Useful application knowledge never lives in a single source. It sits in code, documentation, tickets, meeting notes, project repositories, exchanges, and expert memory. KONTEX organizes this material into a knowledge repository. The platform connects sources, analyzes their content, extracts useful facts, identifies entities, reconstructs relationships, and flags areas to validate. The result is not a simple documentary base. It is qualified knowledge: sourced, deduplicated, linked, auditable, and ready to be used by teams as well as AI.
Connection
Connect sources without imposing a new working tool.
KONTEX connects to the environments where knowledge already lives: Git repositories, folders, internal sites, SharePoint, Jira, Confluence, project documentation, and other structured or semi-structured sources.
Each source is attached to a workspace and synchronized within a defined scope. Teams keep control over what is ingested, how often it is updated, and which permissions apply.
Analysis
Extract the facts, entities, and relationships that explain the application.
KONTEX analyzes sources to identify knowledge items: business rules, processes, dependencies, entities, decisions, flows, anomalies, and contextual signals.
The analysis combines structural understanding of code, documentary extraction, source classification, and progressive relationship enrichment. The platform also detects inconsistencies, gaps, and drifts.
Reconstruction
Recompose knowledge that is readable, verifiable, and reusable.
KONTEX matches extracted elements, removes duplicates, canonicalizes formulations, and reconstructs relationships between facts, entities, and sources.
Each item keeps its link to the evidence that supports it: code, document, ticket, decision, or human validation. This reconstruction turns dispersed information into a base usable for impact analysis, documentation, support, development, and AI agents.
Quality
Measure repository health and target useful validations.
KONTEX tracks the quality of the knowledge produced: domain coverage, validation freshness, open contradictions, gaps, knowledge debt, and sensitive areas.
Knowledge issues can be resolved through automated enrichment, assisted choice, or targeted expert sessions. Quality becomes a continuous process — the repository is maintained through projects, changes, and user feedback.
