Load
Your data
Connectors or approved imports bring in emails, cookies, events, CRM rows, tickets, billing, documents, and outcomes.
Store
The trail
Scout keeps ordered attribution paths in your estate (PostgreSQL/pgvector for proof). Fortress moves heavy load into Rust and LanceDB.
Match
Past outcomes
The Rust engine compares the current path with similar journeys that converted, retained, escalated, delayed, or lost.
Return
A task brief
Checked JSON with top examples, confidence, caveats, and ranked next steps for the goal you asked about.
Explain
Your model or team
Your approved model can explain the brief. It does not invent the plan. Elite covers the leadership walkthrough when needed.
Proof & Validation
This page shows the KynticAI product story in motion: Scout injects authorised data items and preserves attribution paths, Fortress adds the Rust/LanceDB relationship engine, and the output becomes schema-validated execution packet the approved LLM can turn into a useful next-task explanation.
Synthetic enterprise fixtures demonstrate the full chain under controlled conditions. Customer pilots replace fixtures with authorised sources, agreed scopes, and measured outcomes - we do not present synthetic runs as live customer ROI.
Buyer spark
The exciting part is not a badge on a page. It is seeing the whole chain click: authorised data item, attribution path, relationship comparison, schema-validated execution packet, and a useful next task.
A buyer can see the product mechanics before they share sensitive operational data.
An investor can see the engine story without needing to read a repository.
A technical team can point to the exact JSON contract that makes the LLM useful.
Product Proof
The public proof is designed to answer the first real question: does the product chain make sense before any customer shares sensitive systems or records?
KynticAI's product operations surface supports reviewed commercial workflows: accounts, contacts, licences, entitlements, data-plane registration, aggregate usage, downloads, support, audit, health, OpenAPI, GraphQL, and lead handling.
Source: Product operations readiness material
The public contact path supports product updates, customer proof requests, and investor access requests with KynticAI team follow-up.
Source: Contact-form safety checks and hosted request flow
The website, product pages, scenario pages, assumption-based demo, waitlist path, and investor materials are in place for serious first conversations.
Source: IONOS go-live checks and current marketing site state
The Enterprise/Fortress evidence runtime proof is ready: a synthetic source can move through embedding, vector write, relationship analysis, and L3 evidence synthesis for technical review.
Source: Enterprise runtime notes, ready validation path, and L0-to-L3 benchmark material
The Enterprise core engine benchmark proof is ready: a privacy-safe synthetic, production-shaped proof lane passed with 100,000 vector seed rows, 101,000 final rows, 500 measured vector/search samples, and 19/19 ENT-008 checks.
Source: ENT-041/ENT-008 benchmark proof report and ready validation path
Scout provides the free open-source Context Engine path for source registration, selector shaping, snapshots, relationship facts, JSON output, APIs, and developer-facing integration behaviour.
Source: Scout integration docs and local package validation
Importance Engine and Clarity Gateway are separate product families: Importance Engine is split into KynticAI Importance Kernel, KynticAI Agentic Importance Framework, Klopp Engine, and Forensic Pattern Matching Engine, while Clarity Gateway resolves ambiguous intent before model, human, or AI routing.
Source: Importance Engine and Clarity Gateway ready product proof
We run synthetic enterprise fixtures through the full chain. Scout stores evidence on PostgreSQL/pgvector. Fortress compares relationship sets in the Rust and LanceDB runtime. Elite receives checked JSON for ranked task briefs. That shows the engineering works. It is not a claim of live customer ROI.
Scope: realistic synthetic demo evidence generated from the stored runtime pack. Customer deployments use authorised customer data, agreed source boundaries, and measured outcomes.
10
case studies run
34
Elite output prompts
960
relationship records
PASS
JSON validation
Case-study outputs
Logistics / supply chain
Cold-chain lane failure risk and control-tower intervention
High
Outcome: lane_stabilised
Action: Move the affected load to the contingency carrier, pre-alert the receiving dock, and start a control-tower exception bridge.
Legal / compliance
Privilege-safe matter escalation under deadline pressure
High
Outcome: privilege_safe_escalation_completed
Action: Escalate to the matter owner with a privilege-safe issue summary, deadline map, and outside-counsel question list.
Manufacturing / field operations
Asset downtime risk and predictive maintenance action
High
Outcome: downtime_prevented
Action: Reserve the critical spare, schedule a planned intervention window, and dispatch the qualified technician before automatic shutdown.
Education / university operations
Cohort progression signal and student-success support plan
High
Outcome: cohort_support_plan_started
Action: Start a cohort support plan with advisor outreach, assessment-deadline triage, and targeted workshop invitations.
Technical Workflow
01
Connectors or approved one-off imports load email addresses, cookies, browser events, web searches, registrations, CRM rows, support, usage, billing, product, document, and outcome items.
02
For each item, free open-source Scout stores the relationship set, behavioural path vector, and ordered path of what happened, when it happened, and which source proved it inside the customer-owned PostgreSQL/pgvector operational-path proof store.
03
Enterprise/Fortress moves high-load relationship memory into the Rust engine and LanceDB path-weight relationship store, linking entities, events, recency, contradiction, abstract patterns, importance bands, traversal, confidence bands, and caveats across large relationship sets.
04
For the question being asked, the output is governed JSON containing the top examples, attribution path, cited facts, similar successful patterns, confidence bands, caveats, and task options.
05
Fortress sends schema-validated execution packet to the customer's approved model boundary, such as an internal model, approved gateway, or deployment-specific hosted-provider adapter. The Elite path frames the executive walkthrough around discovery, synthetic demo, Fortress scope, and outcome review.
06
The LLM turns the JSON into a text explanation of the best task to achieve the goal, with human review and the outcome feeding future relationship analysis.
