AI Agents & Automation: AI agents that do real work — on your data, in your systems.
We design and build production AI: retrieval-augmented generation (RAG) assistants that answer only from your approved documents and ERP data and cite their sources; custom AI agents that plan, use tools and take actions in your ERP, CRM, email and databases; multi-agent systems that hand work to each other; and agentic automation for the back-office processes that cost you the most time — with permissions, guardrails and human approval built in.
What it covers
- RAG assistants grounded in your documents & ERP data
- Custom AI agent development
- Multi-agent systems & orchestration
- Agentic workflow & back-office automation
- Tool & MCP integrations with your ERP, CRM & email
- Copilots & chat assistants for your teams
- Document AI: extraction, OCR & classification
- Forecasting & predictive analytics
- Guardrails, evaluation, monitoring & human-in-the-loop
The problem we solve
AI is easy to demo and hard to put to work. A generic chatbot doesn't know your business, a prototype that isn't wired into your data and permissions never leaves the pilot, and an agent that can act without limits is a risk nobody will sign off. The value comes from agents grounded in your own knowledge, connected to your systems through controlled tools, and measured against a real business outcome.
How we work
- 1
Pick a high-value process and define the outcome, the risks and the human checkpoints.
- 2
Ground the AI in your documents and data with a secure RAG pipeline and access controls.
- 3
Build the agent: tools, workflows and approvals, with evaluation and guardrails from day one.
- 4
Deploy into the workflow, monitor quality and cost, and expand to the next process.
What you get
- A production AI agent or assistant tied to a measurable business outcome.
- A secure RAG pipeline and tool integrations, scoped by role and permission.
- Evaluation suites, guardrails and human approval steps where decisions matter.
- Monitoring for quality, cost and usage — plus documentation and a roadmap to scale.
Tools we use
- Anthropic Claude
- Claude Agent SDK
- OpenAI
- Azure OpenAI
- Model Context Protocol (MCP)
- LangGraph
- LangChain
- LlamaIndex
- Semantic Kernel
- pgvector
- Python
- .NET
Have a process an AI agent could take off your team's plate?
Tell us the process. We'll tell you honestly whether an agent, a RAG assistant or plain automation is the right fit — and what it would take.