Your data is holding your AI back
Pod-OS is a time-aware multigraph database — a Memory layer that gives AI the full picture: connected, in context, with history intact.
“Pod-OS allows us to process billions of data points with sophisticated AI in seconds. We can tackle fundamentally bigger problems.”
- 95% of enterprise AI pilots fail to deliver measurable business impact [Fortune & MIT]
- $406M average annual loss per company from poor data quality in AI [Business Wire & Fivetran]
- 42% of companies abandoned most AI initiatives in 2025 [S&P Global]
The loss happens at ingest.
Resolution is the finest grain at which a fact is independently addressable, attributable, and timestamped.
A document is coarse. A chunk is coarser in the way that matters, because a chunk is an arbitrary cut made by a splitter that had no knowledge of the question it would later have to answer. Once you chunk, you have quantized — before the query exists. Every retrieval after that is a search over a lossy encoding whose loss function was chosen by a text splitter.
No amount of retrieval sophistication recovers what was destroyed at ingest.
Why similarity isn't enough.
Untyped text supports exactly two operators: similarity and exact match. Boolean composition, range filters, as-of predicates, negation, aggregation, graph traversal, join — none of them exist in that representation. It isn't that similarity is bad. It's that similarity is a single operator over an unclassified space, and most real decisions are conjunctive, temporal, or relational.
What Pod-OS is.
AI needs three kinds of data. You're running three databases to get them.
- Mathematical — embeddings and vectors, for similarity.
- Relational — facts, entities, and how they connect.
- Temporal — when something was true, and what it replaced.
Today that means a vector store, a graph or relational database, and some bolted-on versioning, glued together and kept in sync.
Pod-OS holds all three in one record.
How it works.
Pod-OS is a time-aware, distributed multigraph database. Every node is an Event Object — one asserted claim carrying its embedding, its typed fields, its validity interval, its source, and its confidence. Tags describe Event Objects. Bi-directional Links connect them, so relationships are traversable from either end. Nothing is overwritten: a change is a new assertion with its own time and source.
Because facts are typed and timestamped at claim level, Pod-OS answers questions a similarity or keyword search cannot express — as-of state, what changed between two dates, contradictions between sources, and what is missing.
Proof.
Ask a vector database what a policy said as of February 2024, what changed since, and which source superseded it.
It can't rank for that — the predicates don't exist in the representation. Pod-OS answers it in one query.
What did the Remote Work Policy say as of February 2024, what changed since, and which source superseded it?
Vector database
similarity onlyPod-OS
Evidence
Three properties that make it work
Claim resolution
One Event Object carries one asserted proposition — typed subject, predicate, object, validity interval, source, confidence. Not a passage. Not a chunk.
History is queryable
Nothing is overwritten. Every fact keeps its trajectory: when it appeared, what superseded it, how confidence moved. Change is data, not a lost snapshot.
Search that computes
Selection, join, aggregation, ranking, conflict detection, and absence-detection run server-side at full fidelity, with no width limit. What reaches the model is the residue those operations can't resolve.