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.

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Relationship IntelligenceField note

Relationship JSON beats raw prompts for serious AI work.

A raw prompt asks a model to be clever. KynticAI gives the model something better: the authorised data item, the attribution path, similar relationship sets, top examples, confidence, caveats, and a JSON recommendation it can explain.

The buyer spark

The magic is not the text. It is the evidence chain underneath the text.

What happens before the LLM speaks

Imagine a new enquiry arrives from an email address. That person also searched a page, looked at a product, and has not registered for an account yet. A normal chatbot sees a prompt and tries to respond from general knowledge. KynticAI treats that moment as a relationship question.

Scout can capture the authorised data items and attribution path. Fortress can move the high-load analysis into the Rust/LanceDB private runtime. The engine compares the path against other relationship sets and produces a small, inspectable JSON object containing the best examples and task options.

Why the output is stronger

  • The LLM sees the relationship path, not a vague instruction.
  • The buyer can inspect the source items, order of events, top matches, confidence, and caveats.
  • The answer becomes explainable because the model is explaining governed JSON, not inventing context.

Fortress and Elite use the same evidence idea

Fortress hands the relationship-analysis JSON to the customer's approved LLM, such as ChatGPT Enterprise or an internal model. Elite can use KynticAI's open-source on-prem LLM model, which keeps the explanation path inside the customer environment and avoids third-party LLM token costs.

The selling point is simple: the model becomes useful because the private relationship engine has already done the hard work. The answer is not just fluent. It is tied to evidence, examples, and the goal the team actually wants to achieve.

Next step

Ask KynticAI what your LLM should see before it answers.

Bring one customer question, one enquiry path, or one conversion workflow. The walkthrough shows how the Rust relationship engine turns source items into JSON your approved LLM can explain.

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