← Adam Yassine

Making AI-generated compliance documents safe to trust

Templating Trust: An AI Policy Generator for ISO and SOC Compliance

Software Engineer · Compliance tooling · Jul 2025 – Aug 2025 · Lean product + engineering team

  • AI Compliance
  • Workflow Automation
  • Document AI

Context

This product turned a compliance workflow into an AI-assisted system. The idea was simple in principle: if a company needed an ISO or SOC policy document, the product should generate a structured draft instead of forcing teams through manual copy, formatting, and review cycles.

The problem

Policy documents are not just documents; they are trust artifacts. A generated draft is not useful if it feels generic, inconsistent, or weakly grounded in the actual control requirements. The product needed a system that could produce quality output while still being auditable and usable by teams who needed confidence in the result.

What I did

  • Built the AI generation path for compliance documents, turning reusable policy requirements into structured output.
  • Helped design the trust layer that made generated content more reliable, including grounding and document structure rather than open-ended freeform generation.
  • Improved the document workflow so teams could move from raw requirements to a usable draft without manual rewriting.
  • Worked on the engineering side so the product behaved like a useful tool, not just a prompt demo.

The decision I’d defend

I focused on the system being document-first and trust-first. The product was a document. I built the AI system that could be trusted to write it.

Outcome

The project reinforced a core product lesson: AI is most useful when it reduces operational friction without sacrificing confidence. For compliance work, the real value came from turning a high-friction document process into a repeatable system a team could rely on.

What I'd do differently

I would have pushed earlier on content validation and review loops. For a trust-heavy product, the biggest quality gains come not from a flashier model, but from making the generated output easier to check, revise, and defend.