ChatGPT integration that works in production, not just the demo.

• 01

  AI chat and assistants
  Conversational AI embedded in your application: support assistants grounded in your product docs, knowledge base Q&A for internal teams, and company assistants that know your policies and pricing. Retrieval over your indexed documents, using RAG on pgvector or Pinecone, means responses cite your actual content, not general training knowledge, streaming cuts the wait to under a second to first token, and out-of-scope queries escalate to a human instead of a hallucinated answer.
• 02

  Document and content AI
  AI features that work on documents throughout their lifecycle: contract clause extraction returning structured JSON, financial report summarisation with table data preserved, plain-language rewrites of technical docs, and first-draft generation from CRM records in your brand voice. PDF extraction preserves headings and tables, and documents longer than the model's context window are processed in parallel chunks, then combined in a second pass.
• 03

  AI for your product
  AI added to your existing SaaS product as integrated features, not a chatbot bolted onto the side: writing assistance that preserves each user's style, content generation from structured data, and background categorisation of records. Multi-tenant prompt isolation scopes brand voice per customer, per-user token tracking supports cost attribution and usage-based billing, and fallback handling keeps features graceful during downtime, all built directly on the OpenAI API.
• 04

  Structured data extraction
  Extract typed, validated data from unstructured inputs with strict schema enforcement, using OpenAI function calling and JSON Schema and validating with Zod or Pydantic, so the model returns only the fields your schema defines, never invented ones. Every response is validated before a write, so out-of-range values are flagged for review instead of written silently, and we benchmark accuracy against a labelled test set before launch so you know the error rate to expect.
• 05

  AI agents and tool use
  Agents that complete multi-step tasks through a loop where the model picks the next tool based on prior results, not a single API call, built on OpenAI function calling and LangGraph. Conditional branches and state persist across tool calls, borderline cases route to human review with context compiled, tool failures surface structured errors instead of hallucinated results, and token budgets prevent runaway spend. See our multi-agent systems page for complex multi-agent orchestration.
• 06

  Cost and performance optimisation
  We reduce OpenAI API costs on existing integrations, typically by 30-60%, without degrading quality: GPT-4o mini handles high-volume focused tasks while GPT-4o is reserved for reasoning that needs the depth. Semantic caching returns stored answers for near-duplicate queries and prompt caching cuts cost and latency on long system prompts, and production monitoring tracks cost per conversation, latency, and error rates in real time.

1. Week 1
   01

   Discover and scope

   We map the integration requirements: which models fit the task, what data the model needs access to, and how outputs connect to your product or workflow. You leave week 1 with a written scope and a fixed-price quote.

2. Weeks 2-3
   02

   Design and architect

   RAG pipeline design, prompt architecture, function call schemas, and data flow before a line of production code is written. Design decisions made here cost a fraction of the same decisions made mid-build.

3. Weeks 4-12
   03

   Build, integrate, and QA

   Working integration at a staging URL by the end of sprint one. Bi-weekly demos. Accuracy benchmarking against labelled test sets for extraction and classification tasks. QA runs in parallel with every sprint.

4. Weeks 12+
   04

   Launch and monitor

   Production deployment with cost monitoring, latency tracking, and error rate dashboards active on launch day. 8 weeks of post-launch support included in every project.

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