Integration discipline · the agentic era
Fifteen years making enterprise systems talk to each other reliably in production — X12, EDIFACT, ERP, mainframe. Now applied to AI that has to survive contact with the real world, not just demo well.
Who we serve
The integration layer is where agentic systems succeed or quietly fail. We work both sides of it.
You built the model. Landing it inside a Fortune 500 — past the EDI feeds, the ERP exports, the twenty-year-old middleware — is a different discipline. I'm the integration partner who deploys agentic systems where the APIs are ugly and the data is messy. The last mile, not the demo.
Your agents are only as good as the data reaching them. We rebuild the integration layer — ingestion, transformation, reconciliation — so legacy systems feed agent-ready, trustworthy data. Modernization that doesn't corrupt what already works.
What we deliver
From autonomous agents to the data layer beneath them, every engagement gets the same bar — systems that keep running after the handoff.
NAICS 541512
Agentic systems engineered to run unattended in production, not just demo well — built with enterprise-integration rigor.
NAICS 541512
Making heterogeneous systems talk to each other reliably in production — across every format and delivery pattern that matters.
NAICS 541512
The unglamorous 80% that makes agentic systems actually work: reliable ingestion, clean transformation, trustworthy loading.
NAICS 541512
Trading-partner integration done with operational judgment — the part most people underestimate until it breaks at 2am.
NAICS 541512
The connective tissue between AI agents and the enterprise systems they need to actually be useful.
NAICS 541512
When off-the-shelf won't bridge the gap, purpose-built middleware that respects the constraints on both ends.
The moat
Intelligent automation lives or dies on reliable ingestion from messy sources, transformation that doesn't corrupt data, and loading into the systems downstream agents depend on. That's been the job for fifteen years — first on mainframes and enterprise middleware, now on AI.
Proof, not claims
Two real engagements on live, on-premise pipelines. Every number below is from actual work — no invented client, no inflated metric.
Case 01 · Silent failure audit
An AI notes pipeline was feeding ~22,500 tokens of transcript into a model capped at 8,192 — a 2.7× overflow. The model silently saw a fraction of each input and produced confident, well-formatted, incomplete notes. No error was ever raised. We traced it through provenance metadata, reproduced the root cause, and rebuilt every affected output with a full rollback path.
Before: “the transcript is quite extensive and gets cut off, but here's a summary…” — the model narrating that it only saw a fragment. After: a complete, structured summary of the entire session.
Case 02 · Private / on-prem build
Recording → Whisper → local LLM → Obsidian, running entirely on-premise — audio never leaves the machine. For a notes tool, the only failure that destroys trust is fabrication: a decision nobody made. Every claim in two real generated notes was checked back against the source transcript, word for word.
Replaces a ~$25/mo SaaS with hardware already on hand — a hard differentiator for healthcare, legal, and government-adjacent work where data sovereignty is the constraint.
How we engage
Corp-to-corp, scoped per engagement. The Audit is the typical entry point — its findings define the work that follows.
A structured review of an existing AI/LLM pipeline for the failure classes that don't crash — they just quietly produce wrong output.
Implement the fixes with production discipline — and a recovery path on every irreversible step.
Design and build fully-local AI document and transcript pipelines for privacy-sensitive work — data never leaves your infrastructure.
Corp-to-corp engagements · scope, deliverables, and timeline defined per engagement.
Contract vehicles
Registered across a focused cluster of computer systems and IT services codes to support a range of government solicitations.
Active SAM.gov registration · UEI FDLBTVYAUQK7
Credentials & standards
What's real ships here. Nothing aspirational gets listed as earned.
Ready to partner?
Federal, state, local, or commercial — bring the messy data problem.
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