TRUSTED AI · AI GOVERNANCE

ScaleQuality. AI across your business. Governance in every decision.

One workspace for multiple models, data protection and governance connected to your code, agents and compliance. From the first conversation to evidence you can act on.

AI Workspace · Code Intelligence · Agents · Compliance

Illustrative platform view. Figures demonstrate the workflow.

Trusted by engineering leaders at

iConnectionsAB InBevATEK GroupBemol DigitaliConnectionsAB InBevATEK GroupBemol Digital

AI WORKSPACE · CHAT & CODE

Chat and code with the models you want.The governance your business needs.

Chat, create and analyze in one workspace. Switch to Code and it works on your repositories: it reads the project, runs the tests and opens the pull request. Families such as GPT, Claude, Gemini and DeepSeek, with protection rules and visibility into usage.

  • Code with SQ Auto or the model you pick, Max Mode for the hard problems, in the ScaleQuality cloud or on your computer.New
  • Rules to detect, redact and block data in supported modes.
  • Costs, access and limits in the context of your operation, for every conversation and every change.
Talk to a specialist
ScaleQuality / AI Workspace
One catalog. Many possibilities.
Where should we start?
payments-apimainAccept editsScaleQuality cloud
Product team

Summarize this contract. Contact: ana@example.com[EMAIL_REDACTED]

Email protected before sending
SQ Auto

I can organize the clauses, deadlines and points that need review.

Payments team · payments-api

Add rate limiting to the payments API and cover it with tests.

3 s · Reading the code…
Pull request #128 openedEdited 2 files · tests passedView PR

Describe what you need…

Choose a model
Highlights from a broader catalog
Data protectionSQ AutoUsage recordedTeam limit38%
Illustrative conversation. In Code, changes go to a pull request and the cost lands on the team that asked.

Connections

Connect where AI already runs in your company.

Providers, clouds and gateways your teams already pay for. Cost, usage and the rules that apply are measured in one place, from each account's own reports.

AnthropicOpenAIGoogle CloudAWSAzureDatabricksSnowflakeCloudflareOpenRouterLiteLLMPortkeyKongCursorAnthropicOpenAIGoogle CloudAWSAzureDatabricksSnowflakeCloudflareOpenRouterLiteLLMPortkeyKongCursor

CODE INTELLIGENCE

Can you trust your code?See the evidence behind the answer.

A diagnosis of code maturity, risks and AI durability. See what holds your project back, follow each finding to its evidence and measure progress.

  • Findings tied to the file, line and measured revision.
  • Continuous measurement when integrated into your pipeline.
  • Priorities that can become corrective actions with agents.
Talk to a specialist
Illustrative project · diagnosis

Can I trust this code?

main · a1b2c3d
Code maturity
53/ 100Level 2 · Initial12 open risks
+6 pts
AI Durability87%

of the code AI wrote survives

13% reworked
Continuous measurement · CI pipelineCommitb4c5d6eCIMeasureUpdate diagnosis
Illustrative cycle with sample data: commit, pipeline measurement and updated diagnosis.

LEGACY & MAINFRAME

Measure the mainframe you run.Modernize without losing control.

The diagnosis reads COBOL, copybooks, JCL, CICS and BMS straight from the source, without running anything. It shows what starts each flow, what depends on what and where to start migrating, with a maturity score built for the mainframe.

  • Programs, jobs, transactions and screens mapped, with entry points and the calls between programs.
  • Maturity scored by the SQCM Mainframe profile, calibrated on public COBOL systems.New
  • Parity at every measurement: each change to the COBOL followed until it reaches the new system.
Talk to a specialist
Illustrative system · Legacy map

What exists on the mainframe

Read from the code, without running anything: what starts each flow, what depends on what, and where to migrate from.

card-payments · main
48COBOL programs
112copybooks
36JCL jobs
21CICS transactions
17screens (BMS)
64/ 100Level 3 · StructuredSQCM Mainframe 1.0
Migration strategy
Wave 1 · independent14 · 9,820 lines
Wave 2 · connected22 · 21,400 lines
Wave 3 · core12 · 18,950 lines
Parity since the migration started9 to repeat31 repeated4 not applicable
Illustrative system with sample figures. The map, the waves and the parity come from the reading of the source.
MCP · INSIDE YOUR CODING TOOL

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.
  • The models can come from here too: point your coding tool at SQ Auto and every call follows the company's rules and limits.
Talk to a specialist

WORKS WITH

Claude CodeCodexCursorVS CodeGemini CLIWindsurf
~/payments-api · claude

Illustrative example. Results depend on the organization's access and plan.

AGENTS

Intelligence finds it.Agents move the work forward.

Specialist agents work on demand or autonomously within the scope you authorize. From a finding to a proposed fix, with validation and traceability.

  • Autonomy within repositories and limits you define.
  • Changes linked to findings and validation results.
  • PR follow-up reviews your team’s pull requests and answers in them.New
  • ScaleQuality does not merge pull requests.
Talk to a specialist
Measured findingScope and limits
SecurityFixes vulnerabilities and exposed credentials
DependenciesUpdates vulnerable libraries and verifies the fix
LicensesBrings packages back within your license policy
Quality & testsExpands tests with verified results
ReliabilityFixes reproducible defects against your tests
PR follow-upReviews your team’s PRs and answers in them
Validate the changeProof attached to the PRPR / MR for your team

Your team controls approval and merge in your provider.

Illustrative workflow. Each agent follows its policy and available evidence.
COMPLIANCE

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

Export full dossier

1

Met

5

Partial

0

Missing

3

Manual

Controls with something pending · 5 of 9

PartialLGPD-01 · Art. 46

No findings matching the specified scanners, domains and severity filter

1 of 18 measured projects without matching findings · 126 open findings in the rest

PR #3 open· Awaiting review and approval
Remediate with agent

Illustrative example. Names and numbers are not from a real organization.

  1. 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.

  2. 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.

  3. 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.

LGPDGDPRSOC 2EU AI ActDORAPCI DSSNIS2HIPAAISO 27701CFM 2.454/2026
Explore the regulations
CONTROLS AND TRUST

Confidence to operate. Evidence to decide.

Encrypted historyOrganization isolationProtection rulesEnterprise SSORole-based accessAudit trail

Policies, controls and evidence connected to AI operations.

Explore how messages and code are processed, which providers are involved and the boundaries of each mode in our Trust Center.

SQCM v3.0 · published, citable, with DOI.

The published methodology describes the five domains, where evidence comes from, and when scores can be compared. It also covers the Mainframe profile for COBOL systems and license evidence, which stays outside the score.

Read the white paper
AWS Activate
Verified startup · cloud infrastructure
Rock New Ventures
Batch 10 ·Demo Day standout
Read the full Trust Center
ENGINEERING MATURITY MODEL

A playbook for your next level of engineering.

From diagnosis to the methodology’s Elite level: a path through 12 domains, practices and verifiable gates. Prioritize work that reduces rework, improves operational efficiency and frees capacity to generate revenue.

Level 1Reactive
5 / 24
Domain score
0 / 30
Gates met

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.

Explore the playbook with a specialist
THE NEXT STEP

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.