Knowledge Thread, a capability of Samya Nexus AI, lets your team ask questions in plain language across every connected document store — and get answers that respect the exact same permissions as the source system, every time.
Documents live everywhere — specs, wikis, tickets, contracts. Most search tools either miss half of it, or surface all of it, permissions be damned.
Requirements, wikis, tickets, and contracts each live in their own tool, with no single place to search across all of them.
Generic AI search tools often ignore who's allowed to see what, risking exposure of restricted or confidential content.
When an AI assistant surfaces an answer, it's hard to prove exactly what it searched and why access was granted.
Knowledge Thread runs on Nexus AI's two-stage engine — one layer that thinks, one layer that acts — with access control enforced at every step.
Type naturally — "Find our data retention policy" or "What did the last design review say about the cabin sensor?" No special syntax, no query language required.
Nexus AI's planner interprets your intent and decides which connected document stores are worth searching — so you never need to know where the content actually lives.
Each document store's own access rules — project roles, group membership, confidentiality tags — are checked before a passage is ever surfaced, so restricted content never reaches the answer or the model's context.
Every answer comes with source links, and every link resolves only if you already have access to it in the underlying system — nothing new is granted, nothing is hidden that you're already entitled to.
Every feature is designed so search power never comes at the cost of governance.
Ask questions the way you'd ask a colleague. The AI interprets intent — not just keywords — and routes your query to the right document stores.
Permissions are checked at the source system, not bolted on afterward — so an answer can never leak content a user isn't already entitled to see.
Goes beyond keyword matching to understand intent, surfacing relevant passages even when your wording doesn't match the document's exactly.
Combine document context with linked issues or requirements in a single query — for example, a policy document and the tickets that reference it.
See exactly which document stores were searched, which passages were considered, and why access was granted or denied for each result.
If one document store is slow or unavailable, search continues with what's available and clearly flags what could not be retrieved — no silent failures.
Deployed entirely within your own infrastructure. Your documents never leave your environment — no third-party cloud, no external data sharing.
Knowledge Thread inherits the full Nexus AI roadmap, plus capability-specific improvements below. (Flag any of these for edits — drafted from the platform direction, worth your confirmation before publishing.)
Document search currently runs on semantic search. Support for switching between additional AI models is coming soon, so teams can choose the model that fits their needs.
Results ranked by relevance, recency, and business impact — not just keyword match.
Frequently requested queries are cached intelligently, reducing load on your systems and delivering near-instant responses.
Knowledge Thread runs entirely within your own environment — on-premises or in your private cloud. No data is sent to external AI providers or third-party servers, and access to every document stays governed by the same permissions your source systems already enforce.
See Knowledge Thread in action with a live demo tailored to your existing tool landscape and access model.