Early Stage · Building in the Open

AI transformed coding.
Data analytics is next.

We're building the intelligence layer between dirty databases and real answers. Not another text-to-SQL tool—a reasoning engine that thinks like a senior data analyst.

The AI Gap in Data Analytics

Every other technical role got an AI co-pilot. Data analysts are still waiting.

Software Engineering

Vibe coding changed everything

Chat → IDE → Terminal Agent → Desktop App
Engineers describe intent, AI writes code
AI became the main driver, human is co-pilot
10x productivity gains are real
Data Analytics

Still stuck

Text-to-SQL tools assume clean, documented data
Chat BI products need pre-built semantic layers
Drag-and-drop UIs avoid SQL but don’t solve the real problem
No tool understands dirty data like analysts do

The Real Problem

Every data intelligence product assumes clean data. But the hard part—the part that consumes 80% of an analyst's time—is everything before the analysis.

80%of time spent

“Dirty Work”

Finding the right table among duplicates & legacy data
Tracing data pipelines to understand discrepancies
Learning business terminology & company-specific rules
Deduplicating results from improper joins
Validating numbers against business expectations
Communicating with DBAs & other teams to confirm sources
20%

Actual Analysis

Writing the final query, building the dashboard, delivering the report.

Key Insight

Existing products optimize the 20%. We're tackling the 80%.

From dirty data to clean data—that's the real foundation for enterprise AI. Data analysts are the ones who build that foundation today. We're building the tool that accelerates that process.

Three Pillars

We're not building a faster SQL writer. We're embedding the reasoning process of an experienced data analyst into AI.

01

Find the Right Source

Databases are messy. Duplicate tables, legacy pipelines, redundant columns. We build a knowledge graph of your entire database so the AI knows which sources to trust.

Automated reliability scoring for every table and view
Lineage tracking across data pipelines
Legacy and duplicate detection
02

Build the Right Logic

SQL is easy. Business knowledge is hard. We embed public domain expertise and company-specific rules so the AI reasons like a senior analyst, not a code generator.

Industry-specific terminology and conventions
Company-contributed private knowledge base
Business rule-aware query planning
03

Validate the Result

Generating SQL isn't enough. A real analyst checks for duplicates, compares against known benchmarks, and flags when numbers don’t make sense. Our AI does the same.

Duplicate detection and join validation
Cross-reference with industry benchmarks
Confidence scoring for generated results

We're building in the open

No demos yet. No polished product. Just a clear problem, a strong team, and a vision we believe in. If this resonates, we'd love to talk.