The intelligence and governance foundation to scale AI with confidence.

Models, coding assistants, agents and orchestrators evolve and get replaced all the time.ScaleQuality measures, governs and fixes the base that holds them all: your code, your teams and your AI adoption.

Trusted by leaders at
  • iConnections
  • AB InBev
  • ATEK Group
  • Bemol Digital

Make AI adoption measurable. Make engineering better.

Stop managing AI adoption, engineering quality, security and compliance in disconnected silos. Turn every signal into measurable progress, governed action and business impact.

Reduce waste. Improve ROI.

Reveal AI spending, engineering inefficiencies and costly rework. Connect investments to measurable outcomes.

ProofThe AI in your code

Scale AI without losing control.

Protect sensitive data, govern AI usage and expose Shadow AI risks across connected environments.

ProofAI Workspace

Build software that lasts.

Measure AI-generated code durability, security and maintainability. Reduce technical debt and recurring fixes.

ProofCode maturity

Continuously improve engineering maturity.

Turn real engineering signals into maturity assessments, prioritized roadmaps and measurable improvement.

ProofCode maturity

Go from insight to action.

Use governed agents to address gaps, propose fixes and verify results with human oversight.

ProofAgents

Prove trust. Simplify compliance.

Connect technical evidence to controls, risks and audit requirements. Know what's missing and what changed.

ProofGovernance and compliance

The AI in your code What stays, and whose AI wrote it?

Read from the commit history of every repository, with no survey. A commit counts as AI when the tool signed it.

Illustrative dataRead from the commit history over the last 90 days

Code maturity Can you trust the code you ship?

One reading of the repository, no questionnaire. Security, reliability, maintainability, supply chain, AI durability and the test coverage your suite really reaches become one score by a published model. Each finding points to the file, the line and the revision, and an agent opens the pull request that closes it.

What the reading runs

Static analysissrc/**
Secrets, history includedevery commit
Dependencies and licensespackage-lock.json
Infrastructure as codeinfra/*.tf · Dockerfile
Your tests, executednpm test --coverage
Git history, 90 dayswho wrote what, what stayed

Test coverage is holding the score back

Illustrative score, by SQCM v3.0, a published methodRead the paper →

What to fix, and where

CriticalSupply chain

lodash 4.17.20 allows command injection (CVE-2021-23337)

package-lock.json · rev 9f3c2e1
Fix with an agent+2 to the score
HighSecurity

An API key is still in the git history

config/payments.ts:12 · rev 4b7a0d2
Fix with an agent
MediumReliability

A retry loop swallows the error it retries

src/billing/retry.ts:88 · rev 4b7a0d2
Fix with an agent
Holding the scoreTest coverage

31% of the lines run under your tests

measured by running your suite · 412 of 1,329 lines
Write tests with an agent
The architecture it found
Interfacesrc/api
Applicationsrc/use-cases
Domainsrc/domain
Infrastructuresrc/infra
LayeredTests in CITypedLintConventional commits, 64%
Licenses, against your policy
765Allowed
2To review
0Forbidden
Read from every lockfile, with an SBOM in CycloneDX and SPDX.
Measured again on every change
JulAugSepOct
$ npx @scalequality/check
measuring…

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.

Agents Who fixes what the reading finds?

Specialist agents work on demand or on their own, inside the scope you authorize. Each one opens a pull request with the proof attached. Your team approves and merges.

From a finding to a pull request

Illustration. Each agent follows its policy and the evidence available

MCP Does your coding assistant know how the code is doing?

Connect the ScaleQuality MCP and your assistant reads the real measurement of the repository, points at what weighs on the score and dispatches a specialist agent. Never the model's estimate.

Works with

    Inside your coding assistant

    Illustration. Results depend on the organization's access and plan

    AI Workspace Chat and Code with more than 500 models, under your company's rules.

    Your teams pick the model for each task, from Anthropic, OpenAI, Google, Meta, Mistral, DeepSeek and dozens of other makers. Every call passes through your AI Protection before it reaches the model, and what it costs lands on the team and the person who made it. Code works in the cloud or on the developer's own computer, and your company's own provider keys work too.

    Where your teams work

    ChatAna R. · Payments

    CPF hidden before sending

    Here is the renewal summary for 123.456.789-09, with a draft reply below.
    CodeCloudThis computer
    Fix the retry loop in src/billing/retry.ts and open the pull request.
    1. Read src/billing/retry.ts
    2. Edited 2 files
    3. Tests: 48 of 48 passed
    4. Pull request #421 opened

    Your rules, on every call

    Sensitive dataapplied
    Attacks on the modelapplied
    Secure development in Codeapplied
    Your own protectionapplied

    What it costs, and who

    Payments team · October
    Illustrative dataMarks belong to their owners and identify the models they make

    +12 protections ready to turn on, and the ones you create

    • Sensitive data
    • Attacks on the model
    • AI governance
    • Brazilian AI bill (PL 2338)Bill
    • AI in research with human participants
    • Secure development in Code
    • Acceptable use
    • Sensitive personal data
    • Financial sector
    • AI in medical practice
    • Data of EU residents
    • Grounded answers
    • Your own protection

    Each one is a set of rules your company turns on. Your own combines the platform's rules with terms and formats you define.

    Governance and compliance Can you prove it to an auditor?

    Controls are read from the code, not from a questionnaire. An exception never turns green. The evidence leaves as a dossier, ready to hand over.

    A reading of six controls

    Illustration. Status is a reading of the evidence against the rule. Certification stays with auditors

    The references it reads from your code

    Artificial intelligence
    • ISO/IEC 42001
    • EU AI Act
    • NIST AI RMF 1.0
    Security and software
    • ISO/IEC 27001:2022
    • SOC 2 Trust Services Criteria
    • NIST SP 800-218 SSDF
    • EU Cyber Resilience Act
    Brazil
    • LGPD arts. 46 to 49
    • CMN Resolution 4,893/2021
    • CFM Resolution 2,454/2026
    • ANVISA RDC 657/2022
    • PL 2338/2023 bill

    Also in the catalog, with the scope of each one declaredGDPR, DORA, PCI DSS, NIS2, HIPAA, ISO 27701 and 14 more

    See the scope, reference by reference

    Confidence to operate. Evidence to decide.

    • 4published versions of the method, each with a DOI
    • 13providers, clouds and gateways measured
    • 90days following every line an AI signs
    • 30gates across 12 domains of engineering maturity
    • Encrypted history
    • Organization isolation
    • Protection rules
    • Enterprise SSO
    • Role-based access
    • Audit trail

    Read the full Trust CenterRead the white paper

    Part of the AWS and Datadog programs.

    Programs for companies that build on and connect to these platforms.

    • AWS ActivateAWS Activate
    • DatadogDatadog Partner Network

    AWS and Datadog marks belong to their owners.

    See it on your own code.

    Request a diagnosis. A specialist reads your repositories with you, on a call, and you leave with the first reading in hand.

    Or write to us on WhatsApp

    Trusted AI. Measured, governed and proven.

    Request a diagnosis

    A specialist reads your repositories with you, on a call.