Automate information and knowledge management

The problem isn't a lack of information. It's that the existing information isn't leveraged. Hundreds of reports, contracts, records, guides, and precedents... years of work buried, forgotten, scattered.

The result:

  • Endless searches
  • Precedents no one can find
  • Patterns that go unnoticed
  • Knowledge lost when someone leaves

The Solution

We've developed two complementary layers that can be deployed separately, combined, or in phases:

1. Retrieval-Augmented Generation (RAG) Layer

Enables the agent to search, read, and respond across your entire document base

2. Institutional Memory Layer

Enables the accumulation, capitalization, and reuse of knowledge

Retrieval and Generation layer

Centralizes all internal and external information in the agent's knowledge base to answer team questions:

  • Finds specific data in seconds
  • Avoids cumbersome searches and tedious reading
  • Enriches internal data with external data
  • Analyzes the data with the most sophisticated AI
  • Discovers hidden patterns in the data
  • Responds with traceability to the original source

The Problem: Institutional Memory Layer

The problem with the RAG layer is that it responds and forgets; each query starts from scratch.

  • Does not leverage work already done
  • Insights from previous analyses are not considered
  • There is no collective memory; it's up to each person

The solution

The solution is an institutional memory layer that your agent builds and maintains with an internal wiki:

  • Maintains pages on clients, markets, processes, trends, etc.
  • Integrates each new document with existing precedents in your document base
  • Detects contradictions, reinforcements, and changes in existing knowledge
  • Maintains indexes, a glossary, and relationship maps