Mud CakeBring in data
Upload spreadsheet data, connect existing SQL tables, or extract structured information from business documents. Multiple sources, one governed platform.
traced end to end
Governed business-data workflows
Turn the data your business already has into repeatable business workflows.
Mud Cake combines spreadsheets, enterprise SQL data, and other business sources into governed transformation workflows that perform calculations at scale and produce reusable datasets. No one-off scripts. No sprawling calculation spreadsheets. No user-authored SQL.
Workflow cataloglive
| Workflow | Rows | Source |
|---|---|---|
| Commission Calc | 12,480 | spreadsheet+SQL |
| Pricing Analysis | 8,200 | transform |
| Margin Report | 3,100 | transform |
Confidence
98%
validated before materialization
Lineage
- transform operations
- 6
- user SQL
- 0
- rows per dataset
- M+
- chained workflows
- ∞
- Spreadsheets, SQL, and documents
- Millions of rows, governed
- Tenant-isolated by design
- Full data lineage
- Durable workflow chaining
The problem
Your business runs on spreadsheets and scripts, not governed workflows.
Real business calculations live in Excel files, SQL scripts, manually assembled exports, and person-dependent processes. The logic works, but it is not repeatable, not governed, and does not scale when teams or data grow.
Spreadsheet sprawl
Important calculations live in scattered Excel files. One wrong lookup, one stale reference, and the numbers are wrong.
No repeatability
Every month, someone rebuilds the same calculation from scratch. No versioning, no audit trail, no way to know what changed.
Data lives in silos
Spreadsheet data, SQL tables, and document extracts sit in separate worlds. Combining them means manual exports, pastes, and hope.
Logic is person-dependent
When the person who built the spreadsheet leaves, the calculation leaves with them. No documentation, no handoff.
No governance at scale
Who changed what? Which version is correct? Where did the number come from? Spreadsheets cannot answer these questions.
Mud Cake solves all five
Move important recurring calculations into a controlled platform without taking flexibility away from business teams. Keep what makes spreadsheets useful. Add repeatability, scale, governance, and traceability.
Capabilities
One platform, data in to calculated dataset out.
Each capability is a governed, tenant-scoped surface, not a bolted-on tool.
Organize as datasets
Give business data a governed, inspectable home. Browse, preview, inspect schema and activity, trace lineage, and organize into nested folders.
Transform, no SQL
Build repeatable, versioned recipes with select, rename, calculated, lookup, filter, and aggregate steps. Look up against other datasets or your existing SQL tables. The backend generates the SQL, users never author it.
Automate & chain
Schedule runs hourly to monthly. A successful run durably triggers downstream workflows, with run history and causality tracking.
Govern & isolate
Customer and tenant isolation enforced server-side. Backend authorization is the authoritative boundary, never the UI, never a parsed name.
Scale to millions
Datasets and transformations handle millions of rows. Calculations, lookups, filters, and aggregates all run at production scale, on schedule, every time.
How a workflow works
Data in. Logic applied. Calculated dataset out.
A Mud Cake workflow takes business data from multiple sources, applies repeatable transformations, and produces a new dataset that can itself feed additional workflows.
Spreadsheets
SQL data
Other datasets
Lookup + Calculate + Filter + Aggregate
Versioned, repeatable transformation steps
Calculated dataset
Materialized output with schema, storage binding, and lineage
Another workflow or downstream system
Output datasets can feed additional transformations or be consumed externally
Use cases
An engine for real business processes, not just a place to park data.
Mud Cake runs the calculations your teams already perform: data in, governed transformation, calculated dataset out, scheduled and chained with full lineage.
Sales ops
Commission calculation
Data: attainment spreadsheets, rate cards, SQL employee data. lookup rep to quota and tier, calculated tiered rate, aggregate by rep and period.
Out: commission payout dataset, monthly, chained to payroll
Sales ops
Pricing & margin analysis
Data: price lists, competitor sheets, SQL cost and product data. lookup standard cost, calculated margin %, filter outliers, aggregate by SKU and region.
Out: pricing analysis dataset, weekly
Finance
Invoice & AP processing
Data: vendor invoices, SQL POs and goods receipts. lookup PO match, filter exceptions, aggregate by vendor and period.
Out: AP dataset, daily, chained to approval
Finance
Revenue recognition
Data: contracts, billing schedules, SQL GL data. lookup performance obligations, calculated recognized vs deferred, aggregate by period.
Out: recognition dataset, monthly close
Operations
Inventory reconciliation
Data: cycle-count sheets, SQL stock data, supplier ASNs. lookup system-of-record, filter variances, aggregate by location and SKU.
Out: reconciliation dataset, daily
Reporting
Regulatory & ESG reporting
Data: compliance submissions, ESG data sheets, SQL entity data. lookup reporting taxonomy, calculated metrics, aggregate by entity and period, full lineage for audit.
Out: reporting dataset, quarterly
How it works
From business data to calculated dataset.
A workflow can start with a spreadsheet, SQL data, another dataset, or extracted document data. Each step is governed, versioned, and auditable.
See the full flow- 01
Bring in your data
Upload spreadsheet data, connect existing SQL tables, or extract structured information from business documents.
- 02
Organize as datasets
Give business data a governed, inspectable home with schema, preview, and lineage.
- 03
Build your logic
Create transformations with selection, renaming, calculated columns, lookups, filters, and aggregations, no SQL required.
- 04
Produce calculated datasets
Materialize results into datasets that can become inputs to additional workflows.
- 05
Automate the process
Run workflows manually, on schedules, or from upstream data-refresh dependencies.
- 06
Govern everything
Maintain lineage, versions, execution history, tenant boundaries, and traceability throughout.
Security & governance
Isolation is enforced, not promised.
Mud Cake is built for multi-tenant SaaS from the ground up. Customer and tenant context are part of the operating model, and backend authorization, not UI filtering, not a parsed physical name, is the authoritative security boundary.
Read the governance modelNo user-authored SQL
Trusted backend code, validated identifiers, server-injected tenant and customer predicates.
No cross-tenant access
Selection, lookups, dependencies, schedules, and execution are all tenant-scoped.
Full lineage
Dataset-to-dataset provenance on every publish and run, trace end to end.
Fail-closed by design
Ownership in a DTO is never sufficient authority for a destructive act.
What's next
A governed workflow platform, evolving.
Mud Cake ships against a product-owner-controlled roadmap. Today's foundation is data ingestion, repeatable transformations, and durable automation. What is next is richer data sources, more operations, and deeper integrations.
Shipping now
Data to dataset workflows
Bring in spreadsheets, SQL data, and documents. Build repeatable transformations. Schedule and chain downstream. Govern with full lineage and tenant isolation.
In progress
More data sources & operations
External SQL-backed datasets, managed performance indexes, and additional transformation operations to handle more business scenarios.
On the roadmap
Deeper integrations
More ways to bring data in, more ways to get calculated datasets out, and more flexibility for complex business workflows.
Stop rebuilding calculations. Start running workflows.
See Mud Cake turn your business data into lineage-traced, tenant-isolated, calculated datasets your teams can rely on.