Records accumulate. The context required for work stays scattered.
A decision may be in meeting notes, its scope in a contract, and its replacement in a later policy. Missing any one of them can send both people and AI in the wrong direction.
Turn your knowledge into a world your AI can work in.
Consilience connects documents, data, decisions, and code into an ontology people can shape directly, then gives agents the context to perform real work on a computer.
Similar text is not enough.
Real answers require people, projects, decisions, time, evidence, and change history to be read together. Consilience makes those relationships explicit and keeps human corrections available to the next task.
One workspace, from source to execution.
Source material, human-shaped meaning, retrieval, agent action, and verified results remain connected throughout the work.
Bring in the source.
Read documents, tables, code, images, and webpages while preserving originals and provenance.
Shape the meaning of the work.
Consilience proposes entities, aliases, relationships, and topics; knowledge owners review and correct them.
Reconstruct the context that matters.
EngramRAG follows people, projects, decisions, time, and evidence to connect records into the chain the task requires.
Finish the work and preserve the result.
Agents plan, use tools, and verify outcomes. Their work and human corrections carry into what comes next.
Do not stop at an answer. Iterate to completion.
The agent reads context from the knowledge graph, plans, uses the tools the task requires, and observes the result. It verifies through builds, tests, or rendering, then revises and runs again when needed. Consequential actions stay within explicit approval boundaries.
One agent, from knowledge to execution.
It does not stop at documents or the knowledge graph. The same context carries into code, terminals, browsers, connected apps, and the screen itself—within the permissions you set.
Human-shaped ontology
Define domain terms, entity types, aliases, and relationships, then review proposed links against their sources.
Ontology and topic exploration
Follow entities and relationships, and map the themes and bridge concepts that connect records.
Contextual memory with EngramRAG
Reconstruct what is valid now through meaning, time, evidence, and revisions—with every source visible.
Markdown you own
Keep tables, formulas, and diagrams in editable, portable files connected to their provenance.
Planning and multi-file edits
Read and plan complex work first, then apply coordinated changes across files after approval.
Run and verify to completion
Run commands, inspect builds, tests, and renders, and revise until the goal is met.
Direct computer control
Read the screen and operate browsers and desktop apps with mouse and keyboard.
Connected apps and live web
Use connected tools, web research, and live feeds under one permission and approval model.
Code and Git intelligence
Follow code structure while editing, reviewing diffs, and working with commits and branches.
Design canvas
Use knowledge and brand context to render, inspect, and revise visual work.
Maps and geocoding
Turn places into coordinates and build maps, routes, and reachability analyses.
Models, permissions, and checkpoints
Choose the model and reasoning effort, scope tools, and roll back at message checkpoints.
Find the chain of context, not just similar text.
EngramRAG reconstructs what is valid now by following meaning, time, relationships, evidence, and revisions across records.
Form
Bring material in while keeping its original location and provenance.
Consolidate
Resolve concepts and relationships into a model the team can review.
Recall
Reconstruct context through meaning, time, relationships, and evidence.
Correct
Carry human judgment forward while preserving what changed.
Keep the source intact. Let knowledge owners shape the meaning.
There is no need to rewrite material for another system. Source text and original files remain available while Consilience adds entities, relationships, provenance, and revision history.
Human approvals and corrections shape future retrieval and agent work. Authored knowledge stays in inspectable, portable Markdown rather than disappearing inside an opaque index.
