How TatvaAI Works

When a user asks TatvaAI something like "show me the monthly revenue trend for this fiscal year compared to last year," a multi-step AI pipeline runs behind the scenes to turn that plain-English question into a structured, secure database query. This article walks through that process step by step.

Step-by-Step: From Question to Answer

TatvaAI shows its "thought process" transparently, so users can see exactly what the system is doing:

1. Resolving ERP schema metadata — TatvaAI first loads the active schema and its table metadata, so it knows where each type of data lives.
2. Generating a question embedding — The user's question is converted into a semantic search vector, allowing the system to understand meaning rather than just matching keywords.
3. Checking the query cache — If a similar question has been asked before, a cached result can return a faster response.
4. Finding candidate tables — The system identifies multiple candidate tables and selects the best match along with a confidence score.
5. Reasoning over schema relations — Here the system classifies intent (such as "trend" or "comparison") and identifies the relevant module (such as Sales Distribution), along with a confidence score.
6. Translating the question into SQL — The question becomes a validated, tenant-scoped SELECT query — meaning it is limited strictly to that organization's data.
7. Executing the query on the data lake — The query runs safely and relevant rows are retrieved.
8. Finalizing the response summary — Raw data is converted into a readable, business-friendly summary that highlights key numbers, trends, and insights.

How the Result Is Presented

Rather than just showing raw numbers, TatvaAI highlights insights — such as seasonality patterns, year-over-year growth percentages, or unexpected drops. A "Why this number?" option is also available, adding transparency about how the answer was derived.

TatvaAI also offers follow-up suggestions — such as "Which customers contributed most to revenue growth?" — so users can keep exploring without typing a new question from scratch.

What the Confidence Score Means

Every answer comes with a confidence percentage (such as 73% or 95%), indicating how sure the system is about its interpretation. This helps users judge how reliable a result is, and rephrase their question if needed for more precision.

Why This Approach Matters

Traditional BI tools require users to learn a query language first. TatvaAI's multi-step reasoning process removes that barrier, letting business users independently answer complex data questions — with both accuracy and transparency.

FAQ

Q 1. How does TatvaAI understand a question?

Through semantic embeddings and schema reasoning, not just keyword matching.

Q 2. Does TatvaAI show raw SQL to users?

The system builds validated SQL internally, but users see a simple summary.

Q 3. What does the confidence score indicate?

How sure the system is about its interpretation of the question.

Q 4. Does every query run from scratch?

No, similar questions can return faster results from cache.

Q 5. Does TatvaAI show data from other organizations?

No, all queries are tenant-scoped to your organization's data only.