Capabilities

Not a chatbot.
AI infrastructure.

Doyna combines semantic search, knowledge graphs, entity resolution, custom ML, and autonomous intelligence into a single platform that runs entirely on your server.

0/7
Autonomous monitoring
0s
Time to insight
0+
Things Copilot can't do
0%
On-premises
Search

Find by meaning, not keywords

Traditional search matches words. Doyna matches intent. Ask "who approved the spending plan?" and find emails that never mention the word "approved" — because the CFO wrote "green light" instead.

"who approved the spending plan?"
Knowledge Graph

See relationships others can't

Doyna automatically maps every person, project, decision, and vendor into a traversable knowledge graph. Ask multi-hop questions like "which vendors are connected to board members who approved contracts over €1M?"

CEOPCFOPProject AlphaJBudget DecisionDVendor AVVendor BVProject DeltaJContractD
Proactive AI

Intelligence that works while you sleep

At 6 AM, your briefing is ready. Contract expirations, budget anomalies, resource conflicts, compliance risks — all discovered autonomously, without a single query from you.

06:003 contracts expire this month, 1 needs board sign-off
09:14Project Alpha has 73% chance of budget overrun
14:30Resource conflict: 3 projects need same engineers in 6 weeks
18:00Daily summary: 12 insights, 2 critical, 4 need attention

No prompts. No queries. All discovered autonomously.

Entity Resolution

One person, not four name variants

"D. Diaconu", "Diana Diaconu", "[email protected]", "Diaconu, D. (Finance)" — Doyna resolves all variants into a single canonical entity with 47 documents and 12 threads attached.

D. Diaconu
Diana Diaconu
Diaconu, D. (Finance)
Audit Trail

Every answer is traceable

Query ID, embedding vector, search results, graph traversal path, reasoning chain, confidence score, source documents — all logged. Same query always returns the same answer. Ready for GDPR, MiFID II, and EU AI Act.

Q
Query"Who approved the vendor contract?"
E
Embed384-dim vector, 12ms
V
Search47 → 8 relevant (cosine > 0.82)
G
GraphPerson → Decision → Contract
A
Answer"CFO Diana Diaconu, Jan 15" (0.94)
Custom ML

Models that learn from your data

Fine-tuned named entity recognition trained on YOUR documents. Every correction you make feeds back into the model. Accuracy improves measurably — typical F1 improvement of +17% in year one.

0.72
0.81
0.86
0.89
Month 1Month 3Month 6Month 12

F1 accuracy score — improving with every correction

Ready to see it in your organization?

Every capability above runs on your server, with your data, under your control.

Request a Demo