How it works
Your company already has the knowledge it needs — it's just scattered across systems, documents, and decisions. TopoLift connects it, finds the structure hidden inside it, and lets AI reason within that structure. The result: grounded answers, with the evidence attached.
The idea in four steps
Tables, notes, decisions, and outcomes — wherever they live today. Nothing has to leave your environment.
Instead of asking your experts to hand-draw a diagram of the business, TopoLift measures it directly — using a new, highly effective mathematical embedding. The method is proprietary; contact us to learn more.
The model reasons within what your data actually supports — so it can't wander off and make things up.
Plain-language answers with the evidence, the confidence, and the gaps shown — decisions you can defend.
Why the answers stay honest
The empty space that hallucination usually fills has been pre-filled with measured reality.
The evidence and its connections define the space of sensible conclusions. To hallucinate, the model would have to argue against an explicit, weighted structure sitting right in front of it — much harder than inventing into a vacuum.
Every output traces to the specific evidence — and the source records — that produced it. Reviewers check whether the evidence actually supports the claim. A step with nothing behind it is visible as exactly that.
Each link in a chain of reasoning has measurable footing. Links that fall below threshold are flagged, re-grounded, or routed to a human — and out-of-place instructions stand out instead of blending in.
Because the structure is compact, smaller local models do work that once needed a frontier model. A 33.86× reduction in context is a cost, speed, and data-sovereignty win at once.
The reasoning atom
In most AI setups, relationships, sources, and confidence are bolted on after the text is fetched — and every seam is a place where a made-up answer can slip in. Inside TopoLift there are no seams. Each atom carries five things together:
The proof
In two independent evaluations, TopoLift-backed agents were tested against a top frontier model on real domain tasks, scored with expert-designed rubrics.
On a property underwriting case, the TopoLift-backed agent won by a wide margin — near-full marks where the baseline scored near zero. The difference was in the combinations: freeze–thaw climate × older roof; smoking × missing detectors × slow fire response.
On a stroke-patient case, the agent earned a perfect plan-quality score — connecting the dots across domains, from other health conditions to therapy tolerance to home-environment safety.
Why we're different
Most tools search text and hope the model gets it right. TopoLift changes what the model gets to work with in the first place.
| A typical AI setup | TopoLift | |
|---|---|---|
| Where knowledge comes from | Chunks of text pulled from documents. | Connected evidence, with meaning and confidence attached. |
| How things relate | Simple A-to-B links. | Captures how several factors combine at once, not just in pairs. |
| Confidence | Often just a confident tone. | Bounded by the real quality of the evidence. |
| Hallucination | Cleaned up after the fact. | Prevented before it reaches you. |
| Setup | A big platform commitment. | A light layer on top of what you already have. |
TopoLift is a layer, not a platform. It runs on top of the systems and data you already use — you don't rip anything out to add it.
Our approach to risk
You control how tightly TopoLift keeps the AI on the rails — from fully locked-down decisions to open exploration — and the setting is always visible. The constraint is adjustable; the audit trail is not.
Our paper, The Trust Layer for Enterprise AI, lays out the method, the benchmarks, and the results in full.