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", "diana.diaconu@corp.eu", "Diaconu, D. (Finance)" — Doyna resolves all variants into a single canonical entity with 47 documents and 12 threads attached.

D. Diaconu
Diana Diaconu
diana.diaconu@corp.eu
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