The trusted context layer for enterprise AI.
KEEL turns the documents and data scattered across your organization into evidence-backed answers, retrieved through chat and a REST API, with a confidence score and sources on every response. It is a ViitorCloud infrastructure accelerator we deploy inside your engagement.
Chat is one interface on top of KEEL. Underneath is a context layer, ingestion, tagging, embeddings, a knowledge graph, and a REST API, that any tool can query. The chat UI and the API run the same retrieval engine, so every copilot, assistant, and internal tool is grounded in the same evidence-backed context. Deployed inside ViitorCloud engagements as an infrastructure accelerator.
Every answer carries its receipts.
Ask a question in plain language. KEEL streams the answer, then shows the exact source chunks behind it, each with a match score, and a single confidence figure from the retrieved evidence. Nothing to take on faith.
Enterprise plans are eligible for a prorated refund within 30 days of renewal. The amount is calculated from the cancellation date forward, per the master agreement.
Enterprise customers may receive a prorated refund within 30 days of renewal…
For annual plans, refunds are calculated from the cancellation date forward…
Either party may terminate for convenience with 30 days written notice…
Enterprise AI is only as reliable as the context behind it.
The gap isn't a lack of AI. It's a lack of trustworthy context to give it.
Knowledge is fragmented
Documents, drives, tickets, and reports pile up across dozens of tools with no shared structure.
Verification takes longer than the work
People re-check facts by hand because they can't trust what a search result or AI answer tells them.
AI retrieves without proving
Most retrieval-augmented tools return a plausible chunk of text, not a traceable source.
Institutional knowledge walks out the door
When people leave, the context they carried in their heads leaves with them.
Generic RAG breaks down under scrutiny
Simple chunk retrieval is not enough when an answer may need to be defended or reviewed.
Every AI project starts from zero
Without a reusable context layer, each new copilot or assistant rebuilds its own retrieval pipeline.
One backbone for your organization's knowledge.
KEEL takes data from anywhere in your organization and makes it usable by AI, structured, searchable, and backed by evidence.
Connect your data
Upload PDFs, Word docs, spreadsheets and text, or connect Google Drive and hand-pick exactly which files enter KEEL. Files scale to 500 MB.
Process at scale
An asynchronous pipeline parses with Docling, generates AI tags, chunks the text, embeds it into a vector store, and maps entities into a graph.
Retrieve with evidence
Ask in chat or call the REST API. Every answer lists the source chunks it drew from and carries a confidence score from the retrieved matches.
From raw file to retrievable knowledge.
Upload or sync a file and KEEL takes over: an asynchronous, resumable pipeline parses, tags, chunks, embeds and graphs it, then it's ready to answer questions.
Every document, mapped into a graph.
KEEL runs LLM-based entity extraction over each document and upserts the results into a Neo4j graph, people, organizations, projects, products, industries and dates, deduplicated across your workspace and linked back to the chunks that mention them.
When your question names a known entity, graph-augmented retrieval pulls in extra context from the documents connected to it.
Everything you need to make data answerable.
A focused enterprise feature set, from ingestion to retrieval to programmatic access.
Evidence-backed chat
Answers stream in token-by-token, then surface a confidence indicator and an evidence panel of the exact source chunks behind the response.
AI document tagging
Each document is tagged automatically by an LLM at ingestion. Tags stay fully editable and power search and filtering across your data.
Vector + graph storage
Chunks are embedded into a Qdrant HNSW index for fast similarity search, while entities and relationships are mapped into a Neo4j graph.
REST API
Search, context, chat and ingestion endpoints let your own tools push data in and pull evidence-backed context out, the same engine as the UI.
Source connectors
Connect Google Drive via OAuth, browse the folder tree, and select the specific files to ingest. OneDrive support is on the way.
API keys & rate limits
Generate workspace-scoped read-only or read-write keys, cap them at 100 requests per minute, and revoke any key the moment you need to.
Workspace dashboard
Live counters, a recent ingestion activity feed, and a pipeline health view tell you what data is available and how the platform is being used.
Roles & permissions
Admin and Standard roles govern administrative actions, while every member of a workspace can retrieve from the full ingested dataset.
Real models doing real retrieval work.
KEEL is AI-first by design, every step from parsing to answering runs on production models, not heuristics.
Docling parsing
Extracts clean text, headings and tables from PDF, DOCX, TXT, CSV, XLSX and PPTX, streaming large files page by page.
LLM document tagging
Generates descriptive tags from each document at ingestion to organize and filter your corpus.
OpenAI embeddings
text-embedding-3-small produces 1,536-dimension vectors, indexed with HNSW and cosine similarity for fast search.
Entity & relationship graph
LLM-based extraction maps people, organizations, projects and more into a Neo4j graph, deduplicated per workspace.
Graph-augmented retrieval
When your question names a known entity, KEEL pulls in extra chunks from documents linked to it.
Confidence scoring
Every answer reports the mean similarity of its top retrieved chunks as a percentage, so you know how grounded it is.
Engineered for real enterprise data.
KEEL is engineered for large, messy, real-world enterprise data, not a demo dataset.
Parsing & ingestion
Docling parses PDF, DOCX, TXT, CSV, XLSX and PPTX through an asynchronous, resumable pipeline, streaming large files page by page.
