Automate the processes that RPA couldn't touch.
Traditional automation follows scripts. AI agents reason, adapt, and handle the messy, multi-step processes that rule-based tools can't.
What we build
- 01Document processing workflows: ingest, classify, route, and act on unstructured inputs.
- 02Multi-system orchestration: agents that coordinate across CRM, ERP, and ticketing systems.
- 03Decision support agents: AI that prepares decisions for human approval — doing the legwork, not replacing judgment.
- 04Human-in-the-loop design: clear escalation paths for what the system can't handle.
How we deliver
Document the current process, identify where agents add value, and scope the build.
Deploy the first agent, test with real data, adjust. We use LangChain, LlamaIndex, OpenAI, Anthropic, and open-source models.
Agents in production get observability: success rates, cost tracking, escalation analysis.
Terms
Proof
Common questions.
How is this different from ChatGPT wrappers?
Agents are workflows with memory, tool use, and multi-step reasoning — not a chat interface bolted onto your process. They do things, not just answer questions.
Is this mature enough for production?
It depends on the process. Simple routing and document processing: yes, now. Complex multi-agent orchestration: production-ready for specific use cases, not for everything. We'll tell you honestly.
What about data security?
We deploy in your infrastructure. Data doesn't leave your environment. Model choice (cloud API vs. local) is a scoping decision driven by your data sensitivity.