"AI agent" and "AI automation" get used interchangeably, but they're different problems. Automation executes a fixed workflow faster. An agent reads unstructured input, makes a judgment call, and produces a structured outcome — with a human in the loop where the judgment call matters. We build the second kind.
Our clearest example of this is our own product, DMNexa — an AI agent that reads Instagram conversations and turns them into structured, reviewable orders. It's live, it's ours end to end, and it's the template we bring to client AI agent builds.
What this covers
- Conversation and document understanding — extracting structured data from unstructured input
- Human-in-the-loop review steps, so the agent proposes and a person confirms where it should
- Confidence scoring and escalation thresholds, so low-confidence cases route to a human instead of failing silently
- Multi-channel integration (chat, DMs, email, forms) into one agent pipeline
- Audit trails for every decision the agent makes, for teams that need to show their work
Our approach
We don't wire an LLM to your workflow and call it an agent. We design the decision boundaries first — what the agent should decide on its own, what it should flag, and what it should never touch — then build the pipeline around those boundaries.
Related
- DMNexa — AI Order Agent
- From AI Prototype to Production App
- Native Mobile App Development
- Conversational Order Processing
- AI Automation
- AI Data Validation
Discuss Your AI Agent
Tell us what decision you want an agent to own, and we'll scope where automation ends and agent behavior begins.