Trusted by engineering leaders at
AI shipped. Governance didn't.
Teams build with AI faster than ever. What's missing is the layer that proves it. Without it, leaders pay four silent costs:
AI cost is invoiced, not understood
Provider bills land monthly. Nobody knows which team consumed what, or whether spend tracked with output.
Durability is a hope, not a measurement
The AI code compiles and ships. Whether it lasts or quietly decays into rework is something teams feel, not measure.
Deploy readiness is an opinion, not a call
"Are we ready to ship?" gets answered in the standup, not with data.
A board answer you can't defend
When leadership asks if AI made engineering better, "it feels faster" is not evidence.
ScaleQuality closes the loop: software and AI scaling with confidence, without automating the chaos.
From finding to pull request.
Specialist agents take the gaps the measurement found and turn them into pull requests your team reviews. On demand, or autonomously inside the scope you set.
- Every delivery is a pull request with the measurement before and after. Nothing merges on its own.
- Autonomous agents act only on the repositories, findings and monthly limits you configure.
- When nothing safe can ship, the agent says so. No change made, nothing shipped.
Five autonomous agents, one permission system.
Each one declares when it acts, where it acts and how many runs per month. You switch it on per repository.
The work, in one place
In progress first, then what needs your attention.
Test coverage (verified unit tests) · target ≥ 80
payments-api · On demand · Critical
RunningDetected typescript + jest · installing dependencies…
Step 2 of 6
00:18Expand verified tests · measured coverage 61.9%
ai-governance-service · Autonomous · Critical
Waiting for reviewPull request #16 ready
Coverage 62% to 81%
12 hExpand verified tests · measured coverage 20.2%
insights-service · Autonomous · Critical
AcceptedPull request #5 merged
Merged by your team
1 hSupply chain fix (upgrade a vulnerable dependency) · 32 advisories found by the diagnosis
front-end-tests · Autonomous
Not deliveredNo change made, nothing shipped
No execution credit was reserved.
15 h
Illustrative example. Names and numbers are not from a real organization.
ScaleQuality in your coding assistant.Measure, dispatch and follow up without leaving the terminal.
Connect the ScaleQuality MCP to Claude Code, Codex, Cursor or VS Code. Your assistant reads the real measurement of the repository, points at what weighs on the score and dispatches a specialist agent with your plan's credit.
- Real measurement of the connected branch, never the model's estimate.
- The agent opens a pull request with evidence. It never merges.
- Plan usage visible before dispatching. No surprises.
WORKS WITH
Claude Code
Codex
Cursor
VS Code
Gemini CLI
WindsurfIllustrative example. Results depend on the organization's access and plan.
From obligation to pull request.
Controls measured from the code, corrective actions dispatched to an agent, and evidence exported with a manifest. Regulation stops being a spreadsheet.
Regulation or framework
LGPD (Law 13,709/2018), arts. 46 to 49
pack v1.1 · 90-day window · BR
1
Met
5
Partial
0
Missing
3
Manual
Controls with something pending · 5 of 9
No findings matching the specified scanners, domains and severity filter
1 of 18 measured projects without matching findings · 126 open findings in the rest
Illustrative example. Names and numbers are not from a real organization.
01
Controls measured from code
Each control reads the measurement: findings, scanners, coverage and supply chain. Met, partial, missing or manual, with the projects that fail it.
02
Remediate with agent
A pending control dispatches a specialist agent. The pull request stays linked to the control and to the corrective action until your team reviews it.
03
Evidence you can hand over
SBOM per repository, diagnosis reports, AI governance export and a full dossier with a SHA-256 manifest. One observation window, one download.
24 references, scope declared one by one.
Each reference separates what is already implemented from what is being expanded. Being in the catalog is not a certification, and a merged pull request does not mark a control as met on its own.
One platform, three modules.
Start with a diagnosis of any repo. Prove what AI costs and returns. Keep it durable at scale.
Point any repo, get a verdict
AI-generated or decades-old: a zero-config verdict on code maturity and how durable what AI wrote really is. One run reads the code, the dependencies you ship, the infrastructure that runs it and the image you build. In minutes.
What you get
SAST, secrets, dependencies, IaC and containers · AI-code durability
Decide with AI, prove the return
The projected return before you build. The real cost, measured from your providers, as it runs. Never declared.
What you get
Cost preview · Projected ROI · Real AI cost, measured continuously
Keep it durable at scale
Maturity across 12 domains and 30+ gates, production readiness as one executive call, and governed agents that open the PR to close each gap.
What you get
12 domains · Production readiness · Governed agents that ship the fix
Works with the stack you already use
The three questions every leader asks about their AI.
Cost, trust, exposure, measured per team, never per person.
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
Durability is the number nobody else has: how much of what AI wrote actually survives, and how much quietly became rework you paid for. No market standard covers it. ScaleQuality measures it from your own git history.
Built secure. Backed by a published methodology.
Trusted AI is not born from policies. It is born from continuous measurement and evidence from your environment.
ScaleQuality measures your teams and systems, never your developers, never the content of what anyone writes.
SQCM v2.1 · published, citable, with DOI.
The published methodology describes the five domains, where evidence comes from, and when scores can be compared.
Read the white paperYour engineering true north, measured across 12 domains.
Elite engineering is method, not luck. We curate the domains with periodic research and turn the standard into a path: less rework, more foundation to accelerate with AI.
Cycling through the levels. Click a level to explore it.
Living curation
Domains and gates are revised with periodic market research. Your standard tracks the state of the art, not the date it was written.
Levels can't be bought
A level is the lower of the domain score and the gates met. One strong domain never pays for a missing foundation.
Declared against measured
What the team declares is checked against evidence from your environment. Where evidence disagrees, the domain drops to the floor.
One platform.Two ways to move forward.
Led by your team, or implemented alongside our specialists. Progress measured by ScaleQuality.
ScaleQuality Platform
Your team leads.
Diagnosis, priorities and continuous measurement. Your team uses the platform to decide and implement improvements.
ScaleQuality Managed
We implement with you.
90 days with dedicated specialists, a defined scope and measurable progress in your environment.
Then your team continues with ScaleQuality Enterprise.
Special programs