Pragma

Pragma ingests documents from multiple enterprise sources (likely including cloud storage, document management systems, and internal wikis) and builds a searchable semantic index using vector embeddings. When users query, it performs hybrid search combining keyword matching with semantic similarity to retrieve the most relevant documents, then grounds responses in actual company knowledge rather than generic LLM training data. This architecture reduces hallucinations by constraining the model to only synthesize information from indexed sources.

Pragma maintains conversation context across multiple turns, allowing users to ask follow-up questions that reference previous answers without re-stating context. The system retrieves relevant documents for each query, synthesizes answers using an LLM, and explicitly cites source documents to establish trust and traceability. This differs from generic chatbots by constraining generation to company-specific knowledge and maintaining an audit trail of which documents informed each response.

Pragma can personalize answers based on user role or department — for example, an HR question answered for a manager might include information about team management responsibilities, while the same question for an individual contributor might focus on personal benefits. The system injects user context (department, role, location, tenure) into queries to retrieve more relevant documents and tailor responses. This requires maintaining a user directory with role/department information and mapping it to document access and answer customization rules.

Pragma provides pre-built connectors to common enterprise platforms (Slack, Confluence, Google Drive, SharePoint, Jira, etc.) that handle authentication, incremental syncing, and schema normalization. The connector framework abstracts platform-specific APIs behind a unified ingestion interface, allowing knowledge from disparate systems to be indexed into a single semantic space. This eliminates the need for custom ETL pipelines while maintaining data freshness through scheduled or event-driven sync triggers.

Pragma enforces document-level access control by mapping user identities to permissions defined in source systems (e.g., Slack channel membership, Google Drive sharing settings, Confluence space permissions). When a user queries the knowledge base, the system filters search results to only include documents they have permission to access, preventing unauthorized disclosure of sensitive information. This architecture maintains security posture by respecting existing permission models rather than creating a separate access control layer.

Pragma tracks document metadata (last modified date, source system, sync status) and can flag documents that haven't been updated recently or whose source content has changed. The system may provide dashboards showing indexing coverage, document freshness, and sync errors, helping knowledge managers identify gaps or outdated information. This enables proactive maintenance of the knowledge base rather than relying on users to report incorrect answers.

Pragma uses the indexed knowledge base as context to improve query understanding — it can recognize company-specific terminology, acronyms, and concepts that wouldn't be understood by a generic LLM. For example, if your company uses 'PTO' to mean 'Paid Time Off' and this is defined in your HR policies, Pragma understands this context when interpreting queries. The system likely uses semantic similarity to map user queries to relevant document categories before retrieving specific documents, improving retrieval precision.

Pragma can be deployed as a conversational interface (likely via Slack, web chat, or mobile app) that employees use to ask questions about policies, procedures, benefits, and company information. The system provides instant answers without requiring employees to search through wikis or contact HR/IT, reducing support ticket volume and accelerating onboarding. This capability combines knowledge retrieval with conversational UX to create a self-service support channel.

Pragma collects analytics on which questions are asked most frequently, which documents are accessed most often, and which queries result in low-confidence answers. This data is aggregated into dashboards showing knowledge base effectiveness, identifying popular topics and gaps in coverage. The system can recommend which documents to create or update based on unanswered questions and can track metrics like average answer quality, user satisfaction, and support ticket reduction.

Pragma assigns confidence scores to answers based on how well the retrieved documents support the response. If an answer is directly stated in a document, confidence is high; if the answer requires inference or synthesis across multiple documents, confidence is lower. The system can flag low-confidence answers and may refuse to answer if no relevant documents are found, preventing hallucinations. This architecture provides transparency about answer reliability and helps users decide whether to trust the answer or escalate to human support.

Pragma collects user feedback on answer quality (thumbs up/down, explicit corrections, follow-up questions) and uses this to identify which documents need updating or which topics need better coverage. The system may flag answers that receive negative feedback for review by knowledge managers and can track which documents are frequently corrected or questioned. This creates a feedback loop where user interactions directly inform knowledge base maintenance priorities.