Hire an OpenAI developer who ships with tests and cost caps
GPT integrations wired into real apps. Guardrails, evals, cost tracking. $3,000 per month AI Automation retainer.
Who this is for
SMB or ops lead who wants GPT built into an existing app — document triage, drafting, summarization, lead qualification.
The pain today
- Prompt engineering is ad-hoc — every engineer invents their own style.
- Costs are unclear because nobody tracks token usage per feature.
- Hallucinations slip into production because there are no guardrails.
- No evals — 'works on my three examples' is the release criterion.
The outcome you get
- A senior engineer who ships GPT integrations with real discipline.
- Structured prompts with tests and eval harness.
- Cost caps per user and per feature, monitored.
- Guardrails (output validation, refusal handling, fallback behavior).
What OpenAI integration work actually includes
The AI Automation retainer covers: prompt design (system prompts plus user prompts plus few-shot examples, versioned like code), output validation (structured outputs via JSON mode or function calling, parsed with Zod or similar, errors handled), cost tracking (token counting per feature, per user, per tenant — with budget alerts), rate limiting (per-user and per-tenant caps to prevent runaway cost), eval harness (a test set of inputs plus expected output shapes, run on every prompt change), observability (structured logs of prompt plus completion, plus latency and cost), and guardrails (refusal detection, output classification, fallback to human when the model declines).
Instill — AI product proof point
Instill is my self-initiated AI product: Next.js 16 plus React 19 plus PostgreSQL plus Vercel plus MCP Protocol. 30+ active users, 1,000+ skills saved, 45+ projects powered. It is the first AI case study I have shipped standalone. The OpenAI integration patterns used there (structured outputs, eval harness, cost monitoring, prompt versioning) transfer to any OpenAI-in-existing-app project. The point is not 'I use ChatGPT' — the point is shipping AI features that do not fall apart in week three.
40 hours per month saved on document processing
AI Automation service positioning proof: one client cut 40 hours per month of manual document processing by adding GPT-based triage plus summarization plus structured extraction to their existing workflow (SITE-FACTS §9). That is 2 engineering weeks per month of reclaimed ops time. The retainer ROI math: $3,000 per month for the retainer vs 40 hours at a $50 to $150 per hour loaded ops cost is a 1.5x to 5x return per month — and it compounds as the integration grows.
Pricing and engagement
AI Automation retainer at $3,000 per month. 2 to 4 day delivery cycles. Daily async updates. 14-day money-back inside the first two weeks. Cancel anytime. Work Made for Hire — prompts, code, eval sets, all yours.
Recent proof
A comparable engagement, delivered and documented.
A prompt library that works with every AI tool
A home for your best AI prompts. Save them once, then use them in Claude, Cursor, or any AI tool you work with. No more copy-paste.
Frequently asked questions
The questions prospects ask before they book.
- Which OpenAI models do you ship?
- GPT-4.1, GPT-4o, o1, o3 — picked per task. Cheaper GPT-4o-mini for high-volume classification. Bigger models only when task quality requires them.
- Structured outputs or function calling?
- Structured outputs (JSON mode) for data extraction. Function calling for agentic tool use. Both are production-grade and I ship both depending on use case.
- How do you prevent hallucinations?
- Not eliminate — prevent from shipping. Output validation (Zod schemas), retrieval-grounding (RAG for fact-heavy outputs), eval harness flagging regressions, and human-in-the-loop fallback when the output fails validation.
- Cost monitoring?
- Token counting per request, per feature, per user, per tenant — logged to the same observability stack as the rest of the app. Budget alerts at 50%, 80%, 100% of monthly cap.
- Can you migrate us from OpenAI to Claude (or vice versa)?
- Yes. The prompt-plus-validation layer abstracts the model, so switching providers is a config change once the eval harness is in place. The migration includes re-running evals against the new model to confirm quality.
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