiency and risk
Bottom-up Attribution
- Team's AI cost computed from actual repo activity share
- Workspace and bot-user mapping per agent
- No top-down allocation, no surveillance of individuals
Production Readiness
- Ready / Ready with warnings / Not ready / Insufficient data
- 8 criteria across quality, board signals, risk and confidence
- Critical bugs, incidents and hotfixes from Jira & Azure Boards
Multi-Source Intelligence Engine™
- Grounded in CISQ, McKinsey, Stripe, DORA & Octoverse
- Financial engine recalculates ROI continuously
- Powers every recommendation, score and insight
Evidence-Based Maturity
- 12 domains, 30+ quality gates
- Validated against connected engineering signals
- Aligned / Mismatch detection via SonarCloud
Risk Intelligence
- Risk Exposure scoring (security, reliability, coverage)
- Release guardrails with financial impact estimation
- Business Risk Traceability via tracker items
Execution Layer
- Drag & drop Gantt timeline with Jira sync
- AI-generated epics & stories from maturity gaps
- Roadmap that lives outside slides
Governance Layer
- Tech Radar with ORG → BU → Team inheritance
- Standards that evolve with maturity
- BU and Org rollups across every team
Integration Hub
- GitHub · GitLab · Bitbucket · Azure Repos · Jira · Azure Boards · SonarCloud
- Anthropic · Cursor · Windsurf and Antigravity next
- Linear · Businessmap · Asana shipping in the next release