Hardened AI-SDLC · Sebastian Szczepański

Compliance as a property of the pipeline.

I install developer automation that lets AI-assisted work move fast and still arrive compliant with your specifications, coding standards and security policy. Twenty years of architecture behind every gate — and I still write the code.

Email me your bottleneck consulting or hands-on · Poland, EU
—playing

How a change moves through the pipeline

  1. Change request Anything that wants to become code: a ticket, a pull request, an agent task. intake bot
  2. Definition of ready Nothing enters without context, constraints and a testable outcome. definition-of-ready.yml
  3. Acceptance criteria Criteria a machine can execute and later prove. spec templates + traceability map
  4. Standards as code Your architecture rules and conventions, enforced by linters, generators and agent instructions. standards ruleset + agent instructions
  5. AI implementation Agents work inside the constraints, with scoped context and a bounded blast radius. agent workflows in your repo
  6. Verification Every acceptance criterion mapped to evidence before a human looks. CI verification stage
  7. Security guardrails Secrets, dependencies, data flow and least privilege — checked continuously. policy pack + audit trail
  8. Evidence report One report per change: which criteria, which checks, which proof. evidence-report.md on every PR
  9. Human review Reviewers spend attention on the two decisions that need a human, not the boilerplate. review rubric
  10. Production Shipped with a trail an auditor can follow. release with provenance

Six gates. Six artifacts your team keeps.

installed in your repos and your CI

01 · intake

Definition of ready

Nothing enters without context, constraints and a testable outcome.

definition-of-ready.yml + intake bot

02 · intent

Acceptance criteria

Written so a machine can execute them — and later prove they were met.

spec templates + traceability map

03 · constraints

Standards as code

Architecture rules and conventions become linters, generators and agent instructions.

standards ruleset + agent instructions

04 · execution

AI implementation

Agents work inside the constraints, with scoped context and a bounded blast radius.

agent workflows in your repo

05 · proof

Verification

Every criterion mapped to evidence before a human looks. Reviewers judge; they don't proofread.

CI verification stage + evidence report

06 · assurance

Security guardrails

Secrets, dependencies, data flow and least privilege enforced continuously, with an audit trail.

policy pack + audit trail

Proof

−38%

PR cycle time — fintech scaleup, 40 engineers, gates 01–03

100%

criteria traced to evidence — regulated healthcare platform

3×

throughput per reviewer once boilerplate review became automated

20 yrs

developer → architect → director of engineering. Still writing code.

50+

delivery projects — fintech, healthcare, logistics, public sector

4

continents of teams led remotely — in PL, EN and ES

Three ways to start.

Diagnostic

I trace your lifecycle and rank where AI is leaking value.

2 weeks
fixed fee

Install most common

We build the six gates in your repos and CI. Your engineers learn it by shipping with it.

6–12 weeks
with your team

Embedded

Fractional architect and AI engineering lead. I hold direction and write the hard code.

ongoing
2–3 days/week

Sebastianfounder, s18i

Send me one paragraph about your pipeline.

I'll tell you honestly whether it's a two-week diagnostic, a three-month install, or something you can fix yourselves.

[email protected] →