DigiEdze AI · AI for Data & Business Intelligence
Ask Your Enterprise Data. AI Builds Conversational Analytics for You
Conversational analytics fails when it guesses at business meaning. Built on a semantic layer, it becomes reliable, and once it is reliable, the answer can trigger an action rather than ending in a chart.
- conversational BI
- text-to-SQL
- data agents
- decision intelligence
- executive copilots
- enterprise knowledge
Reference architecture
- 06Natural-language question
- 05Semantic layer & metric definitions
- 04Query generation with validation
- 03Governed data platform
- 02Explanation & citation
- 01Action handoff under approval
What Changes When This Goes Wrong?
Two dashboards report different numbers for the same metric and both are technically correct.
Text-to-SQL over raw schemas produces plausible queries with wrong business meaning.
Insight arrives days after the moment it would have changed a decision.
What We Build
AI for Data & Business Intelligence
Semantic grounding
Questions resolved against governed metric definitions, so a metric means the same thing everywhere it is asked.
Text-to-SQL with guardrails
Generated queries validated against the semantic layer, cost-bounded and returned with the query shown for verification.
Conversational BI
Follow-up questions that retain context, with explicit statements of what was filtered and what was assumed.
Data agents
Agents that monitor metrics, investigate movements and report the likely driver rather than only the deviation.
Executive intelligence
Briefings assembled from governed data with every figure traceable to its source.
Action from insight
Findings connected to the workflow that acts on them, under approval.
How it works
Conversational intelligence powered by powerful LLMs that drive the accuracy and reliability of data without any hallucinations.
Technology
- Chat4ED
- timbr.ai semantic layer
- Lakehouse platforms
- Query validation
- BI integration
Use Cases
Where This Lands in Practice
Retail & CPG
Category managers interrogate performance and receive the driver analysis with the number.
BFSI
Executive briefings assembled from governed metrics with full traceability.
Manufacturing
Production anomalies investigated automatically and reported with likely cause.
Outcomes
What We Measure
- Answer accuracy against a governed set
- Metric consistency across functions
- Time from question to decision
- Self-service adoption
Questions
AI for Data & Business Intelligence, Answered Directly
Why does text-to-SQL fail in production?
Because schemas encode structure, not meaning. Without a semantic layer defining what 'active customer' or 'net revenue' means, the model writes a syntactically valid query answering a subtly different question. The semantic layer is the difference between a demo and a system people trust.
Related services
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Generative AI
Grounded, secure GenAI applications wired into enterprise data.
ExploreChoose your starting point
Where Are You Today?
Enterprise AI conversations stall when the entry point is wrong. Pick the sentence that matches your situation.
We have an idea.
→ AI STRATEGY
We have a POC.
→ AI PRODUCTIONIZATION
We have an AI application.
→ LLMOPS / AGENTOPS
We need autonomous workflows.
→ AGENTIC AI
Our AI is too expensive.
→ AI FINOPS
Our AI can't reach enterprise knowledge.
→ RAG & KNOWLEDGE
We can't send data to public AI.
→ SOVEREIGN AI
We need to run AI securely.
→ AI SECURITY
Build Conversational Intelligence
Embrace the platform tailored to your enterprise. Zero hallucinations and completely reliable for your enterprise data.
30 MINUTES WITH AN ARCHITECT · BRING YOUR HARDEST CONSTRAINT