Storage
PostgreSQL holds relational data, Qdrant indexes embeddings with HNSW and cosine similarity, and Neo4j maps entities and relationships.
Models
OpenAI's text-embedding-3-small (1,536 dimensions) for embeddings and gpt-4o-mini / gpt-4o for chat, fixed per workspace so retrieval stays consistent.
Integration
A REST API mirrors every UI capability, search, context assembly, chat, and ingestion, so external tools use the same engine as KEEL itself.
Connectors
Google Drive today via OAuth and manual file selection, with more sources planned.
Scale
Built for files up to 500 MB, thousand-page documents, and workspaces with large, growing document counts.
Built for how enterprise knowledge actually gets used.
The same context layer supports every one of these, no separate tool per use case.
Enterprise knowledge search
Employees re-search for information that already exists somewhere in the org.
One searchable layer across every uploaded and connected document.
Faster answers, less duplicated research.
Internal AI copilots
Copilots give generic answers when they lack real organizational context.
A REST API that grounds any internal assistant in KEEL's evidence-backed context.
More useful internal tools without building retrieval from scratch.
Policy and SOP intelligence
Long policy documents are slow to search and easy to misquote.
Plain-language questions answered from the exact policy text and section.
Fewer errors from outdated or misremembered procedures.
Project and delivery knowledge
Project history and decisions scatter across files and get lost when teams change.
A structured, searchable record of project documents anyone can query.
Less institutional knowledge lost to turnover.
Customer support knowledge
Support teams re-research issues because the knowledge base is hard to search.
Unified retrieval across product docs and support material, with source snippets.
Faster resolution, more consistent answers.
Technical documentation
Engineers spend real time hunting through scattered, versioned docs.
Semantic search that understands meaning, not just keyword matches.
Less time searching, more time building.
Regulated industry knowledge
Teams need to justify where an answer came from; generic AI can't show its work.
Every answer ships with a confidence score, source documents, and snippets.
Answers that can be reviewed, not just trusted blindly.
AI application grounding
Teams building AI apps need reliable context without a custom retrieval stack.
A REST API for search, context assembly, and chat, the same engine as the UI.
Faster time-to-build for AI-powered applications.
Institutional knowledge preservation
Undocumented knowledge leaves when experienced employees do.
Continuously ingested, structured knowledge that persists independent of any one person.
Reduced knowledge loss, faster onboarding.
Built for different roles, one shared context layer.
Give every AI initiative a shared foundation instead of one-off pilots.
Skip the retrieval stack, build on evidence-backed context.
Ask in plain language, get answers with receipts.
High-trust environments need traceable answers.
Ship AI solutions on a proven context layer.
Answers your team can actually trust.
KEEL is built to be evidence-first, secure, and ready for the scale of real enterprise data.
Evidence on every answer
No black-box responses. Each answer shows the source chunks it used and a confidence score derived from retrieval similarity.
Built for enterprise scale
Designed from day one for files up to 500 MB, thousand-page documents and hundreds of thousands of vectors per workspace.
Workspace-scoped by default
Every search is filtered to your workspace. Access tokens live in memory, refresh tokens stay in HttpOnly cookies.
Programmatic and UI parity
The REST API runs the same retrieval engine as the chat UI, so external tools get identical, evidence-backed context.
More than document search. More than basic RAG.
KEEL isn't competing with a single chatbot or search box, it's built to be the context infrastructure underneath all of them.
| Capability | Keyword search | Basic RAG | Copilot add-ons | KEEL |
|---|---|---|---|---|
| Entity & relationship (graph) awareness | ✕ | ✕ | ✕ | ✓ Included |
| Evidence & source attribution per answer | ✕ | ✕ | — | ✓ Included |
| Reusable across tools (REST API-first) | ✕ | ✕ | — | ✓ Included |
| AI-generated tags & metadata | ✕ | ✕ | — | ✓ Included |
| Fact-level validation & contradiction detection | ✕ | ✕ | ✕ | ◌ Roadmap |
Built with security and traceability in mind.
Workspace isolation
Every search, document, and API key is scoped to a single workspace, there is no cross-workspace data access.
In-memory access tokens
Access tokens live in memory only, never in localStorage. The refresh token stays in an HttpOnly cookie the browser controls.
Two-factor authentication
Add an authenticator-app second factor to any account, with one-time recovery codes if you lose access to it.
Scoped, revocable API keys
Generate read-only or read-write keys, cap them at a configurable rate limit, and revoke any key immediately.
Source traceability
Every answer names the documents and snippets it came from, so it can be checked against the original content.
Admin and Standard roles
Administrative actions, managing users, connectors, and API keys, are separated from day-to-day retrieval.
From context retrieval to trusted knowledge.
KEEL won't treat every document statement equally. It will validate information before promoting it into trusted organizational knowledge.
Built for teams that can't afford to guess.
No. Chat is one interface on top of KEEL. Underneath is a context layer, ingestion, tagging, embeddings, a knowledge graph, and a REST API, that any tool can query. The chat UI and the API run the same retrieval engine.
Make your organization's knowledge answerable.
Create a workspace, connect your data, and start retrieving evidence-backed answers, deployed inside your engagement.