Updates
Follow the KynticAI build
Join for product updates, new demo scenarios, connector progress, and the first opportunities to see KynticAI in action.
Inject
Any data item
Connectors or approved one-off imports load email addresses, cookies, events, CRM rows, products, support, billing, documents, and outcomes.
Store
Attribution paths
Scout stores each item's relationship set, vector, and ordered attribution path in customer-owned PostgreSQL/pgvector. Fortress and Elite move high-load memory into Rust/LanceDB, not KynticAI product operations.
Compare
Rust + LanceDB
Enterprise contains the proprietary Rust relationship, weighting, traversal, and LanceDB path for fast similarity analysis across millions of relationship sets.
Output
Top-example JSON
The engine creates JSON with the best examples, weights, confidence, caveats, and ranked task options for the question being asked.
Explain
LLM task brief
Fortress sends JSON to your chosen LLM. Elite can use KynticAI's on-prem open-source LLM path with no third-party token charges.
Updates
Join for product updates, new demo scenarios, connector progress, and the first opportunities to see KynticAI in action.
Evidence Results
These examples keep commercial conversations focused on product fit, source boundaries, and the first measurable workflow.
Commercial scenario - product fit, deployment shape, support need
No public pricing table needed
“Commercial conversations should start with product fit, source boundaries, deployment shape, and the first measurable workflow.”
Commercial scenario - one workflow, agreed sources, outcome measure
Scope the buyer properly
“A good pilot starts with one workflow and one next-best-task question, not a generic AI transformation promise.”
Commercial scenario - serious first conversation, deeper materials
Keep the conversation focused
“Investors and design partners need the product story first, then deeper materials when the fit is real.”