Prepared exclusively for AI Automation Lead

Audit First. Architect Second. Then We Build.

A tailored strategy to solve your most critical challenges and unlock growth.

📅 June 2, 2026 👤 Prepared by Jason 🔒 Confidential

Where You Are Today

We've taken the time to deeply understand your current situation. Here's what we identified.

🧪

Most agents are demos, not systems

They work on the happy path, then fall over the first time an API times out, a webhook fires twice, or an LLM hallucinates a tool call. Production reliability is an architecture problem, not a prompt problem.

🕳️

No baseline before the build

Teams ship agents into broken processes and amplify the chaos. Without auditing the current workflow — where data lives, where it leaks, what's already fragile — you're automating noise.

🧱

Tool sprawl with no backbone

Direct API calls, no retries, no idempotency, no observability, no cost ceilings. The agent runs fine until your OpenAI bill triples or a silent failure costs a customer. Maintainability dies on day 30.

An audit-first AI agent practice — architected to scale, designed to be maintained.

Before I write a line of agent code, I audit your current stack: where the data lives, where the gaps are, which workflows are ready for automation and which need refactoring first. Then I design the agent topology — state, error boundaries, observability, cost controls — and only then do we build. The result is a system your team can actually own, extend, and trust in production.

Services & Deliverables

Everything you need — built, delivered, and ready to run.

🔍

Phase 1 — System Audit

  • Map current workflows, tools, data sources, and integration points
  • Identify failure modes, silent breakage, and single points of failure
  • Score each workflow on automation-readiness (low-hanging vs. needs-refactor)
  • Deliver: audit report + prioritized roadmap (Loom walkthrough included)
📐

Phase 2 — Architecture Design

  • Agent topology — single-agent, multi-agent, or hybrid (justified, not defaulted)
  • State management, retry logic, idempotency keys, error boundaries
  • Observability layer — traces, structured logs, cost-per-run telemetry
  • Deliver: architecture doc + diagram, reviewed and approved before build
⚙️

Phase 3 — Production Build

  • Python or Node backend services with clean separation of concerns
  • LLM integration (OpenAI, Anthropic) with model routing and fallback
  • Webhook and API integrations with retries, circuit breakers, dead-letter queues
  • Deployment with monitoring dashboards, runbooks, and cost alerts
🛠️

Phase 4 — Hand-off + Retainer

  • Documentation your team can actually read — no folklore, no tribal knowledge
  • Observability dashboards your ops team can use without me
  • Optional retainer: model tuning, new workflows, cost optimization
  • I learn your business deeply — incentives aligned, no consultant churn

How We Get There

A clear, phased approach so you always know what's next.

1
System Audit Week 1

Map current stack, identify weak points, deliver prioritized roadmap.

2
Architecture Design Week 2

Topology, state, observability, cost design — reviewed and signed off before build.

3
Production Build Weeks 3–4

Backend services, LLM integration, integrations, monitoring — deployed and live.

4
Hand-off + Retainer Ongoing

Docs, dashboards, runbooks. Optional retainer for evolution and optimization.

How Your System Works

A visual breakdown of your build — from first touch to close.

No Yes Discovery Call System Audit Stack Ready? Refactor First Architecture Design Build + Deploy Monitor + Optimize Retainer Engagement
Ready to move forward

Let's audit the system before we automate it.

Send me a few times you're free this week. We'll start with a 30-minute discovery call — I'll come prepared with questions about your current stack and the workflows you most want to offload.