Use cases
Concrete work, not concepts
Three engagements that show the range — from data mappings to fully agentic operations, each with the way we actually build it.
Source-to-target mappings for Dynamics 365
Legacy CRM and ERP data rarely fits a new system's schema. We use LLM-assisted schema mapping to propose field-level source-to-target mappings — customers, orders, custom entities — validate every record against the target schema, and route low-confidence mappings to a human review queue. The result is a repeatable, auditable migration pipeline into Dynamics 365 instead of months of manual spreadsheet mapping.
- Mapping proposals — generated from schema and sample data, each with a confidence score.
- Deterministic validation — every record checked against the target schema before any write.
- Review queue — approved mappings become reusable, versioned rules for the next batch.
An agent inside ServiceNow, not beside it
For IT operations we build agents that live in the ticket flow — starting at intake: users report issues in Microsoft Teams, Copilot or a chat embedded in any of your apps (via API or web embedding), and the agent turns the conversation into a clean, complete ticket. It then triages, pulls the right knowledge-base articles and runbooks via retrieval, and drafts resolutions and status reports on demand.
- Omnichannel intake — Teams, M365 Copilot or an embedded chat in any app; same agent, same contract.
- Grounded drafts — retrieval over KB and runbooks, with citations in every draft.
- Approval before write-back — ServiceNow APIs are only called after human sign-off; every step traced, every action reversible.
FounderOS — our own agentic product
We don't just build agents for clients. With FounderOS — running on Antlet.OS, our own peer platform — we are building an agent-driven operating layer for company builders: independent peer agents that carry the operational load — documents, workflows, follow-ups. What we learn there flows straight back into client work.
- Independent peers — each domain runs as its own peer with a private background graph, speaking A2A + Antlet semantics.
- Simulation-tested — simulated counterpart peers let us test whole journeys end to end before anything goes live.
- Feedback loop — patterns proven here become the layer we deliver in client projects.
Your use case could be case 04
Bring a process, a system or a data problem — we assess feasibility, risks and a realistic path to production in a free initial conversation.