Relationship-Analysis JSON Example
The public schema shape shows what the LLM receives: data items, attribution path, relationship set, top examples for the question, recommendation options, estimated priority bands, confidence bands, next action, caveats, and provenance. Fortress sends that JSON to the customer's approved model boundary or workflow; the Elite path frames the executive walkthrough around discovery, synthetic demo, Fortress scope, and outcome review.
{
"schema": "kynticai.relationship_analysis.example.v1",
"privacySafeSyntheticExample": true,
"validationPath": "public shape backed by local validation path; customer pilots replace this with authorised data",
"subject": {
"entityType": "contact",
"emailAddress": "testname@test.com",
"crmContactId": "crm_contact_synth_4821"
},
"rawDataBoundary": {
"rawOperationalDataLocation": "customer-owned data plane",
"controlPlaneReceives": [
"licensing metadata",
"update metadata",
"aggregate usage",
"health state",
"support and commercial records"
],
"controlPlaneDoesNotReceive": [
"connector credentials",
"raw customer records",
"prompt payloads",
"source exports"
]
},
"authorisation": {
"purpose": "sales_next_best_action",
"role": "revenue_operations",
"auditId": "audit_synth_2026_06_17_001",
"retentionPolicy": "pilot scoped"
},
"sourceSignals": {
"emailEnquiry": "new inbound enquiry",
"cookie": "web_cookie_4281",
"webSearch": "page_a",
"productInterest": "product_b",
"crmStatus": "new enquiry",
"accountRegistration": "not yet registered",
"supportHistory": "none for this contact",
"productUsage": "not yet active",
"billingStatus": "not a customer yet",
"wonLostSaleOutcome": "similar converted and non-converted journeys available for comparison"
},
"attributionPath": [
{ "order": 1, "item": "emailAddress", "value": "testname@test.com", "event": "email_enquiry_received" },
{ "order": 2, "item": "cookie", "value": "web_cookie_4281", "event": "web_search_page_a" },
{ "order": 3, "item": "product", "value": "product_b", "event": "product_interest_recorded" }
],
"relationshipSet": {
"subject": "testname@test.com",
"relationships": [
"same_email_inquiry_pattern",
"same_product_browse_path",
"email_enquiry_plus_generic_web_search",
"not_yet_registered_account"
],
"storedIn": "Scout PostgreSQL/pgvector operational-path proof store; Enterprise/Fortress Rust/LanceDB private runtime for high load"
},
"relationshipSignals": [
{ "signal": "emailEnquiry", "importanceBand": "high", "direction": "positive" },
{ "signal": "pageASearch", "importanceBand": "medium", "direction": "positive" },
{ "signal": "productBInterest", "importanceBand": "high", "direction": "positive" },
{ "signal": "noAccountRegistrationYet", "importanceBand": "caveat", "direction": "caveat" }
],
"similarOutcomePatterns": {
"converted": "previous contacts with email enquiry + page A search + product B interest converted more often after fast follow-up",
"notConverted": "previous contacts with email enquiry only often cooled without account registration or timely response"
},
"topExamplesForQuestion": [
{
"question": "How do we convert similar email enquiries to a sale?",
"examplePath": "email enquiry -> page A search -> product B interest -> fast follow-up -> account registration -> sale",
"matchStrengthBand": "strong"
},
{
"question": "How do we get similar users to search the site again?",
"examplePath": "email enquiry -> product B answer -> guided page A link -> second product search",
"matchStrengthBand": "supporting"
}
],
"recommendation": {
"summary": "Send a focused follow-up email and offer a simple account registration path.",
"valueTarget": "increase conversion likelihood",
"similarSuccessfulPattern": "email enquiry + page A search + product B interest",
"confidenceBand": "evidence-supported",
"options": [
{ "task": "send_follow_up_email", "priority": "high" },
{ "task": "ask_user_to_register_account", "priority": "medium" }
],
"nextAction": "Send a relationship-backed follow-up that references product B and offers the shortest account registration path.",
"caveats": [
"synthetic public example",
"human review required",
"not an outcome promise"
]
},
"llmHandoff": {
"target": "Fortress: customer-approved model boundary or workflow; Elite: executive walkthrough route",
"instruction": "Explain the best task to do next using the relationship analysis, caveats, and source trail."
}
}Validation Paths
These validation paths help technical buyers decide where to start. Customer-specific connector credentials, operational records, and deployment details stay inside the agreed walkthrough.
Open-core demo path
Available for technical review
Workflow automation slice
Hosted-validated and available for technical review
Private connector families
Validated and ready for private technical walkthrough
Document and object-store expansion
Hosted-validated and ready for controlled review
Clean Boundaries
The product workflow, source categories, runtime evidence shape, JSON handoff, and privacy-safe examples that explain how KynticAI works.
Connector-specific evidence, deployment topology, run logs, and the customer-approved data-plane details needed to scope a serious rollout.
Raw operational data, connector credentials, prompt payloads, and relationship facts stay inside the customer-controlled data plane by default.
Next Step
Product Reveals
These examples show how source items become relationship paths, JSON output, and task explanation.
Proof scenario - stored JSON, relationship sets, generated Elite output
Inspect the evidence chain
“The proof page shows how stored relationship sets become schema-validated execution packet and generated task recommendations.”
Proof scenario - Scout, Fortress, Elite, connector path
See the full product chain
“Scout proves the data plane, Fortress proves private relationship analysis, and Elite proves JSON-to-text task explanation.”
Proof scenario - customer-owned data plane, product-operations metadata
Trust the boundary before the pilot
“The proof story shows where customer evidence stays private and what minimal commercial metadata KynticAI product operations need.”