// 04 · AI
RAG + function calling

ChatGPT / AI Integration.

Your business, RAG-powered.

Custom GPT chatbots with retrieval-augmented generation, fine-tuning, function calling, and full grounding on your internal data.

What we build — and why it matters

AI isn't magic — it's engineering. We build custom AI solutions that ground large language models (GPT-4, Claude, Gemini) on your actual business data using retrieval-augmented generation (RAG), vector databases, and function-calling architectures. The result: AI that answers questions about your products, your policies, and your customers — not hallucinated general knowledge.

Our AI stack is built for production. We handle embedding pipelines, chunking strategies, hybrid search (semantic + keyword), re-ranking, streaming responses, token budgeting, and cost optimization. Whether you need an internal knowledge-base chatbot for your team, a customer-facing support agent that resolves 67% of tickets automatically, or an AI analyst that queries your database in natural language — we've shipped it.

We're model-agnostic and provider-agnostic. We'll recommend the right model for your use case (cost, latency, capability trade-offs) and can run inference via OpenAI, Anthropic, Google, Azure, or self-hosted open-source models. You're never locked into one provider.

Best fit for

SaaS companies, customer support teams, knowledge-heavy organizations, and any business sitting on unstructured data they want to make queryable.

// features

What's included.

RAG Architecture

Vector database grounding (Pinecone, Weaviate, pgvector) with hybrid search and re-ranking for accurate, citation-backed answers.

Function Calling

AI agents that can query your database, send emails, create tickets, update CRMs — not just chat, but act.

Streaming & Real-Time

Token-by-token streaming UIs with typing indicators, source citations, and confidence scores.

Cost Optimization

Prompt caching, model routing (cheap model for easy queries, smart model for hard ones), and usage analytics dashboards.

// case study

Real outcome. Real team.

Customer Support · 5 weeks

RAG-grounded support agent for SaaS

67% ticket deflection rate

GPT-4PineconeLangChainNext.js
67%
// faq

Questions about chatgpt / ai integration.

How long does a typical chatgpt / ai integration project take?+
Most chatgpt / ai integration projects ship in 3–6 weeks depending on complexity. We provide a detailed timeline with weekly milestones before you commit.
Do you provide ongoing support for chatgpt / ai integration?+
Yes. All projects include 30 days of post-launch support. Growth plans include 90 days with priority Slack access. Enterprise plans include 24/7 on-call with a 1-hour response SLA.
Can you integrate chatgpt / ai integration with our existing tools?+
Absolutely. We integrate with 400+ SaaS platforms and can build custom connectors for any API. Your new chatgpt / ai integration will fit into your existing stack, not replace it.
Who owns the code after the project?+
You own everything. Every project includes a full source-code handoff, documentation, and infrastructure-as-code. We never lock you to us.
// related

You might also need.

Ready to build your chatgpt / ai integration?

Book a free 30-minute discovery call. We'll map your requirements and send a written plan within 48 hours — even if you don't end up working with us.