AI Automation for Salesforce Delivery Teams
Practical AI across the Salesforce SDLC — pull-request review, spec-driven development, and backlog and documentation automation — with human-review gates and token budgets, not vibes. I build my own public tooling this way; a pilot brings the same discipline to your team.
When You Need This
- Leadership wants an AI plan, and all you have is slideware.
- Your senior developers spend their days reviewing routine changes.
- Test coverage is a tax nobody pays until deployment day.
- Individual developers tried AI tools ad hoc, and governance shut them down.
What a Pilot Can Automate
A pilot targets one of these workflows first — proven and measured before the next. Every one keeps a human approval gate; none of it ships unreviewed.
Cost-disciplined AI-assisted development
AI-assisted Apex and LWC work with explicit context and token budgets — the cost of AI-assisted delivery stays predictable and reviewable, not an open tab.
AI pull-request review
Automated first-pass review on every pull request — Apex, LWC, and metadata — surfacing risks so human reviewers spend their time where it matters. The human gate still approves.
Spec-driven development
The agent writes against an approved, written specification rather than a loose prompt, so what ships can be checked against what was agreed.
User-story templating
Consistent user stories and acceptance criteria generated from rough notes — a uniform, estimable Salesforce backlog instead of free-form tickets.
Documentation automation
Release notes, runbooks, and org documentation generated from the metadata and pipeline, kept in sync from the source of truth instead of drifting out of date.
Meeting transcripts to tracked outcomes
Refinement and stand-up transcripts turned into decisions, action items, and tickets, so meetings produce a record the team acts on.
How the pilot works
- One workflow automated end-to-end, chosen from the list above and scoped to your pipeline
- Guardrails designed in: human approval gates, prompts and configuration versioned in git, no unreviewed AI code reaching production
- Claude Code + GitHub Actions wired into your existing pipeline — no platform migration
- Before/after measurement: cycle time, review load, and token cost, in numbers
- A playbook handover so your team runs and extends it without me
Engagement
AI Delivery Pilot
€4,000 – 8,000
One workflow, automated end-to-end with human-review gates, measured before and after. Small enough to approve, real enough to prove the case.
- Workflow selection + guardrail design
- Build + integration into your CI
- Before/after measurement
- Playbook + team handover
Proof
My public tooling is built exactly this way — AI-first with Claude Code, then reviewed, tested, and shipped: reusable GitHub Actions modules (unit-tested TypeScript, CI dist-verification), multi-arch Salesforce CI images (test-gated, security-scanned), and an enterprise SFDX template. Read the commit history before we ever talk.
This is engineering discipline applied to AI — not an AI research practice. The same release-governance rules from my Salesforce CI/CD work apply to every AI workflow I ship.
Frequently Asked Questions
Does AI-written code go to production unreviewed?
No. Every workflow I build has non-bypassable human review gates — the same branch-protection discipline I apply to any release pipeline. AI accelerates the work; a human approves it.
What happens to our code and IP?
Everything runs in your GitHub organisation with your API keys and your data policies. Prompts and configuration are versioned in your repos. Nothing leaves your control.
Which tools do you use?
Claude Code and GitHub Actions today, wired into your existing pipeline. The workflow pattern — agent does the work, human gate approves it — is tool-agnostic and survives tool churn.
Tell me where your team loses time
Book a 30-minute call — you'll leave with an honest read on whether a pilot makes sense and what it would cost. No pitch, no pressure.
What happens next
- 1. A 30-minute discovery call about your situation.
- 2. A scheduled start date — typically within 2–4 weeks.
Related: Salesforce CI/CD & release engineering · fractional technical leadership