The Universal Context Layer for Enterprise AI
Your Data Already Knows the Answer
KynticAI is sovereign AI infrastructure that sits above your existing SQL, CRM, and ERP — and below any AI model — turning dark enterprise data into governed, AI-ready context. No data movement. No rip-and-replace. No US cloud dependency.
Enterprise data invisible to AI
Deployment to first value
Enterprise connectors
Zero data movement architecture
What makes this different
We Don't Do the AI Bit
Everyone is building AI models. Almost nobody is building the infrastructure that feeds them. KynticAI is not another chatbot, not another wrapper, not another “AI-powered” dashboard. It is the governed data substrate that makes every AI model in your organisation actually work — because it gives them real business context instead of hallucinated guesses.
Raw Ingestion
Metadata harvested from SQL, CRM, ERP, SharePoint, email headers — without moving the underlying data
Recursive Distillation
Raw fields compressed into semantic attributes with confidence scores, provenance chains, and temporal decay
AI-Ready Context
Governed context facts served to any model via GraphQL or REST — with full audit trail and confidence badges
Scroll the control plane
Watch Enterprise Data Become AI-Ready Context
Sources, distillation, proof, and compounding results move as one governed layer, so leaders can see how raw enterprise systems become trustworthy AI context.
01 / Sense
Connectors read metadata in place
SQL, CRM, ERP, SharePoint, logs, and service desks stay exactly where they are. KynticAI listens for structure, freshness, provenance, and business meaning.
02 / Distil
Recursive layers turn fields into facts
Raw columns become governed context facts with confidence, decay, source lineage, and model-ready language that any agent can use safely.
03 / Prove
Every result feeds the flywheel
Conversions, churn saves, support resolutions, and operational outcomes are attributed back to the selectors that caused them.
Not just metadata narratives
Real Data In. Real Context Out.
From SQL Table to Context Fact
Before (raw)
SELECT customer_id, last_order_date FROM orders WHERE status = 'completed'
After (context fact)
{ entity: "customer-8291", attribute: "churnRisk", value: 0.73, confidence: "high", source: "orders.last_order_date", freshness: "4h" }From CRM Field to Revenue Signal
Before (raw)
HubSpot → Contacts → Deal Stage: "Proposal Sent" → Last Activity: 22 days ago
After (context fact)
{ entity: "deal-4410", attribute: "dealDecay", value: 0.81, signal: "stale-proposal", action: "re-engage within 48h or mark cold" }From Server Logs to Attribution
Before (raw)
GET /pricing HTTP/1.1 — 200 OK — Referer: google.com/search?q=enterprise+AI+UK
After (context fact)
{ session: "s-29a1", touchpoints: ["organic-search", "/problem", "/architecture", "/pricing"], conversion: true, value: "enterprise-lead" }The Six Fears Keeping Enterprise Leaders Awake
Every large organisation faces the same existential anxieties about AI adoption. KynticAI was engineered to neutralise every single one.
Dark Data Graveyards
80% of enterprise data is invisible to AI. Trapped in legacy SQL tables, flat files, and siloed CRMs that nobody dares touch. That is not a gap — it is a graveyard of unrealised revenue.
The AI Wrapper Bubble
Thin GPT wrappers with no proprietary data moat will collapse the moment OpenAI adds the feature natively. Without a data layer, you are renting someone else's intelligence.
The DXP Money Pit
Sitecore, Adobe, Optimizely — enterprises spend millions on content platforms that take years to migrate and deliver incremental improvement at best. The last agency told you it would take 18 months. It took 30.
The 20-Person Data Team
Manual data-entry armies that exist because nobody owns the integration layer. You are paying humans to do what a semantic overlay could do in milliseconds.
Sovereignty Is Not Optional
NHS Trusts, MoD contractors, and regulated industries cannot send data to US cloud hyperscalers. GDPR compliance is a legal requirement, not a marketing checkbox.
No Proof, No Purchase
Every AI vendor promises transformation. None of them prove it before the contract is signed. Your board will not approve another hope-based technology bet.
Three Reasons KynticAI Is Different
Zero Data Movement
KynticAI reads metadata where it lives. Your CRM data never leaves your servers. Your SAP stays in your data centre. Sovereignty by architecture, not policy.
Self-Improving Flywheel
Every conversion, every click, every support ticket feeds back into the model. Day one is your worst day. Context compounds like interest, getting sharper with every interaction.
90-Day Provable ROI
The Discovery Agent runs a pre-sales audit on your metadata before you sign anything. You see the revenue gaps, the attribution blind spots, and the projected lift — in ten minutes, using your data.
The Old Way vs The KynticAI Way
| The Old Way | The KynticAI Way |
|---|---|
| Rip-and-replace migration | Semantic overlay that reads existing systems in place |
| 12-18 month integration project | 6-week deployment with value visible in days |
| Data warehouse required first | Zero data movement — reads CRM, ERP, SQL in situ |
| AI is a bolt-on afterthought | AI-native from the ground up, model-agnostic |
| Static reports and stale dashboards | Self-improving context that compounds daily |
| Vendor lock-in (Salesforce, Adobe, Sitecore) | Open-core, 40+ connectors, your data stays yours |
| US cloud dependency for AI features | Sovereign on-prem with embedded Llama / Mistral |
| No attribution — hope-based marketing | Server-side pixel with conversion attribution to the selector level |
Live Intelligence
Live Intelligence From Real Enterprises
These are real insights generated by the KynticAI engine across live deployments. Click to reveal measurable outcomes.
Multi-Channel Fashion Retailer — £85M revenue
£47k/month saved
“SKU 8842 (Charcoal Wool Coat, size M) now shows a 31% online return rate versus 12% in-store. Customers who viewed the size-guide had 42% lower returns. Recommended action: add mandatory size-guide modal for this SKU and all lookalikes. Projected impact: £47k/month reduction in returns.”
Steel Producer — 3 blast furnaces, legacy SCADA + SQL Server 2014
£1.8M annualised saving
“Blast Furnace 2 produced 0.3% higher defect rate over 72 hours. The Rust engine correlated this with a 1.4% increase in silica content in iron ore from Supplier Kongsvik. Recommended: blend remaining batch with Ironridge ore at 60/40 ratio. Projected impact: return defect rate to baseline, saving £1.8M annualised.”
NHS Acute Hospital Trust — sovereign, on-prem
+14 procedures/week
“Theatre 4 utilisation has fallen 18% over 11 days. Root cause is post-op ward 7B at 94% capacity plus 3 recovery nurses on long-term sick. Recommended: redirect morning slots to Theatres 2 and 3 until staffing recovers. Projected impact: +14 additional procedures per week.”
Challenger Bank — 800,000 customers
4,200 accounts at risk
“Customers who open a current account but don’t set up a direct debit within 14 days have a 63% probability of becoming dormant within 90 days. 4,200 customers are currently in this window. 78% opened the ‘Set Up Direct Debit’ screen but didn’t complete — suggesting UX friction, not lack of intent.”