Business context for
agentic data pipelines
Autopilot Spec turns business requirements into a structured specification of business logic, domain expertise, and validation criteria, ready for your team to review and your data engineering agents to use.
Trusted by leading organizations globally
From scattered business logic to an agent-ready data pipeline spec
Autopilot Spec turns data discovery into structured business context, so agentic data pipeline development starts from explicit business requirements and decisions.
Resolve ambiguity before your data engineering agent writes code
Surface conflicting definitions, missing business rules, edge cases, and unresolved decisions before they become transformation logic in your data pipeline.
Model the scope, metrics, entities, and relationships
Autopilot Spec turns business artifacts into a structured business knowledge bundle covering the domain, use cases, KPIs, business entities, attributes, and relationships the data model needs to represent.
Generate a business specification for stakeholder review
Gain a business specification that captures the project scope and agreed business logic. Send it to domain owners before the build so the coding agent starts from the reality of how your organization operates.
Turn requirements into testable validation criteria
Autopilot Spec generates concrete business questions and answers that can be calculated from the finished data model. Use those answers as validation targets to test whether the generated pipeline matches the business requirements, not simply whether the SQL runs.
Package business context for your data engineering agent
Autopilot Spec packages approved business context and validation targets as agent-readable Markdown. Your data engineering agent can use it to build staging, intermediate, and mart layers that reflect how your business operates.
The data pipeline spec to start your project right
Project scope
What we're building, what's explicitly out, and what success looks like.
Reconciliation targets
Real figures and expected answers pulled from your inputs - revenue totals, customer counts, metric definitions - that the pipeline validates against at every step.
Business domains
Your industry, business model, org structure, and the terminology your team actually uses.
Use cases
The exact questions your stakeholders need to answer - everything else is built to serve these.
Processes
How your business actually operates: quote-to-cash, month-end close, order management.
KPIs
Your operational and financial metrics, with their definitions - not just their names.
Source systems
Every ERP, CRM, and platform involved, with modules, versions, and ownership.
Business entities
The core objects your business reasons about: customers, products, accounts, invoices.
Business rules
The constraints that make your data yours: intercompany exclusions, FX logic, fiscal boundaries.
Start with your business documents
and data engineering assets
Frequently Asked Questions
Autopilot Spec is a spec builder for data engineering agents. It turns business requirements from meetings, dashboards, and systems of record into a specification that captures the business context, decisions, and validation targets agents need before building data pipelines.
Autopilot Spec can process meeting transcripts, emails, documents, dashboard screenshots, spreadsheets, schemas, SQL files, and existing data models to extract business requirements, metrics, rules, and validation targets for agentic data pipeline development.
When requirements are incomplete or ambiguous, Autopilot Spec identifies missing information and asks you targeted questions to clarify business logic with your stakeholders before generating specifications.
Autopilot Spec generates structured specifications including business rules, data requirements, entities, relationships, metrics, validation criteria, and documentation needed for pipeline development.
Autopilot Spec gives data engineers and their agents a shared source of business context, including business requirements, source-system rules, and validation targets, so they can build accurate data pipelines with fewer corrective iterations.
Yes. Generated specifications can be reviewed, refined, and validated by business and technical teams before moving into data pipeline development.
Spec before you build
Turn business knowledge into a spec your data engineering agent can build from.