AI Knowledge Management Services

Question-answering systems built on retrieval-augmented generation that pull answers directly from your organisation's documents, wikis, and structured data rather than from general model training. Document ingestion pipeline: Confluence pages, Notion databases, SharePoint libraries, Google Drive folders, and PDF repositories indexed at paragraph level -- each chunk embedded with OpenAI text-embedding-3-small or Cohere embed-english-v3 and stored in pgvector (for teams already running PostgreSQL) or Pinecone (for scale above 5M vectors). Retrieval strategy: hybrid search combining dense vector similarity with BM25 keyword scoring fused via Reciprocal Rank Fusion, followed by a Cohere Rerank or BGE cross-encoder re-ranking pass that selects the 5 most relevant passages before they reach the language model. Answer synthesis: GPT-4o or Claude 3.5 Sonnet instructed to answer from retrieved context only, returning a structured response with the source document title, section heading, and a deep link so employees can navigate directly to the source. Latency target under 2 seconds end-to-end for a standard query on a 500K document corpus. Incremental indexing pipeline monitors source systems via webhook or polling, re-embeds changed documents, deletes stale vectors, and maintains a last_synced timestamp per document -- keeping the knowledge base current without manual curation.

A single query interface across every knowledge source in the organisation -- Confluence, Google Drive, SharePoint, Slack, Jira, GitHub wikis, and internal SQL databases -- with results ranked by semantic relevance to the question rather than keyword frequency. Connector architecture: one ingestion module per source type, each normalising documents to a common schema (content, title, source, author, last modified, permission groups) before embedding and indexing. Permission-aware retrieval enforced at query time: the user's group memberships are fetched from your identity provider (Okta, Azure AD, Google Workspace) and used to filter the candidate result set before any re-ranking, so users never see documents their account cannot access -- and the permission check adds under 50ms to latency. Unified relevance scoring: documents from Confluence and Slack rank against each other on semantic similarity to the query, so the most relevant Slack thread surfaces above a less-relevant Confluence page even though they come from different sources. Result UI: each result shows source type icon, document title, the most relevant passage highlighted, author, and last-modified date -- with filters for source, team, date range, and document type applied client-side without a new retrieval round-trip. Zero-result rate monitoring: queries returning no results above the confidence threshold are logged for analysis and used to identify knowledge gaps where content does not yet exist.

Automated pipelines that extract structured knowledge from unstructured content at scale, reducing the manual curation work that prevents most knowledge base projects from staying current. Extraction targets include product specifications from PDF datasheets, decisions and action items from meeting transcripts, policies from compliance documents, procedures from operational guides, and defined terms from legal contracts. Extraction approaches matched to content type: LLM-based extraction (GPT-4o or Claude) with a structured output schema for semi-structured documents where field positions vary; rule-based extraction with spaCy NLP for high-volume consistent formats; Azure Document Intelligence or AWS Textract for scanned and handwritten documents with complex table layouts. Output formats: structured records inserted into your PostgreSQL or DynamoDB knowledge store with typed fields per entity type; JSON-LD knowledge graph triples (subject-predicate-object) for relationship-heavy domains loaded into Neo4j or AWS Neptune; or enriched document metadata added back to the source system via API. Confidence scoring on extracted fields: values below a configurable threshold (typically 0.85) are flagged for human review rather than auto-populated -- preventing wrong extractions from corrupting downstream knowledge. Extraction pipeline orchestrated in Apache Airflow or Prefect with per-document audit logging of what was extracted, the confidence scores, and the review disposition for every flagged item.

AI-powered help centre and self-service systems that answer customer questions from your product documentation, help articles, and resolved support ticket history -- returning a synthesised answer with a citation link rather than a list of articles the customer still has to read through. RAG architecture: help centre articles, product documentation, and FAQ content chunked at the article-section level and embedded; incoming customer queries matched against the knowledge base using hybrid retrieval and re-ranking before answer synthesis with GPT-4o or Claude instructed to answer from retrieved content only, never from general training. Escalation logic: when retrieved content does not address the query with sufficient confidence (below a configurable threshold), the system responds with the closest available content and surfaces an escalation option -- the question, the conversation history, and the retrieved but insufficient articles are passed to the human agent so they have full context before the first response. Zendesk integration: answers surface in the Zendesk widget with a "Was this helpful?" prompt -- negative feedback logged and routed to a content gap queue so the documentation team knows which questions are answered poorly. Intercom and Freshdesk integrations follow the same pattern. Ticket deflection rate measured as the percentage of conversations resolved without a human agent -- typical baseline of 30-50% for well-documented products, tracked weekly to identify content gaps where deflection is lower than expected for common query types.

For domains where the relationships between entities matter as much as the entities themselves -- regulated products, pharmaceutical interactions, legal contract cross-references, financial instrument dependencies, industrial component hierarchies -- we build knowledge graphs that represent entities and their connections explicitly rather than relying on vector similarity alone. Entity extraction pipeline: spaCy or GLiNER for named entity recognition on domain-specific entity types (products, regulations, companies, components), followed by a relationship extraction step using GPT-4o with a structured output schema that identifies the predicate connecting each entity pair. Graph storage in Neo4j (the standard choice for most domains) or Amazon Neptune for teams on AWS who need managed graph infrastructure. Graph schema design before any extraction begins: entity types, relationship types, and cardinality constraints defined so the graph represents your domain accurately and the query patterns your use case requires are supported efficiently. Multi-hop reasoning enabled by graph traversal: "which customers purchased products affected by Regulation X" requires traversal across three node types (customers, products, regulations) and two relationship types -- a query pattern that vector retrieval cannot answer because it requires joining across knowledge that is not co-located in any single document passage. Graph-augmented RAG combining vector retrieval for document context and graph traversal for relationship context in the same answer -- the architecture that handles both "explain this regulation" (document retrieval) and "which of our products does it affect" (graph traversal) in a single query.

AI assistants embedded directly in Slack or Microsoft Teams that answer questions from your knowledge base in the channel where work happens, without requiring employees to switch to a separate search interface. Slack implementation using the Bolt for Python or Bolt for JavaScript SDK: the bot listens for @mention events and direct messages via the Events API, extracts the question, queries the RAG pipeline, and responds with a threaded reply containing the answer and a source citation link -- reply posted within 3 seconds for standard queries. Thread context handling: follow-up questions in a thread include the prior turns as conversation history in the retrieval query, so "what's the exception for UK employees?" following "what is the expense reimbursement limit?" correctly narrows the retrieval scope without requiring the employee to re-state context. Microsoft Teams implementation via the Bot Framework SDK and Azure Bot Service, deployed as a Teams app with the same RAG backend -- maintaining a single knowledge pipeline serving both platforms. High-frequency question categories handled without interrupting subject-matter experts: HR policy queries (leave entitlements, expense limits, benefits), IT procedure queries (system access requests, password resets, VPN setup), product specification lookups, and new employee onboarding questions that currently consume 2-4 hours per week of senior team member time. Feedback mechanism: each answer includes a thumbs-up/thumbs-down reaction; negative feedback logged with the query and the retrieved content so the knowledge base team can identify gaps and improve source documentation. Unanswered questions (below confidence threshold) are routed to the appropriate team channel with the original question and the attempted retrieval so a human can answer and the response can be added to the knowledge base.