Automate AI Solutions

Automate AI Solutions

We automate AI-powered solutions end-to-end where it adds value: from data prep and prompts to deployment and monitoring. Automation is scripted, versioned, and integrated into our CI/CD and delivery cycles.

Pipelines

Automated runs for training, evaluation, or inference where applicable. We avoid manual, one-off runs; repeatability is key.

Example: a minimal pipeline step that runs a “docs check” agent and fails the build if the check fails. The important part is that it’s in version control and runs the same way every time.

# .github/workflows/docs-check.yml (conceptual)
name: Docs check

on:
  pull_request:
    paths: ['content/docs/**']

jobs:
  check:
    runs-on: ubuntu-latest
    steps:
      - uses: actions/checkout@v4
      - name: Setup
        run: npm ci
      - name: Lint docs
        run: npm run lint:docs
      - name: Docs build
        run: npm run build
        env:
          SKIP_AGENT_DOCS_CHECK: 'false'

We use the same idea for other AI-assisted steps: script the step, run it in CI, and gate on success.

Quality gates

Automated checks (e.g. tests, lint, safety) run before deployment. AI-generated or -assisted outputs go through the same gates as human-written code.

Typical gates we use:

If the agent or AI-assisted code doesn’t pass, we fix the prompt or the code until it does. We don’t bypass gates for “AI-generated” code.

Documentation

Runbooks and docs so the team can operate and extend automation. We document:

• What the pipeline does and when it runs.
• How to run it locally or in a branch.
• Failure modes and how to fix common errors.
• Rollback or disable steps if something breaks.

See also