The unit of work is no longer a pipeline. It is a governed business outcome.
Turn a business objective into entities, metrics, grain, freshness, controls and expected outputs.
Business objective
Metrics and grain
Quality expectations
Delivery contract
A copilot helps an engineer write SQL. Facthory accepts responsibility for the analytical outcome: discovering sources, deriving the model, generating code, executing tests, reconciling results, packaging production artifacts and escalating only the decisions that require human authority.
Inspect schemas, APIs, files, keys, update patterns and samples across fragmented enterprise systems.
Infer business entities, ownership of truth, metric definitions and relationships hidden inside source-system structures.
Generate extraction and transformation logic as inspectable, versioned production code rather than opaque AI output.
Create technical tests, business reconciliations and quality gates that prove outputs before promotion.
Deliver governed metrics, analytical models, dashboards, APIs and AI-ready datasets from the same business specification.
Observe failures and drift, diagnose causes, generate repairs and route production changes through policy.
Think of Facthory as a compiler for enterprise analytics. Business intent is the high-level language. SAP, Dynamics, Salesforce, databases, APIs and spreadsheets are the source languages. Facthory constructs a semantic intermediate representation of entities, mappings, metrics, lineage, constraints and quality rules, then compiles it into the code and runtime artifacts required by the customer's environment. The generated implementation can change as technology changes. The validated business meaning remains durable. That separation lets one analytical specification evolve across warehouses, lakehouses, BI tools, AI systems and future execution engines without starting the understanding process again.
Create connectors, transformations, models, tests and orchestration artifacts.
Execute bounded workloads and reconcile outputs against deterministic expectations.
Package approved artifacts into the customer's governed production environment.
Detect drift and failure, diagnose impact and generate governed fixes.
The same governed data product can serve executives, analysts, operational applications, machine-learning systems and AI agents. Facthory turns analytics engineering into shared enterprise infrastructure rather than another one-off reporting project.
Deliver reconciled metrics, dashboards and analytical models around real business questions.
Produce governed datasets and context layers for machine learning, copilots and enterprise agents.
Package semantics, lineage, quality and delivery logic as durable products instead of disposable pipelines.
Apply the same autonomous engineering engine across the questions enterprises repeatedly struggle to operationalize.
Compile margin, cash, working capital and profitability questions into reconciled analytical products.
Build trusted performance models across production, service, quality and operational systems.
Unify customer, sales, pricing and delivery data into governed revenue intelligence.
Create governed analytical datasets that agents and models can safely consume downstream.
The strategic asset is not the generated SQL. Code can be regenerated. The durable asset is the validated understanding of how business concepts map onto enterprise systems, together with their lineage, tests, decisions and operating history. Every completed data product strengthens that semantic foundation. Future requests begin with more known entities, mappings, metrics and trusted rules, turning analytics engineering from repeated project work into a compounding enterprise capability.
Stop asking engineers to translate every business question by hand. Let the platform compile intent into trusted analytics.
Choose one valuable analytical outcome with clear users, decisions and acceptance criteria.
Grant governed access to the systems and files likely to contain the required evidence.
Let agents discover, model, generate, execute and test the complete analytics implementation.
Review semantic assumptions, reconciliations, lineage and unresolved decisions before production promotion.
Run continuously with quality gates, observability, drift detection and governed repair.
Formalize metrics, grain, freshness, acceptance criteria and delivery requirements before code generation.
Produce inspectable implementation artifacts that can be reviewed, tested, versioned and redeployed.
Verify schemas, counts, constraints, relationships, distributions and business reconciliations with executable tests.
Trace business outputs through transformations to source systems, fields and validation evidence.
Run generated code inside isolated, permission-scoped environments with explicit resource and action boundaries.
Escalate material semantic assumptions and production changes to accountable business and data owners.
Detect upstream schema, volume and behavioral changes before they silently corrupt downstream analytics.
Diagnose failures, propose corrected artifacts, rerun validation and request approval for safe promotion.
Facthory can separate reasoning and control from the execution plane. Generated workloads can run inside customer-controlled cloud or private environments, close to governed enterprise data, with scoped credentials, isolated execution and explicit production approvals. The goal is maximum automation without creating a new uncontrolled copy of the enterprise data estate.
Enterprises already have the data. The bottleneck is turning fragmented sources into governed, reusable business outcomes.
say data silos hinder innovation
feel ready for AI revenue
applications in the average enterprise
cite integration as an AI challenge
Give Facthory one high-value business question and access to the relevant systems. Let the platform discover, engineer, validate and operationalize the data product behind the answer.
