Your Salesforce Org,
Explained for AI
A living knowledge graph that merges metadata + transactional history, giving AI copilots the full picture of how your business really runs.
Why Metadata Alone Isn't Enough
Most "data dictionaries" only map metadata: fields, objects, flows, and Apex references. That shows how Salesforce is structured, but not how it behaves.
Without real change data, you're guessing: Which fields actually drive automation? Which processes really run most often? Which dependencies matter in practice?
Processity Data Dictionary combines your metadata graph with Data History events - actual records of field changes, user actions, and process executions - to reveal how your Salesforce org truly works, not just how it's wired.
Metadata-only = theoretical structure
With Data History = what actually happens
With Processity (Data History + Process Context) = why it happens and how to improve or automate it
Metadata + History = Reality
1. Metadata foundation
We extract every object, field, Apex class, trigger, Flow, and permission set: your complete structural model.
2. Behavioral enrichment
We overlay that model with Data History change logs: each create, update, and delete event across your Salesforce org. These events expose which fields change together, which triggers fire most often, and which automations actually execute.
3. Knowledge graph for AI
We fuse both layers into a machine-understandable graph that AI copilots can safely query through the Model Context Protocol (MCP). The result: context-rich, reality-based reasoning instead of static metadata analysis.
What You Can See Now
| Question | Traditional Metadata Tools | With Data History (Real Events) | With Processity (History + Process Context) |
|---|---|---|---|
| What touches Opportunity.StageName? | Lists triggers, flows, and formulas that reference the field. | Adds frequency and recency - which records actually change and which automations fire. | Maps the end-to-end journey of a deal - reveals the real process path, automation chains, and dependencies that drive StageName transitions. |
| Where should we automate next? | Guesses from metadata or flow names. | Surfaces high-frequency field changes and co-changing patterns. | Identifies full recurring processes (e.g. renewal → contract → invoice) with confidence scores, ready for automation or AI recommendations. |
| What breaks if we change this field? | Static dependency tree: formulas, Apex, and flows. | Adds impact magnitude: how often those dependencies are actually exercised. | Quantifies business risk showing which processes, teams, and customers are affected by the change in practice. |
| How does data really flow through my org? | Not visible. Only object relationships. | Partial view: field-to-field changes. | Full lineage: metadata, data changes, and process flow combined into a single knowledge graph that shows how information moves through the business. |
| What's my AI copilot seeing? | Nothing: metadata is static text. | Structured graph of data changes. | Dynamic knowledge graph with semantic process context. AI can reason about cause, effect, and intent behind every change. |
The Big Picture
- •Metadata tells you what could happen.
- •Data History shows you what did happen.
- •Processity explains why it happened and what to do next.
That final layer - process context - turns your data dictionary into a knowledge graph for AI, enabling LLMs, copilots, and automation engines to operate with full situational awareness.
Key Capabilities
- •Hybrid metadata + event integration: every object, field, flow, and code dependency enriched with live change data.
- •Process discovery from reality: identify the business workflows your users actually follow.
- •AI-ready graph interface: copilots can query and reason over real usage, not hypothetical design.
- •Impact & automation analysis: quantify the risk or ROI of a change before you deploy.
- •Secure AI gateway (MCP): read-only, token-protected, audit-logged access for trusted assistants.
Built for AI Enablement
Large-language models and internal copilots reason better when they understand both structure and behavior.
The Processity Data Dictionary provides that context:
- •A unified, graph-based schema of your Salesforce org.
- •Enriched with actual Data History events.
- •Delivered through a safe API so AI can query, summarise, and act - without ever touching production data.
It's the knowledge graph for AI-driven Salesforce operations.
Who It's For
Teams who need to understand the full picture of how their Salesforce org works - not just how it's configured.
Get full-fidelity impact analysis that combines metadata structure with actual usage patterns. Understand which dependencies matter in practice before making changes.
Debug complex Apex/Flow interdependencies with a complete view of how code, automation, and data changes interact in your org.
Identify process bottlenecks by seeing which workflows actually run most often and where automation opportunities exist based on real behavior.
Train copilots on real organizational context with a knowledge graph that merges structure and behavior, enabling AI to reason about your business processes.
Security & Architecture
- •OAuth2 + Azure Key Vault for token security.
- •Neo4j graph backend with read-only query validation.
- •API gateway isolates AI assistants from credentials.
- •Optional on-prem or VPC deployment.
Bridge the gap between how your org is built and how it behaves.