From connected data to analysis your team can review and reuse
Notellect aligns business meaning first, gives teams a DuckDB and Jupyter workbench for real investigation, and keeps every claim tied to evidence so reviewed work becomes reusable context.
Dataset Explorer
Connect source types, define semantic models, and keep facts plus glossary close to the data.
Data Workbench
Run DuckDB and Jupyter analysis, mix Python and SQL, and capture draft claims with support.
Insight Reports
Build report sections from claims and tables without stripping away their support.
Semantic + trust layer
- Semantic models and business terms
- Evidence attached to each claim
- Draft vs trusted review states
- Trust Center reuse across workflows
Semantic layer first
Map raw schema into business models, facts, glossary terms, and example queries so analysis starts from aligned meaning.
DuckDB + Jupyter core
Run fast SQL and Python work in one notebook workflow instead of scattering analysis across query tabs, code notebooks, and slides.
Trust built into the workflow
Generated claims stay draft until reviewed, with support visible and promotion into Trust Center only when the team chooses reuse.
Four surfaces that turn analysis into trusted team knowledge
Most AI products stop at the answer box. Notellect covers the actual places analytical teams work: connected data, DuckDB and Jupyter analysis, reports, and reusable trusted context.
Dataset Explorer
Connect databases, files, workbook tables, and remote file paths, then turn raw schema into business-ready models, facts, glossary terms, and example queries.
Data Workbench
Run DuckDB-backed notebook sessions with Jupyter so Python, SQL, outputs, and draft claims stay in one analytical thread.
Insight Reports
Build report sections from claims, tables, and commentary so supported conclusions and provisional ones remain distinguishable.
Trust Center
Promote accepted facts, glossary terms, execution results, and claims so the next cycle starts from reviewed context instead of a blank page.
Align business meaning before analysis starts
Garbage in, garbage out. Notellect uses a semantic layer plus data knowledge so raw schema, business terms, facts, and example queries are aligned before generation begins.
Connected sources land in a fast DuckDB analytical runtime, so teams can investigate across files, databases, and workbook data without waiting on pipeline work.
Garbage in, garbage out. Faster generation does not help if every cycle starts with raw tables and inconsistent business definitions.
Notellect keeps semantic models, facts, glossary, and example queries close to the data so analysts can focus on judgment instead of schema archaeology.
Investigate the margin drop, keep the supporting steps visible, and promote only what the team wants to reuse.
Unit economics worsened in the enterprise segment.
Discounting increased in two regions after pricing tests.
Gross margin should normalize next quarter if spend mix resets.
Evidence
- Executed SQL step
- Notebook output table
- Pricing glossary term
Trusted context
Powered by DuckDB and Jupyter, built for real analytical work
Notellect combines a fast DuckDB analytical runtime with a Jupyter notebook workbench. Teams can query connected sources, run Python and SQL in one place, and keep outputs close to the claims they support.
Run Python and SQL together in a Jupyter notebook backed by DuckDB.
Start from semantic models, raw tables, or workbook data in the same session.
Work across PostgreSQL, MySQL, SQLite, CSV, Parquet, JSON, Excel, and remote file transports.
Export useful results back into workbook tables and carry claims forward to review.
What keeps generated analysis reviewable
A generated conclusion is only useful if the next reviewer can still inspect what supports it, what is still draft, and what has already been trusted.
Evidence
Claims can link to executed notebook results, upstream facts, glossary terms, and prior claims instead of standing alone.
Draft by default
Generated claims stay separate from trusted team context until someone reviews and promotes them.
Promotion preview
Before adding work to Trust Center, Notellect can show the dependency tree of claims and execution results that come with it.
Scoped reuse
Trusted context can be kept at workspace, datasource, or model scope so reuse stays intentional.
Trust is explicit, reviewable, and reusable
Claims produced in the workbench do not become team knowledge automatically. Reviewers can inspect supporting facts, preview what promotion pulls in, and keep draft and trusted context separate.
Draft and trusted states stay visibly separate.
Claims can carry glossary, fact, execution result, and claim dependencies.
Promotion preview shows what will be added before Trust Center reuse.
Trusted context can be scoped to workspace, datasource, or model.
See the product on a real analytical workflow.
We will map Dataset Explorer, Workbench, Insight Reports, and Trust Center to one recurring analysis process.