COGknows consolidates every source you hold — documents, databases, open source, field reports, sensors and messaging — into one governed knowledge layer, then answers questions across all of it. Every answer arrives with a RIVER™ trust score, and every trust score comes with a reason — AI answers you can defend.
Your knowledge is scattered across silos that were never designed to talk. COGknows ingests all of it through a single pipeline — detection, conversion, chunking, embedding, entity extraction — and resolves it into a unified knowledge graph. Ask one question; get an answer spanning every source, at any complexity.
Full-text, vector and graph search fused — semantic recall with structural precision.
Duplicates consolidated with an audit trail. One person, one node — across every source.
Types and relationships inferred as data arrives — or define your schema by hand.
From production databases to field WhatsApp groups — 30+ connectors feed one governed knowledge layer. Most never copy your data: they map it, describe it, and query it in place.
Most systems treat every retrieved fact as equally true — and quietly amplify rumours, stale charts and single-source claims. RIVER™ scores every datum on five orthogonal dimensions, propagates the signal from source to chunk to entity to answer, and shows it on every result.
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One knowledge layer, four surfaces — build views, ask questions, generate working apps, or see it all on a map. All share the same governed data and the same RIVER™ trust signal.
Natural-language widgets — charts, tickers, distinct-value panels and live queries — composed on a grid, published with a click, and shared via revocable public links. Every figure traces back to its sources and its trust score.
A streaming RAG agent with tradecraft built in — hybrid search, graph traversal, timelines and dossier tools across a multi-step loop. It triangulates, separates fact from inference, flags collection gaps, and grounds every claim against its retrieved context.
Describe a tool; COGknows writes JS or Python that runs in a locked sandbox — display and calculation only, no OS, no network, reading only whitelisted data through a signed bridge. Embed it as a dashboard tile or share it as a read-only link.
A GPU-accelerated geospatial view layers incidents, entities, routes and territories on one interactive map — points, heatmaps, arcs, clusters, lines and polygons. Layers are fed live by the chat agent, the knowledge graph, WhatsApp and your own imports, and every feature keeps its RIVER™ trust signal.
Wherever answers are buried across disconnected sources, COGknows consolidates them and returns a defensible answer. A few of the questions teams bring us — road safety is one example, not the whole story.
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For government and commercial intelligence alike, the data is the risk. COGknows is built so that one customer can never see another's — and so that your data is never used to train anyone's model, including ours.
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Each workspace gets its own database, its own graph, its own object store and its own search index. Customer-to-customer isolation is structural — there is no shared query path to leak across.
Plans scale with documents, workspaces, external sources and monthly LLM tokens — and the model tier you pick. Pick a starting point, then fine-tune everything in the configurator below.
Choose how smart your models should be, set your scale, switch on the modules you need — the price updates live.
Tell us about your data and your deployment constraints. We'll come back with a scoped plan — managed cloud, private cloud (VPC) or fully on-prem / air-gapped.
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