Speed without control isn’t acceleration.
It’s risk.
The faster software gets produced, the more systematic its verification has to be. These are the practices we use to hold that line on every project.
- How does Ciancoders ensure the quality of the software it delivers?
- Through a set of practices that are part of Cian-OS and apply to every project: human review of every change, automated testing, static analysis, dependency and secrets management, data protection, threat modeling, a production-readiness checklist, observability and incident response. We don’t publish certifications we don’t hold.
Ten practices. On every project.
- 01
Human code review
No change — written by a person or an AI agent — reaches the main branch without an engineer reviewing it and being able to explain it.
- 02
Automated testing
Unit and integration tests on critical flows, run on every change before merging.
- 03
Static analysis
Linters, typing and code analysis in continuous integration to catch errors before anything runs.
- 04
Dependency management
A dependency inventory, known-vulnerability checks and planned updates.
- 05
Secrets
Keys and credentials kept out of the code, in environment variables or secrets managers, rotated when appropriate.
- 06
Data protection
Permissions validated on the server, encryption in transit and at rest, and minimization of personal data.
- 07
Threat modeling
In Foundation we identify what can go wrong, who could cause it and how it’s mitigated — before building.
- 08
Production readiness
An explicit checklist — deployment, backups, monitoring, documentation — that defines when something is done.
- 09
Observability
Logs, metrics and alerts designed so an engineer finds out before the user does.
- 10
Incident response
A defined procedure: who responds, how fast, how it’s communicated and what gets learned afterward.
Rules for the agents, not only for the people.
The context AI agents receive — conventions, constraints, decisions — is written into the repository and versioned like any other asset.
Agents work on bounded tasks with explicit limits: what they may touch, what they may not, and what every change has to include (tests, documentation).
No sensitive client data is used to train models. The AI tools we use operate under professional-use agreements.
What we don’t claim.
We don’t publish SOC 2, ISO 27001, cloud or security certifications we haven’t verified we hold. If your project requires a specific certification, we discuss it before starting and design the project to meet it.
External evidence
Public sources supporting this page’s general statements about engineering, security and AI. Cian-OS is Ciancoders’ own methodology; these sources neither endorse nor certify it.
- DORA · Research (Google Cloud)Public research on software-delivery performance and AI adoption in engineering teams.
- NIST SP 800-218 · Secure Software Development FrameworkSecure-development practice framework: review, analysis and vulnerability management.
- OWASP Top 10 for Large Language Model ApplicationsRisks specific to applications that integrate language models.
- NIST AI Risk Management FrameworkReference for governing risk in AI-enabled systems.
Questions about quality and security
Do you sign NDAs?+
Yes — before we see any of your code or business data.
Where do my code and data live?+
In your repository and your infrastructure. Ciancoders works with controlled access you can revoke at any time.
Do you use my data to train AI models?+
No. The AI tools we use operate under professional-use agreements that exclude training on client data.
Can you audit the security of an existing application?+
Yes. It’s one of the eight dimensions of the Production Readiness Assessment.
Control and speed don’t compete.
They’re designed together.
Tell us what you're trying to solve. We'll help you decide whether to build, rescue, extend your team — or take a different path.
30 minutes · No commitment · English or Spanish