Customers
Three engagements that shaped the platform.
We don't publish customer names or logos. We describe the work and what it changed, without numbers we can't publish.
Engagement 01 · low-code platform vendor
Test automation for a whole partner ecosystem.
Before Sahasa.AI was a product, our team built and ran the test automation platform for a leading low-code vendor's partner ecosystem. It recorded and generated tests for the vendor's interfaces, process models and integrations, ran them across environments and reported to release owners.
It taught us how a QE platform gets adopted inside a large ecosystem, and how it gets kept alive after the launch.
What changed
- Test authoring moved from specialist engineers to the wider delivery team
- Regression became a routine gate on every release instead of a project
- We came away with the model we still use: a platform, plus the people who run it inside the customer
Engagement 02 · low-code product company · live
Faster releases and a shorter security assessment.
A fast-moving low-code product company uses Sahasa.AI day to day: requirements to test cases to runnable suites, with bug and security analysis on every change. Its engineers use different AI coding assistants, and the same checks apply to all of them.
What changed
- A quicker path from feature idea to tested release, in the team's own frameworks
- Code-level security findings and the threat model are handled before the pen-test, so the assessment concentrates on what's left
- Leadership reads each result directly as measured, generated only or N/A
Engagement 03 · large Canadian retailer
A platform shaped to their own quality model.
A large retailer with an established quality organisation and its own standards. We learned their expectations and existing modules first, then fitted the platform to them, with AI-led engineering across requirements, test generation, security and traceability, in the terms they already used.
What changed
- Their taxonomy, gates and dashboards, shipped as configuration rather than a fork
- Traceability across many teams and repositories
- Security and quality signals on every change, inside their own environment
Who uses it
Each role keeps its job. The platform does the mechanical part.
Developers
Unit and integration tests, bug findings with fixes, blast radius and root cause from a stack trace, each cited to file and line.
WEB APP · IDE PLUGINS NEXT
Automation engineers
Runnable suites in their framework, contract tests, live browser runs, failure triage and suite clean-up.
REPO · CI REPORTS
Security engineers
SAST, DAST, dependency CVEs, secrets and a threat model checked against real findings.
THREAT REGISTER · CWE MAPPING
Manual QA and analysts
Testability scores, scenario maps, prioritised cases and plans from requirements, with no code involved.
BRD · JIRA EXPORT · EXCEL RTM
Engineering leadership
One dashboard per product: quality, security, risk and coverage, each measured or marked N/A.
DASHBOARD · MONDAY REVIEW
AI product teams
Evals, red teaming, A/B testing, safety, drift alerts and cost for the AI features in the product, through Vigil.
VIGIL
Who it's for
Engineering organisations that own their code.
Built for organisations of roughly 200 to 5,000 engineers that already use AI coding assistants and have security or compliance obligations, and for the VPs of Engineering, Heads of Quality Engineering and CISOs who answer for what ships.
Regulated enterprises
Banking, insurance, healthcare and retail. Requirement-to-commit traceability and security evidence in one place, inside your boundary.
SELF-HOSTED · TRACEABILITY · SSO NEXT
Software and platform companies
SaaS, low-code and product teams shipping weekly. Findings cited to file and line, tests in your frameworks, no proprietary format.
GITHUB · GITLAB AND BITBUCKET IN BUILD
Global capability centres
Large QE teams in India moving to AI-led testing for US and European enterprises.
EMBEDDED QE TEAM · YOUR TIME ZONE
Teams shipping AI features
Any product with a chatbot, copilot or agent inside it. Vigil tests those features on every prompt or model change.
EVALS · RED TEAMING · DRIFT ALERTS · FINOPS
Next step
Bring one repository.
We'll walk you through Sahasa.AI on a sample project and talk through how it would fit your stack. If it looks useful, we can scope a pilot together.
- 01A short call. Your stack, your frameworks, and where quality hurts today.
- 02A demo. The platform on a sample project, and how it would connect to your tools.
- 03A pilot, if it fits. Scoped with you around one service.