# Information, semantics and knowledge
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markdown_url: https://www.3.14financialcontents.com/en/capabilities/information-and-knowledge/index.md
language: en
content_type: capabilities
status: published
description: We work with ingestion, structured extraction, entities, relationships, taxonomies, graphs, semantics, hybrid search, RAG, embeddings, reranking and memory. We design representations that make it possible to find, relate and reuse…

Turning scattered sources into information that is structured, connected and usable by people and systems.

## What this capability brings together

- preserving sources
- extracting facts and relationships
- resolving entities
- defining concepts
- representing logic
- maintaining versions
- retrieving evidence
- detecting contradictions
- delivering knowledge to people, models, agents and applications

## Tools and technologies

- PostgreSQL
- pgvector
- full-text search
- TypeDB
- LinkML
- DocuLogic
- KIR
- Datalog
- DMN
- CEL
- Tree-sitter
- LSP
- embeddings
- rerankers
- parsers
- language models
- document vision

Every claim needs evidence that supports exactly its meaning and its scope.

Systems receive information in different forms: documents, tables, messages, pages, databases, code, images, audio, video, events and explanations from people.

Each form carries its own structure and context.

Semantics connects data with the domain. It allows «customer», «case», «status», «completion», «risk» or «current version» to have a meaning shared widely enough to be able to query, decide and act.

## Ingestion and structure

We build pipelines for documents, PDFs, spreadsheets, databases, APIs, web pages, messages, repositories, code, configurations, images, audio, video and events.

The originals and their metadata are preserved. Extraction produces objects with their source location and validation status.

## Entities and relationships

We identify people, organisations, products, documents, operations, systems, concepts, events, figures, rules, decisions, states and questions.

Resolution relates aliases and versions, and keeps apart those objects whose meaning or scope differs.

## Domain models

A domain model describes vocabulary, entities, relationships, hierarchies, states, rules, constraints, invariants and exceptions.

It can be expressed through schemas, taxonomies, graphs and KIR, the typed intermediate representation from which it is projected towards each use.

The representation is developed with specialists and evolves with the evidence.

## Graphs and logic

Graphs make it possible to navigate relationships that linear text hides.

For example:

```text
SOURCE → CLAIM → ENTITY → RULE → DECISION → OPERATION
```

or:

```text
QUESTION → EVIDENCE → HYPOTHESIS → DECISION → WORK
```

Rules and deductions are kept explicit when they need to be reproducible and reviewable.

## Hybrid retrieval and RAG

A RAG system relates retrieval, context, evidence, permissions and composition in order to answer in an examinable way.

We combine exact, lexical, semantic, structured and relational search. The system can retrieve an identifier, a passage, an entity, a version, a relationship, a rule, a code dependency, a decision or a set of pieces of evidence.

Reranking orders the shortlist. Expansion adds related context. Composition uses only permitted evidence and preserves citations.

## Memory

Memory organises decisions, states, questions, results, relationships, learning and temporal context.

We design policies for scope, version, currency and forgetting. Memory preserves objectives, decisions, states and dependencies with explicit policies, so that it helps to carry on without dragging along obsolete context.

## Applications

- informational intelligence;
- research;
- reporting;
- business logic;
- localisation;
- project memory;
- agent context;
- documentation;
- corporate search;
- code analysis;
- decision support.

## Evaluation

- coverage;
- extraction accuracy;
- entity resolution;
- retrieval;
- citation support;
- contradiction detection;
- abstention;
- updating;
- usefulness.

## Closing

Information becomes knowledge when it preserves meaning, relationships, provenance and a clear way of entering the work.

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