OpenAI + GPT integration services — guardrails, evals, cost caps
GPT embedded into real workflows with tests. One client saved 40 hours per month on manual document processing.
Who this is for
SMB or ops team that wants GPT embedded into an existing workflow or SaaS feature.
The pain today
- Hallucinations slip into production because nobody validates outputs.
- Cost creep because nobody tracks tokens per feature.
- No evals — 'works on my three test prompts' is the release bar.
- No observability — when a prompt regresses, you find out from users.
The outcome you get
- GPT integrations wired into product with tests and eval harness.
- Guardrails (output validation, refusal handling, fallback to human).
- Cost caps per user, per feature, per tenant.
- Observability: prompt plus completion logs, latency, cost tracked.
Reference architectures
Three common OpenAI architectures I ship. Chat: GPT-powered conversation over a knowledge base, with context windowing, safety filters, and cost caps. RAG: retrieve relevant chunks, inject into prompt, generate grounded answer, validate output structure. Function calling: GPT picks from a tool registry, calls the tool, continues the conversation with the result. Each architecture has a production-grade template with eval harness, cost tracking, and observability baked in. Not a tutorial demo — a pattern that survives production.
Instill + 40 hours saved — real references
Instill (SITE-FACTS §6) is the AI-tooling case study: 1,000+ skills saved, 45+ projects powered, 30+ active users. It proves AI product shipping at startup scale. The AI Automation service positioning proof (SITE-FACTS §9): one client cut 40 hours per month of manual document processing via GPT-based triage plus extraction plus HubSpot push. 2 engineering weeks per month of reclaimed ops time. Retainer at $3,000 per month pays for itself in the first month on that ROI alone.
Cost caps — why most GPT integrations surprise the CFO
GPT integrations surprise CFOs for three reasons. One: no per-user caps — one user with a bug in their workflow generates 10,000 calls overnight. Two: no model-tier discipline — GPT-4o-mini is 10x cheaper than GPT-4 for 95% of classification tasks, but the team picked GPT-4 for everything. Three: no token-count per feature — nobody knows which prompts are expensive. The engagement fixes all three: per-user rate limiting, per-tier model selection based on task quality requirement, per-feature token tracking in the observability stack.
Pricing and scope
AI Automation retainer at $3,000 per month. 2 to 4 day delivery cycles. 14-day money-back. Cancel anytime. Typical GPT integration engagement: 4 to 10 weeks for one focused use case, then ongoing under the retainer.
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 GPT models do you ship?
- GPT-4.1 or GPT-4o for most quality-critical production work. GPT-4o-mini for high-volume classification. o1 or o3 for reasoning-heavy tasks. Selection per task, not blanket.
- Structured outputs or function calling?
- Structured outputs (JSON mode) for data extraction. Function calling for agentic tool use. Both production-grade; both shipped under the retainer.
- Eval harness — what does it look like?
- A test set of 20 to 200 input examples with expected output shape. Runs on every prompt change. Failures block merge. Metrics: accuracy, latency, cost per example.
- Do you handle RAG?
- Yes — see the rag-pipeline-development combo page. RAG is often part of the GPT integration, not a separate thing.
- Can you work with existing GPT integrations?
- Yes. Most engagements start by auditing the existing integration, adding evals plus cost caps plus observability, then iterating on prompt quality.
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