Integrating GPT-class models into enterprise workflows

The fastest wins from large language models are rarely chatbots. They are the invisible features: the summary at the top of a ticket, the draft reply, the extracted fields from a PDF, the classification that routes a request. Here is how we integrate them without creating a new liability.

Where LLMs pay off first

• Drafting: replies, proposals, reports, release notes, from structured inputs.
• Extraction: invoices, contracts, forms into clean JSON.
• Summarisation: long threads, meetings, documents.
• Classification and routing: intent, priority, department.
• Search that answers: RAG over your knowledge base.

The architecture we recommend

1. A gateway service in front of every model call: authentication, rate limits, logging, cost attribution and a kill switch. Never let applications call vendor APIs directly.
2. Retrieval with access control: filter documents by the user's permissions before they reach the model.
3. Structured outputs: validate every response against a schema; retry or route to a human on failure.
4. Evaluation harness: a versioned set of inputs and expected outputs run in CI, so model upgrades are a decision, not a surprise.
5. Observability: latency, token spend, refusal rate and user feedback per feature.

Model choice is a portfolio decision

Use frontier hosted models for hard reasoning, smaller or open models for high-volume classification, and cache aggressively. Cost differences of 10–50× between tiers are normal; route by task.

Security and compliance basics

• No-training API tiers or private deployment.
• Prompt-injection defences for anything reading untrusted content (emails, web pages).
• Data-flow diagrams reviewed with security before build.
• Human approval for irreversible actions.

Do this once, properly, and every subsequent LLM feature is a two-week project instead of a two-quarter one.