Your knowledge.
Instantly accessible.
RAG across your entire document landscape — Confluence, SharePoint, wikis, tickets, runbooks. Answers in natural language, with source citations, on your hardware.
Not search.
Understanding.
Multi-Source Ingestion
Connect Confluence, SharePoint, Git repos, file shares, databases, ticket systems. One unified knowledge layer across all silos. Incremental sync, not batch.
Semantic Search
Vector embeddings with hybrid BM25 ranking. Understands intent, not just keywords. "How do we handle GDPR deletion requests?" returns the actual process, not 200 documents.
Source-Cited Answers
Every response includes clickable source references. Know where the answer came from. Audit trail for compliance. No hallucination without citation.
Access Control Inheritance
Respects your existing permissions. SharePoint ACLs, Confluence spaces, LDAP groups. A user only sees answers from documents they are allowed to read.
Domain Adaptation
Fine-tune retrieval for your terminology, your abbreviations, your internal naming conventions. Banking, healthcare, legal, engineering — each domain has its own language.
On-Premise Embeddings
Local embedding models (BGE, E5, Nomic). No document content leaves your network. Vector store on your infrastructure.
Where knowledge
creates value.
Internal Knowledge Portal
Self-service answers for employees across HR policies, IT runbooks, product documentation, engineering decisions. Reduces tier-1 support tickets by 60%+. Makes new hires productive from week one.
Regulatory Compliance Assistant
Instant answers across compliance frameworks, internal policies, audit documentation. "Does our data retention policy comply with NIS-2?" — answered in seconds with source links.
Customer Support Augmentation
Support agents get instant answers from product docs, known issues, resolution histories. Faster resolution, consistent quality, automatic documentation of each case.
Engineering Knowledge Base
Architecture decisions, API documentation, deployment runbooks, incident post-mortems. The institutional memory that doesn't leave when engineers do.
From question to
cited answer.
Every query follows a deterministic six-stage pipeline. Each stage is observable, each result is traceable, every source is verifiable.
Query Input
User submits a natural-language question through the interface or API.
Intent Analysis
Parses query intent, extracts entities, determines scope and required source types.
Hybrid Retrieval
Parallel vector search and BM25 keyword retrieval across all connected sources.
Re-Ranking
Cross-encoder re-ranking of candidate chunks. Filters by ACL permissions and relevance.
LLM Synthesis
Local LLM generates a coherent answer grounded in retrieved context. No fabrication.
Cited Response
Answer delivered with inline source citations, confidence scores and audit metadata.
Built for
production.
Hybrid vector + BM25
Sub-500ms query time. Parallel dense and sparse retrieval with cross-encoder re-ranking for maximum precision.
BGE / E5 / Nomic
Local inference, 1024-dimensional vectors. No external API calls. ONNX Runtime acceleration on CPU and GPU.
Llama 4 / Qwen 3.6 / Gemma 4 / Nemotron
Local inference, fully configurable. Choose the model that fits your hardware, your language, your domain.
Confluence, SharePoint, Git, Databases
File shares, SQL/NoSQL databases, REST APIs. Incremental sync with change detection. Plug in any source via connector SDK.
Zero-cloud, ACL inheritance
Full audit trail. Permission-aware retrieval. No data leaves your network. LDAP/AD integration out of the box.
10M+ documents
Incremental sync, horizontal scaling. Shard vector stores across nodes. Production-tested at enterprise scale.