The answers are already in your systems — they just can't agree on who's who. We resolve the same person, facility, and contract across dozens of disconnected repositories, and surface the links between them — without moving or replacing anything. Then we validated it across a terabyte-scale campaign — 3.95 billion records, 5,329 independent runs.
Federal agencies have accumulated the data and the systems — but decisions wait, because the same person, 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 person / 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 independently generated estates, each scored against its own answer key. Across the entire campaign, the score never drifted:
Honest scope: synthetic, PHI-free estate whose mess model and matcher are co-designed — these figures validate the mechanics and the relative lift of each layer, not live-data accuracy (that's what a CRADA validates). The campaign is 5,329 independent fixed-size runs totalling 1.02 TB of generated JSON (≈740 GB compact); single-population sharded resolution is built and validated — blocking-key partitioning reproduces the unsharded result exactly. One laptop, 19.5 hours, measured.
Every clue is weighted by how telling it is — a matching SSN is proof, a matching city is coincidence. The production engine (Splink) learns those weights from your data; our published figures use expert-set weights — the conservative baseline. Same statistical method national census bureaus use.
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 measured 19.5 h for the campaign; server-class parallelism should cut that to about an hour — an extrapolation we'll validate.
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 you to buy anything or migrate anything. Through a Cooperative R&D Agreement (CRADA), 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 agency.
Data access, environment, and domain expertise — inside your 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 →