Temporal Knowledge Graph for AI
Tesia stores your organization's knowledge as a temporal knowledge graph, then detects change across time and document versions. Every answer is grounded in a source node and version — never generated from thin air.
Built for
Compliance · Legal & Policy · Finance · Regulated enterprise knowledge
The Problem
Static RAG retrieves text, but it has no sense of time. When a regulation is revised or a policy ships a new version, the model keeps answering from whatever chunk it happened to fetch — silently mixing outdated clauses with current ones and inventing the connections in between.
Without a structured record of what changed, when, and what depends on it, LLMs can't tell you which downstream documents a single amended rule just broke.
Core Technology
Three layers turn a pile of documents into memory you can trust: a graph that remembers, a diff engine that watches it change, and an agent that only speaks from what the graph can prove.
Pillar 01
Knowledge is stored as nodes and edges with time built in, so every fact carries the interval during which it held true. Instead of a flat snapshot, you get a living structure you can query at any point in its history.
Pillar 02
Tesia compares any two points in time — or any two document versions — and computes exactly which nodes, edges, and clauses were added, removed, or rewritten. Change becomes a measurable delta, not something a reader has to hunt for.
Pillar 03
Every claim in an answer traces back to a specific source node, edge, and version, or it isn't returned. The agent reasons strictly over the graph, so there is no room to fabricate facts the sources never contained.
Capabilities
Grounded QA
Ask questions across large, versioned document sets and get answers assembled from the graph — with the exact passages and versions each answer draws from.
Regulatory
Track regulatory and policy changes automatically, and produce an evidence trail showing what changed, when it took effect, and where it applies.
Agentic
When a rule changes, an agent walks the graph to surface every connected clause, control, and document the change propagates to — so nothing downstream is missed.
Diffing
Quantify what differs between two points in time or two versions of a knowledge base, measuring both the change itself and the artifacts it affects.
Persistent
Organizational knowledge accumulates in a persistent graph that retains its full history, so context built years ago stays queryable and version-aware today.
Traceable
Every response links back to the underlying nodes, edges, and versions, making each claim auditable down to the sentence it came from.
How It Works
Connect your documents, regulations, and policies; Tesia parses and normalizes them into structured facts.
Facts become time-stamped nodes and edges, capturing not just what is true but when.
Each new version is diffed against history to compute exactly what was added, removed, or rewritten.
An agent traces changes through connected knowledge and reports the downstream impact, grounded entirely in the graph.
of answers traceable to a source node and version
ungrounded claims returned
diffs between any two versions or points in time
retained and queryable at any point in time
Use Cases
01 — Regulated
Detect regulatory changes as they happen and generate a defensible record of what moved, when, and which controls it touches.
02 — Legal
Compare document versions clause by clause and trace how a single edit ripples across contracts, policies, and obligations.
03 — Internal
Give internal teams a shared, version-aware memory that answers questions with sources instead of guesses.
Vision
We believe AI memory should be as accountable as the people who rely on it. In a world where regulations, policies, and knowledge never stop changing, an AI's recall of them must be grounded, time-aware, and provable — so that trusting a machine's answer no longer means taking it on faith.
Mission
Tesia builds the temporal knowledge graph and agentic layer that make AI memory trustworthy in practice. Day to day, that means capturing knowledge as it changes, measuring the gap between versions, and ensuring every answer we return can be traced back to its source.
See how Tesia turns changing documents into a temporal knowledge graph — with answers grounded in every source.