Transform scanned documents into AI-ready data
Agentforce is only as good as the data it's grounded on. We turn PDFs, scans, and attachments into governed records in Data Cloud.
MuleSoft Anypoint IDP or Data Cloud Document AI: choosing the right path on volume, cost, governance, and where you already sit on the Salesforce platform.
The opportunity hiding in your documents
Most enterprise data that would make an agent genuinely useful is still locked away. The pattern is the same across insurance and beyond.
The agents are ready, the data isn't
The platform has the agents, the LLMs, and the Trust Layer. What's missing is the data, still locked in PDFs, scans, and email attachments. Grounding agents means freeing that data first.
The data exists, it just isn't structured
Loss runs, claim attachments, submissions, and policy documents already hold the answers. They simply aren't in Salesforce as queryable, governed records your agents and reps can use.
The extraction engine is swappable
The hard part is the pipeline around it, not the engine inside it. Choose IDP or Document AI, and change your mind later. The front door and landing zone stay constant.
One pipeline. A swappable engine.
Whichever extraction engine you pick, the surrounding architecture looks the same, so the choice stays contained and low-risk.
Document source
Upload, email, fax intake, or integration.
MuleSoft front door
Ingestion, normalization, and splitting under per-file limits.
Extraction engine
IDP, Document AI, or both. This is the swappable piece.
Data Cloud Data Lake Object
Structured output, governed by the Einstein Trust Layer.
Salesforce + Agentforce
Records, summaries, and grounded agent context.
Estimate your document automation ROI
Describe your workload in plain language. Our assistant gathers a few details, then estimates your savings versus manual handling, and tells you whether MuleSoft IDP or Data Cloud Document AI is the cheaper engine for your document profile.
Your estimate appears here
Answer a few quick questions on the left and we'll show your projected annual savings and the cheaper extraction engine.
Three credible answers
The right one depends on volume, cost structure, governance, and where the customer already sits on the platform.
MuleSoft Anypoint IDP
The mature, production-proven path
Best fit when
- Already a MuleSoft customer with vCore capacity
- High, predictable volume where batch is acceptable
- Classification across many document types
- Pipeline feeds multiple systems beyond Salesforce
Cost model Consumption via Automation Credits at roughly 30 credits per page (Automation Credits 3.0).
Data Cloud Document AI
Salesforce's native, Agentforce-ready path
Best fit when
- Committed to Data Cloud as the Agentforce foundation
- Moderate volume or relatively small files
- Native Agentforce grounding without extra pipeline work
- Unified governance under the Einstein Trust Layer
Cost model Consumption via Data Cloud credits at roughly 750 credits per MB under Intelligent Processing.
Hybrid / Phased
For mixed workloads and transitions
Best fit when
- Mixed workload profiles across document types
- IDP for high-volume, complex-classification work
- Document AI for newer Agentforce-grounded use cases
- Customers mid-transition to Data Cloud
Predictable economics you can defend
Each engine meters on a different unit. Once you know which lever drives your cost, modeling becomes straightforward, producing a defensible estimate and confident, predictable budgeting from day one.
MuleSoft IDP
Leveraged by page count
Pages × 30 credits × your rate
Data Cloud Document AI
Leveraged by file size, not pages
Megabytes × 750 credits × your rate
Four questions decide the architecture
The customer's answers determine which path fits. Most decisions are clear once the document profile is actually measured.
01
Where's the platform center of gravity?
If MuleSoft is the orchestration backbone and Data Cloud is just provisioned, lean IDP. If Data Cloud is the platform of record and Agentforce is near-term, lean Document AI.
02
What's the document volume and profile?
High-volume batch of complex multi-form documents favors IDP's classification and predictable per-page cost. Moderate volume, simpler types, or real-time agentic use favors Document AI.
03
What's the governance posture?
For PHI, PII, or regulated data where audit, BAA coverage, and unified governance matter, Document AI's path through the Einstein Trust Layer simplifies the compliance story.
04
What's the AI roadmap?
Building toward Agentforce as the core agentic platform aligns with Document AI. Using Salesforce as one of several downstream systems favors IDP's broader ecosystem integration.
The full unstructured-to-structured lifecycle
PS Advisory works with insurance carriers, MGAs, and reinsurers on the whole journey, from the question to a production pipeline.
Discovery & architecture
Profile your documents across volume, size, type, and scan quality, map them to the right engine, and produce a cost model grounded in your contracted rates, not list pricing.
Pilot & validation
Stand up a contained 60–90 day parallel run that benchmarks accuracy on real documents, validates operational behavior at volume, and produces AE-confirmed cost figures.
Production implementation
Build the MuleSoft pipeline, the Data Cloud DLO schema, the Salesforce records, the Agentforce grounding, and the runbook, all with insurance-specific document patterns.
Optimization & roadmap
As Document AI matures and volume grows, the Year 1 choice may not fit Year 3. We help you measure, adjust, and migrate without redoing the pipeline.