The KnowledgeDB for your company's brain.
XHawk connects your tools and turns the data into structured, queryable knowledge, so LLMs can reason, not just retrieve.
LLMs can't ingest your enterprise in real time. Indexing isn't optional.
Models keep getting smarter. Your data keeps getting bigger. Slack, Jira, Zendesk, GitHub — each generates volumes no model can fit in a context window.
The bottleneck isn't intelligence, it's context. An LLM needs an indexed, pre-computed representation of your business: entities, relationships, events. That's what a KnowledgeDB is.
XHawk ingests and interprets enterprise data into entities, relationships, and events, then makes it queryable in one pass.
Plug into the systems your work already lives in.
Conversations, tickets, workflows, code, deals, and ops. XHawk keeps a continuous, deduplicated stream from every source.
- Slack · Teams:conversations & threads
- Jira · Linear:issues, sprints, dependencies
- Zendesk · Intercom:tickets, customer signals
- GitHub · GitLab:code, PRs, deploys
- HubSpot · Salesforce:pipeline & accounts
Turn raw activity into structure that machines can reason about.
Every record is extracted, interpreted, and stored as linked, structured knowledge.
- Entities:systems, users, customers, objects
- Relationships:dependencies, ownership, causality
- Events:changes, incidents, decisions
Semantic + graph + temporal, in one query.
One API combines what would otherwise require three stacks. Every answer cites the entities, relationships, and events behind it.
- Semantic retrieval:meaning across documents
- Graph traversal:follow dependencies & ownership
- Temporal filtering:state at any point in time
Beyond search. Built for LLMs. Explainable by design.
Traditional systems retrieve documents.
XHawk understands how your system actually works, entities and dependencies, not lookalike text fragments.
Pre-structured context, not prompt gymnastics.
Replaces brittle RAG pipelines and prompt engineering with deterministic, queryable context.
Every answer is grounded.
Cited back to the entities, relationships, and events it came from, auditable end-to-end.
Indexed, never exposed.
Source-system ACLs travel with every record, enforced at query time. What you can see in Slack, you can see in XHawk. Nothing more.
Run your domain logic, where the knowledge lives. Stored procedures, for context.
Every business has rules nobody else can write. What counts as an active customer. How to score an incident. When two tickets describe the same outage. Which Slack thread closes which Jira issue.
XHawkDB lets you ship that logic into the database itself, not into ten different consumer apps. Write a Knowledge Procedure once, in SQL, Python, or TypeScript, and it runs at ingest, on every relevant entity, forever.
-- Knowledge Procedure: derive a "customer health" event -- whenever support, product, or revenue signals change. CREATE PROCEDURE customer_health(account Entity) RETURNS Event AS $$ LET tickets = graph.neighbors(account, "reported_by", last "30d"); LET sentiment = semantic.score(tickets, "frustration"); LET arr_delta = temporal.delta(account.arr, "90d"); RETURN emit({ type: "health_changed", account, score: weighted(sentiment, arr_delta, tickets.open), grounded_in: tickets || account.contracts, }); $$ RUNS ON [account.updated, ticket.created, deal.stage_changed];
Every function in the company has questions only XHawk can answer.
Bring any model. XHawk speaks all of them.
XHawk is LLM-agnostic. Connect any model over MCP, our skills SDK, or REST. Same knowledge, same grounding, same audit trail.
Enterprise isolation, fully managed. Every customer query, sandboxed.
Zero operations overhead. Setup and running in 60 seconds.
Customers, agents, and analysts can run queries and Knowledge Procedures in isolated sandboxes. No cross-tenant leakage. No raw data leaves the perimeter. Every call is audited end-to-end.
Make your data LLM-ready.
Spin up a KnowledgeDB on top of the tools you already use. Start with Slack, Jira, or GitHub, connect the rest in minutes.