The answers are already in your systems — they just can't agree on who's who. We resolve the same veteran, facility, and contract across dozens of disconnected repositories, and surface the links between them — without moving or replacing anything. Then we proved it end-to-end on a literal terabyte.
The VA has accumulated the data and the systems — but decisions wait, because the same veteran, facility, and contract look different in every repository, and nothing connects across them.
Every vendor shows a beautiful front end. The hard part — the part nobody demos — is making the backend actually work the data: resolving identity across systems that share no common key, and proving each connection is right.
Platform-agnostic. It sits on top of whatever you run — Oracle, SharePoint, cloud, mainframe — and deploys inside your security boundary. We don't migrate your data; we resolve across it where it lives.
Connect the same veteran / facility / provider / contract across systems that share no common ID — and prove every connection against evidence.
Which contracts touch a facility, its operational status, whether that tracks with care delivery — the enterprise question you can't answer today.
Only the resolved links persist. No central copy, no rip-and-replace, no proprietary lock-in — open, standards-based, in-boundary.
Every resolution is inspectable and human-reviewable — the foundation a Chief AI Officer and data-governance role can build on.
The question any data person asks is "does it hold at scale?" So we didn't promise — we ran it: 3.95 billion records across 5,329 independent partitions, each scored against a known answer key. If accuracy degraded with scale, this band would spread. Across a full terabyte, it didn't.
Figures demonstrate the method and its scale-invariance on a synthetic, PHI-free, VA-shaped data estate scored against ground truth — not a guaranteed accuracy number on live data. The mechanism holds on real data inside your boundary; that's what a CRADA validates. Run: one laptop, 19.5 hours, partitioned + resolved in parallel — the way distributed entity resolution actually works.
Every clue is weighted by how telling it is — a matching SSN is proof, a matching city is coincidence — and the system learns the weights from your data. The same statistical method national census bureaus use to link records.
Comparing every record to every other is impossible at scale. We only compare records that could plausibly match — so it stays fast from a laptop to a terabyte.
Resolve bounded partitions in parallel — accuracy constant per partition, throughput linear in cores. A laptop proves it; a server does a terabyte in about an hour.
IBM Data Fabric, Palantir Foundry, Snowflake — platforms you buy, marry, and migrate into. Big, slow, lock-in. Another store to move your data out of its systems and into theirs.
Platform-agnostic, in-boundary, no lock-in — and we prove the hard part (resolution + correlation) on your data, fast. We can even ride on top of the platform you already bought. No rip-and-replace.
We're not asking the VA to buy anything or migrate anything. Through a Cooperative R&D Agreement via VA Pathfinder, we co-develop and validate the fabric on your data, in your environment — no funds exchanged, data never leaves.
The data fabric, the engineering team, and the build — co-invested, at no cost to the VA.
Data access, environment, and domain expertise — inside the VA boundary; data never leaves.
A short confidentiality agreement to scope feasibility and share safely.
Co-develop & validate — synthetic → de-identified → real, under protections.
Prove on real data; ready the enterprise rollout.
On your own data, inside your boundary, with zero acquisition risk. The next step is a short conversation.
Email Phill Sieg →