Co Creators | Living Eden Frameworks

The Engine

Presence-based AI can't find a gap, and that's the whole point. Patent-analytics platforms index existing filings. Research-search tools rank existing papers. LLM assistants summarize existing content. All of them operate on presence, and a void has no document to return, no passage to summarize, no token to predict, so they are structurally incapable of finding it.

LEF Ai Engine. The engine itself, in a container, in your environment.

It reasons over the shape of the surrounding evidence rather than the evidence alone. Give it a body of records and it reads what has come together and what has stayed empty: convergences, voids ringed by dense and mature activity, the edges between them, and how each has moved across the life of the records. Which gaps are closing, which are opening, and what the corpus itself says could fill them. The same apparatus can read corpora that have nothing to do with one another and find where they can or should converge. That read goes to whoever owns the records: what the corpus carries, what it is missing, and what would have to be collected to close the gap.

Already running: Research · Patents · Biology · Federal & Civic ISB

Introduction Video →

Both reads below ship inside the container. You run the blind read on any corpus you hold, and the assessment against any seat you need, not only the one you came to us with.

Corpus Fit Read. We read your corpus and name the seat it can carry, and the tasks inside it. One build, all tasks combined into a single seat.

Corpus Fit Assessment. Or you bring the seat. Tell us the seat you need and we run your corpus to see whether it supports what you are asking for. Where your ask cannot be applied, we tell you which data points are missing. Where it does land, we point out other tasks you may not have considered, based on your corpus.

Corpus Fit →

Our other builds:

LEF Ai is three builds of which LEF Ai Engine is one.

LEF Ai Education

Single-case research made operational for schools. Score dashboards rank children against a fixed bar; LEF Ai Education measures the environment around each student against how that student learns, so every child is their own baseline and every classroom a measured condition. Reads the behaviors behind the scores from assessment data schools already produce, and emits placements, groupings and school matches proven from the building’s own history.

LEF Ai Engineering

The engine pointed at what the field is asking to be built. Reads the open solicitation record against patent claims and published research to find where the asked-for thing is already occupied, then assembles what is left into a whole candidate build: the subsystems, the parts a shop would make or buy, and whether a small crew could make it. Every position is tested against what is already sold and already built, and carries what it did not establish. The answer is as often do not rebuild this as a thing to build.

Competitive Determinations

Awardable & Awarded

Outcomes of assessments run on LEF by the buying side, against criteria they published. Awardable status is eligibility to be purchased through a streamlined pathway; it is not an award and does not guarantee selection.

Deemed Awardable

Decision Support at the Command Edge

Finding What Multi-Source Records Should Contain and Do Not. Assessed against the published criteria and now visible to Government Customers in the IARPA Solutions Marketplace, where the five-minute solution video sits behind their sign-in.

Determined 22 September 2026 • Submission 8-26-0025

IARPA Solutions Marketplace →

Intellectual Property