Data Q&A Copilot Demo

What it is: Business users ask questions in plain English (“What were our top 10 products by margin last quarter in the US?”); the platform generates the right SQL, runs it, and returns answers, charts, and explanation.

Why it matters: Eliminates dependency on analysts, shortens decision cycles, and works across any cloud or database because everything is YAML-configured.

Where to use it: Executives, Sales, Marketing teams, Analysts, PMs, and ops teams who need self-service KPIs without learning SQL or BI tools.

What it is: An AI SQL assistant built on Open_Data_QnA to help engineers and analysts write, optimize, and debug SQL across heterogeneous platforms.

What it does: Converts natural language to draft SQL, suggests joins/filters/aggregates, explains & debugs queries, and adapts to multiple SQL dialects.

Why it matters: Gives sales and marketing teams instant insight into evolving trends while boosting both sales outcomes and analyst productivity.

What it is: Use the LLM pipeline to generate human-readable table and column descriptions across all databases.

What it does: Reads schema/keys/sample data, generates business-friendly descriptions and tags, and outputs YAML/JSON for catalogs or Git.

Why it matters: Reduces manual documentation, clarifies “what’s in the data,” and keeps docs versioned as code.

What it is: A unified query experience across multiple databases and clouds without re-platforming.

What it does: Resolves which systems to hit, generates/executes per-source SQL, joins results at the platform layer, and returns a coherent answer.

Why it matters: Delivers “data fabric” style insights while keeping data in place; new systems are added via YAML configs.

What it is: A governed analytics assistant that honors your identity/RBAC and DB security rules.

What it does: Integrates IAM/groups/RLS, ensures each user only sees permitted data, and generates policy-aware SQL from declarative configs.

Why it matters: Enables AI-powered analytics without bypassing compliance; policies live centrally in YAML.

What it is: Embed Open_Data_QnA as a chat widget inside internal portals, CRM, ERP, or custom apps.

What it does: Context-aware questions (“Show last 6 months’ tickets for this customer”) with the same multi-cloud backend in a lightweight UI.

Why it matters: Puts analytics where people work, improving adoption and creating a consistent AI layer across apps.

What it is: An AI assistant for profiling, validating, and comparing datasets during migrations.

What it does: Auto-generates profiling queries, compares source vs. target, and produces human-readable summaries of anomalies.

Why it matters: Speeds migrations, reduces manual reconciliation, and gives PMs clear quality summaries.

Examples:

• Travel/Transportation: “How many moves by city, truck size, and day of week last quarter?”
• Finance/Insurance: “Show net revenue, loss ratio, and claim frequency by product line and region.”
• Healthcare/Life Sciences: “Count patients by cohort, treatment arm, and visit schedule.”

Why it matters: Vertical-ready through YAML configs for data sources, LLMs, and SQL dialects — the platform stays the same.