Custom AI Agents
Built for Production.
We build LangChain agents, RAG pipelines, and GPT-4o tool-calling bots that take real actions in your business querying databases, reading documents, sending emails, and making decisions autonomously.
What We Build
Every agent is production-hardened: structured error handling, retry logic, observability, and a human-in-the-loop escape hatch.
RAG Document Agents
Chat with your documents PDFs, Notion, Confluence, or S3 files. Vector search via Pinecone or pgvector. Accurate citation of sources prevents hallucination.
Tool-Calling Agents
GPT-4o agents with structured tool-calling: query your database, create CRM records, send emails, call APIs all from natural language instructions.
Multi-Agent Orchestration
LangGraph-powered supervisor agents that co-ordinate specialist sub-agents: Researcher ? Analyst ? Writer ? QA. With state persistence and human-in-the-loop checkpoints.
WhatsApp / Slack Bots
AI assistants deployed to WhatsApp Business API, Slack, or Discord. Handle support tickets, FAQ lookups, appointment booking, and lead qualification automatically.
Data Processing Agents
Agents that extract, classify, transform, and load reading invoices, contracts, or spreadsheets and pushing clean structured data to your database or CRM.
30-Day Warranty
30 days of free support and refinement post-delivery. All agent code, prompts, and infrastructure are yours from Day 1. Full IP ownership.
Our AI Agent Stack
"We had 50,000 support docs in Confluence. Gadzooks built a RAG agent that answers onboarding questions with exact page citations. Our support ticket volume dropped 43% in month one."
"They built a LangGraph multi-agent that reads inbound invoices via email, extracts line items, and creates entries in QuickBooks automatically. Saves my team 15 hours per week."
Related services and tools
Frequently Asked Questions
What is a custom AI agent vs just using ChatGPT?
A custom AI agent connects a language model to your specific data, tools, and APIs. Unlike ChatGPT, it can take real actions: querying your database, sending emails, reading Confluence pages, and making decisions all autonomously and on a schedule.
What AI frameworks do you use?
We primarily use LangChain (Python) and LangGraph for multi-agent orchestration, LlamaIndex for RAG, and the OpenAI Assistants API for tool-calling agents. We also work with Anthropic Claude, Mistral, and local Ollama models for privacy-sensitive workloads.
How long does it take to build a custom AI agent?
A focused single-agent with tool-calling, RAG over a document set, and a basic UI typically takes 24 weeks. Multi-agent orchestration systems with human-in-the-loop and long-term memory take 48 weeks.
Build an AI Agent That Actually Works in Production
Free 15-minute technical audit. We'll tell you exactly which architecture and models fit your use case.
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