How Laura works
Answers in the conversation. Actions after it.
Temporal knowledge graph
Most AI forgets the meeting. This one remembers the company.
Ask an assistant who owns the migration and it re-reads a document. Ask this one and it answers from a graph of your company that has a timeline — who owned it in March, who owns it now, what slipped in between, and who is carrying too much this quarter.
Documents tell you what someone wrote down once. A temporal graph tells you what is true today, and what changed to make it true.
Entities and dates, not paragraphs
Your workspace is read in as timestamped episodes — people, projects, owners, deadlines, and the dated relationships between them. Questions get answered by traversal, not by hoping the right paragraph was retrieved.
Never at the cost of the conversation
The graph is consulted once per question, on a hard timeout. If it's slow the answer falls back to the flat snapshot and arrives anyway. In a live room, late is the same as wrong.
Your graph, on our own stack
Partitioned per organisation and built entirely on our own inference stack — no third-party embedder, no data handed to another vendor to make your memory work.
Every company already has this graph. It just lives in six people’s heads and leaves when they do.
0%
fewer repeated process questions
0.0×
faster internal answers
24/7
process guidance, whenever meetings happen
Every meeting
gets an expert in the room
Illustrative placeholder metrics — replace with your measured results.