Sovereign relationship intelligence
Stop guessing. Show the next task from real company data
KynticAI sits above the systems you already run. It keeps the path of what happened, matches it to similar outcomes, and returns a clear brief for what to do next. Your model, if you use one, explains that brief. It does not invent the answer from scrap text.
Start free with Scout. Move to Fortress when you need a private enterprise runtime. Use Elite when leadership needs the full walkthrough.
Limited design partner seats: typically 40–50% off pilot scope, founder access, 60–90 day window.
authorised company data set
relationship store
known outcome paths
evidence-backed next task
See Scout, Fortress, and Elite deciding what to do next.
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.
- Ecommerce journeys rank purchase, registration, and re-engagement actions from the same customer trail.
- Logistics, NHS, legal, manufacturing, and education cases use domain-specific events, not one generic website funnel.
- Every visible recommendation links back to stored JSON and generated Elite output in the repo.
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
Four proof points from the same runtime pack.
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.
Upgrade path
Scout (free proof) → Fortress (scale) → Elite (full operating model)
Start where risk is lowest. Move only when the proof, privacy boundary, or leadership walkthrough needs the next layer.
01 · Free proof
Scout
Prove one real workflow on your machine with PostgreSQL/pgvector.
Open Scout02 · Scale privately
Fortress
Private connectors, Rust/LanceDB runtime, and deployment under your control.
Open Fortress03 · Full operating model
Elite
Discovery, synthetic demo, pilot scope, and a leadership walkthrough path.
Open Elite
Why we built this · founder
Paul Maddison — 20+ years enterprise architecture.
Built after years designing systems for large organisations: AI fails when the relationship between signals is missing or outside your control — not when the model is not clever enough.
What changes for the team
Your data finally tells people what to do next.
Scout keeps the timeline. Fortress compares it with past wins and losses. Elite hands a checked packet to your model or your team. You get a task brief, not another wall of charts.
Sales gets a next step, not another dashboard nobody trusts.
Security stays happy: operational data stays in your estate by default.
Engineers get a path they can prove: source in, packet out, outcome recorded.
Context Engine
The main product path this site is built around
KynticAI’s primary product is Context Engine: authorised company data in, a source-traced next-task brief out. Scout proves it. Fortress runs it privately. Elite takes leadership through the full story. Everything else on this site supports that path unless you open Other Products.
Scout → Fortress → Elite
01 · Prove it locally
Scout
Free and open source. Pull in authorised events, keep attribution paths, and produce a checked packet on PostgreSQL/pgvector so your team can see the shape.
Open Scout02 · Run it privately
Fortress
The enterprise runtime: private connectors, the Rust path-weight engine, LanceDB outcome matching, and deployment under your control.
Open Fortress03 · Take it to leadership
Elite
Discovery MCP, a synthetic demo, Fortress scope, a strict model-boundary packet, and a review rhythm leadership can follow.
Open EliteLooking for something else?
Other Products
Importance Engine and Clarity Gateway are completely standalone products — not modules of Context Engine. They live under Other Products so the main site stays focused on relationship intelligence.
Commercial lift
The value moment is when the system tells the team what to do next and why.
Task engine
Inject every useful data item
Bring in authorised email, web, CRM, support, billing, usage, product, document, cookie, event, and outcome items through connectors or approved one-off import/mapping work.
Task engine
Store the attribution path
For each customer, email address, cookie, browser event, account, or object, store what happened, when it happened, and which source proved it. Scout proves the path; Fortress takes the private runtime into enterprise scale.
Task engine
Compare the right relationship paths
KynticAI compares the current situation with known converted, retained, escalated, delayed, or lost journeys so the next task is supported by previous outcomes.
Task engine
Return a task brief
The output is source-traced JSON: strongest examples, caveats, missing data, and next-task options for the buyer's approved model, workflow, or human team.
How it works
Load the trail. Keep the order. Match what worked. Hand over a brief.
01 / Load
Bring in the signals you already have
Connectors, files, APIs, and approved import work can pull in emails, cookies, web events, CRM rows, tickets, usage, billing, documents, and outcomes.
Useful items become usable
02 / Keep the trail
Store what happened, in order
Cookies, emails, and events are not treated as a pile of files. Context Engine keeps each object as a trail: related items, the order of events, and which source proved them. Scout stores that on PostgreSQL/pgvector for proof and lighter loads.
A timeline you can inspect
03 / Match
Compare with paths that already won or lost
Fortress does not just rank similar text. It matches the current trail against converted, retained, escalated, delayed, or lost journeys so the next step is driven by outcomes you have already seen.
Past outcomes guide the next move
04 / Packet
Return a checked brief for the goal
Say you need to convert an email enquiry. The engine returns validated JSON with the strongest matched examples, path weights, confidence, caveats, and ranked next steps.
A brief, not a dumped context window
05 / Explain
Hand it to your model or your team
Fortress and Elite pass that packet to your approved model, workflow, or owner. The model can explain the brief. It does not invent the route. Approved outcomes feed the next cycle.
Explain the brief, do not invent it
Before and after
Source noise becomes a reviewable task brief.
Privacy-safe synthetic examples show the product shape: authorised data items become attribution paths, relationship sets, Rust/LanceDB similarity analysis, JSON output, and a plain-English next task while customer records, credentials, and source exports stay inside the customer-controlled data plane.
Inbound enquiry: from testname@test.com to the next best task
Before
An email enquiry, one web search on page A, interest in product B, CRM history, support notes, usage, billing, and previous converted customers are split across tools.
With KynticAI
KynticAI stores the enquiry as an attribution path, matches outcome-weighted converted and non-converted trajectories, then emits a schema-validated execution packet the model can only translate into a human task brief.
