Free AI tool · 30-day trial · No credit card
Design a clean Salesforce data model
Most long-term pain in a Salesforce org traces back to early data-model decisions: a custom object built where a standard object already existed, master-detail used where lookup was needed (or the reverse), a many-to-many relationship modeled without a junction object, or an entity so overloaded with record types that no one can report on it cleanly. These choices are cheap to get right at design time and expensive to unwind after data and automation pile on top.
The core best practices are stable: prefer standard objects wherever they fit (they carry built-in functionality, reporting, and ecosystem compatibility); choose relationship types deliberately, because master-detail controls sharing, rollups, and deletion behavior; design for the report you'll need, not just the record you'll store; and think about data volume from day one, since large data volumes change indexing, skew, and archival strategy.
This tool turns those principles into a concrete starting model. Describe the use case, industry, and key entities, and it returns the standard objects to use, the custom objects that are genuinely needed, the relationships between them with types and rationale, and the governor-limit and large-data-volume considerations to design around.
Data Model Recommender
Get the right Salesforce object model — standard objects, custom objects, and key relationships.
🔒 Describe scenarios in general terms — please don't include confidential client names or personal data.
Best practices the model applies
- →Standard objects first — Account, Contact, Case, Opportunity, and industry objects carry free functionality custom objects must rebuild.
- →Relationship types chosen deliberately — master-detail for tight ownership and rollups; lookup for independence; junction objects for many-to-many.
- →Report-driven design — if a required report is awkward in the model, the model is wrong.
- →Volume awareness — objects expecting millions of rows need selective queries, indexed filters, and an archival plan.
- →Restraint on record types — process variation is real, but a record type per team is a smell.
How it works
Enter the use case, industry, key entities, and estimated record volume. The recommender maps entities to standard objects where possible, proposes custom objects only where justified, and specifies each relationship with its type and the reasoning — plus the limit considerations (query selectivity, data skew, rollup constraints) that follow from your volumes.
When to use it
At the start of any new implementation or major feature, when reviewing a proposed design from a partner, or when documenting an inherited org and deciding what to refactor. It's a first-draft model to pressure-test with your data and reporting requirements — not a substitute for that pressure-testing.
Frequently asked questions
Should I use standard or custom objects in Salesforce?
Standard objects wherever they reasonably fit — they include reporting, automation hooks, mobile support, and AppExchange compatibility that custom objects must rebuild. Create custom objects for genuinely distinct entities with no standard equivalent.
When should I use master-detail vs lookup relationships?
Master-detail when the child's lifecycle belongs to the parent (cascade delete, inherited sharing, rollup summaries needed). Lookup when the records have independent lifecycles or the relationship is optional. The choice affects sharing and deletion behavior, so it deserves deliberate thought.
How does data volume affect data model design?
Above a few million rows per object, query selectivity, ownership skew, and reporting performance become design constraints: you need indexed filter fields, skew-aware ownership, and often an archival strategy. Designing for volume after go-live is far more expensive.
Related tools
Try it now — free for 30 days
No credit card required. Part of Reliaarch's suite of 15 AI tools for Salesforce professionals.
Use the Data Model Recommender →