Connect your systems. Put AI to useful work.
Start with the task your team needs to complete. Build on Salesforce configuration where it fits, and use AI for specific steps whose output your team can check.
Build on the system of record
Our core delivery experience is in Sales, Service and Experience Cloud. Integration and data work connect the records the team needs. Our wider Salesforce work includes Marketing Cloud and Nonprofit Cloud.
Use AI for the right parts of the job.
AI can help interpret documents, classify requests and prepare drafts. Use application code for calculations, required-field checks, permissions and record updates. Choose the simplest combination that meets the agreed requirements.
Our policy-portal work followed this separation: the model handled the conversation, while application code assembled documents. Each part had a defined job that could be tested.
Test the exceptions before going live.
Check complete submissions alongside missing attachments, conflicting values, outdated policies and denied access. Test misleading instructions inside uploaded documents, failed connections and repeated requests. Agree when the system should proceed, ask for help or stop.
Repeat these checks when a model, prompt, rule or integration changes. Passing a demo is a starting point; the agreed test cases determine whether the workflow is ready.
Control what AI can change.
Give each automated step a defined set of actions: prepare a draft, flag missing information or create a review task. The application should enforce permissions and validate updates. A prompt alone is not an access control.
For actions that need approval, record who may approve, what they are approving and what happens if approval is refused. Confirm interfaces, licences and data flow during discovery before promising an integration.
Where we have delivery experience
Our AI work includes a policy knowledge portal and internal tools for code review. Our Salesforce experience centres on Sales, Service and Experience Cloud, with integrations and custom workflows built around each team’s needs.
Choose the right first conversation
Start by reviewing one workflow and the systems behind it. If your Salesforce team has at least 20 users, ask about a scoped Health Check to identify configuration, data and workflow priorities.
Make the business rules explicit.
Before connecting AI, agree what the records mean: when an application is complete, which source wins when values conflict, which policy version applies and who owns an exception. Identify the approved sources and the information each step needs.
Build on existing CRM configuration and integrations where they fit. Start with one workflow; introduce extra agents or custom components only when the work requires them.
Example: from application to approved CRM update.
Example workflow. This describes a proposed design, not a completed client project. An application arrives with a missing attachment and conflicting values. The proposed workflow prepares the record and routes the exception for review; it does not make the funding decision.
1. Receive: An email or document packet arrives.
2. Prepare: Extract proposed fields into a draft record.
3. Check: Check required documents and flag conflicts.
4. Review: A staff member resolves exceptions and approves the update.
5. Update: Write only the approved changes to the CRM.
6. Record: Keep the sources, changes and approval record.
Agree duplicate checks, permitted updates and failure recovery before implementation.
Where should you start with AI?
Review one Salesforce workflow, its data and the people responsible for it. Our ten-question self-assessment gives you practical next steps, with no email required.