Example fields
email = testname@test.com
cookie = web_cookie_4281
web_search = page_a
product_interest = product_b
attribution_path = email -> page_a -> product_b
outcome_data = converted / did_not_convert
Relationship facts
top_example = previous email + page_a + product_b conversion path
option_1 = send follow-up email | priority = high
option_2 = ask user to register account | priority = medium
output = JSON file for model or team explanation
Money move
Give the sales team a plain-English task brief: what to do next, why that move is supported, and which evidence should be checked first.
Ecommerce: abandoned basket recovery
Before
Basket events, product page views, dispatch status, support questions, discount history, and purchase outcomes are analysed after the customer has gone cold.
With KynticAI
KynticAI compares the basket against previous recovered and lost journeys, then sends the model a JSON brief for the next action.
Example fields
basket_value = medium
commercial_intent_page_visit = true
support_ticket = sizing question
billing_status = payment failed once
Relationship facts
recommendedAction = sizing guide + payment retry link
similarWonPattern = answer support question before discount
confidence_band = evidence-supported
Money move
Recover the basket with useful evidence rather than an indiscriminate discount.
Support: churn prevention brief
Before
Ticket backlog, usage drop, account tier, billing risk, and previous renewal outcomes are reviewed manually after escalation.
With KynticAI
KynticAI finds which previous support interventions were linked to retained accounts and passes that relationship analysis to the local model.
Example fields
support_ticket = API latency
usage_14d = down 29%
billing_status = active
crm_contact = ops sponsor
Relationship facts
recommendedAction = senior engineer response + account-owner call
similarSavedPattern = resolved support + usage recovery
confidence_band = evidence-supported
Money move
Prioritise the intervention most associated with successful retention, with human review.
The old way vs KynticAI
Traditional products add effort and decay. KynticAI compounds.
This is the compounding intelligence criterion worth selling: the system learns from approved outcomes and improves task selection over time.
| The Old Way | The KynticAI Way |
|---|---|
| Ask AI to invent an answer from whatever text a user pasted | Deliver a schema-validated execution packet before any model translates a task brief |
| Copy company data into yet another hosted analytics layer | Keep operational state and historical paths in sovereign relationship memory |
| Watch analytics views decay as users stop trusting them | Compound path weights as new conversion and failure outcomes enter Fortress |
| Treat every signal as equal cosine-distance context | Traverse outcome-weighted trajectories that change the next best task |
| Sell a static workflow or RAG wrapper | Sell operational state-routing infrastructure that compounds with every approved outcome |
Context Engine · choose a starting point
One main product. Three buying paths. Data in, useful task out.
This website is built around Context Engine. Start free with Scout, run privately with Fortress, or take leadership through Elite. Looking for Importance Engine or Clarity Gateway? They are standalone products under Other Products — not part of this path.
Looking for standalone products outside Context Engine? See Other Products for Importance Engine (Klopp Engine + Forensic Pattern Matching) and Clarity Gateway.
Design partner program · limited seats
A real program: discounted pilot, founder access, roadmap input — for one hard workflow.
We are looking for a small set of partners who need AI that behaves on enterprise data without giving that data away. You get clear commercial preference, direct time with Paul, and a path to a co-branded case study. We get production-shaped feedback on Scout, Fortress, and Elite.
Seats
Limited
Pilot window
60–90 days
Your time
2–4 hrs / month
Pricing
Typically 40–50% off
Preferential pilot pricing
Typically 40–50% below standard commercial rates on discovery and the first Fortress pilot scope, locked for the agreed pilot window (usually 60–90 days).
Direct founder access
Working sessions with Paul on architecture, data boundary, and pilot success criteria — not a hand-off to a scripted sales process.
Roadmap influence
Your workflow helps set connector order, packet shape, and the Scout → Fortress path so the product fits how you actually buy and deploy.
Co-branded case study path
Where both sides agree, we publish a privacy-safe outcome story your industry peers can trust.
Clear feedback contract
About 2–4 hours a month from one workflow owner: one short review call plus async notes. We agree the cadence in writing before the pilot starts.
Priority support while seats last
Walkthroughs, proof reviews, and scope workshops are scheduled first for design partners.
Who this is for
- You help decide how data, AI, or integrations land in the business.
- You have one workflow where a better next task would matter this quarter.
- You can approve a narrow, read-scoped source for a pilot.
- You want data to stay under your control, not shipped into a black-box SaaS by default.
How the program works
- 1
Apply
Tell us the workflow, systems, and what good looks like in 60–90 days. Leave secrets and raw data out of the form.
- 2
20-min discussion
We map Scout → Fortress → Elite to your constraints and confirm seat fit.
- 3
Scope + terms
We lock data boundary, acceptance checks, pilot window, preferential pricing, and feedback rhythm.
Built by · founder credibility
Paul Maddison — 20+ years of enterprise architecture, applied to AI that has to behave.
Paul founded KynticAI after designing data and platform systems for FTSE 250 and public-sector organisations. His view is blunt: most AI projects fail because the relationship between signals is missing or left outside the customer's control, not because the model is not clever enough.
Design partners work with him directly on scope, proof, and what a successful pilot looks like in a real estate — not a generic demo script.
What you leave the first call with
- A clear map: Context Engine in the middle, Importance and Clarity only if you need them.
- A sensible path: Scout to prove, Fortress to run privately, Elite when leadership needs the full story.
- Honest proof: synthetic fixtures and runtime checks, with plain labels for what is not claimed yet.
- A next step that is real: design partner scope or a technical discussion.