The knowledge layer behind Claude, Copilot,
and whatever comes next.
Catalyst Learning trains healthcare workforce teams across the country, and the AI layer sitting on top of Salesforce needs to sound like Catalyst, not like a generic assistant. I build the RAG foundation and knowledge base first: a tool agnostic layer that captures your voice, your process documentation, and your sales playbooks, so Claude, Microsoft 365 Copilot, or any AI platform you adopt next can plug into the same governed source of truth instead of starting over.
Why AI tools alone aren't the foundation.
Three failure modes show up when AI tools get wired into Salesforce before the knowledge layer underneath them is built.
Picking a tool first locks you in early
Integrating an AI tool directly into Salesforce without a knowledge layer underneath means every prompt, permission, and workflow gets rebuilt from scratch if you switch platforms or add a second one later.
Generic AI answers don't sound like Catalyst
Off the shelf assistants answer in their own voice, not the tone your sales and operations teams use with healthcare clients. Without a grounded knowledge base, every AI answer needs manual editing to sound right.
Healthcare data raises the governance bar
Customer and company information has to stay protected while still being usable for automation. Without clear permissions and data governance built into the architecture, that trust is hard to guarantee.
The knowledge base comes before the integration.
I start with the knowledge base, not the integration. Your process documentation, sales playbooks, and internal writing get structured into a governed, chunked knowledge base built for retrieval, with permissions mapped to who should see what. That knowledge base becomes the grounded source of truth for RAG, so answers come back in Catalyst's voice and cite the actual document behind them, not a generic AI tone. On top of that foundation, I architect the Salesforce integration layer, evaluating Flow, native connectors, and middleware like Workato or Power Automate, to wire Claude and Microsoft 365 Copilot into your sales and administrative workflows through the same governed knowledge layer. Because the knowledge base is tool agnostic, swapping models, adding a second AI platform, or upgrading Copilot down the line means reconnecting to the same source of truth, not rebuilding it. Every workflow ships with documentation and a knowledge transfer session so your team owns the system, not just uses it.
What you get. Phase by phase.
Every phase ships with concrete deliverables you sign off on before the next begins. No vague "ongoing collaboration" hours, no mystery scope.
- Full audit of your current Salesforce environment, AI readiness, and existing documentation
- Inventory of process docs, sales playbooks, and internal writing that define Catalyst's voice
- Data governance and permissions mapping for healthcare sensitive customer information
- Recommendations report outlining integration options, architecture, and best practices
- Chunking and retrieval strategy built around your actual process and sales documentation
- Governed knowledge base structured for RAG, with source citations on every answer
- Voice and tone preserved directly from your existing content, not genericized
- Permissions layer built so sensitive customer data stays scoped to the right roles
- Evaluation of Salesforce Flow, native connectors, and middleware (Workato, Power Automate, Zapier, Make)
- Integration method recommended and configured for connecting AI tools to Salesforce data
- OAuth and secure API authentication wired in for every connection
- Repetitive administrative and sales workflows mapped for automation
- Claude and Microsoft 365 Copilot connected to the shared knowledge base through the integration layer
- Prompt management and AI usage governance documented for ongoing consistency
- Workflow automation configured for the highest value administrative and sales tasks
- Security review confirming confidential customer and company data stays protected
- Implementation roadmap and governance recommendations documented for your team
- Knowledge transfer session covering architecture, prompt management, and ongoing maintenance
- Guidance for adding future AI tools onto the same knowledge base without rework
- Async availability for optimization as usage patterns and tools evolve
Four weeks. Foundation to live.
Each week ships a working deliverable. Click any week to see exactly what lands in your environment by Friday.
Deliverables this week
- Full audit of your current Salesforce environment, AI readiness, and existing documentation
- Inventory of process docs, sales playbooks, and internal writing that define Catalyst's voice
- Data governance and permissions mapping for healthcare sensitive customer information
- Recommendations report outlining integration options, architecture, and best practices
Deliverables this week
- Chunking and retrieval strategy built around your actual process and sales documentation
- Governed knowledge base structured for RAG, with source citations on every answer
- Voice and tone preserved directly from your existing content, not genericized
- Permissions layer built so sensitive customer data stays scoped to the right roles
Deliverables this week
- Evaluation of Salesforce Flow, native connectors, and middleware (Workato, Power Automate, Zapier, Make)
- Integration method recommended and configured for connecting AI tools to Salesforce data
- OAuth and secure API authentication wired in for every connection
- Repetitive administrative and sales workflows mapped for automation
Deliverables this week
- Claude and Microsoft 365 Copilot connected to the shared knowledge base through the integration layer
- Security review confirming confidential customer and company data stays protected
- Implementation roadmap and governance recommendations documented for your team
- Knowledge transfer session covering architecture, prompt management, and ongoing maintenance
Catalyst, let's build the knowledge layer first.
A call to walk through the assessment approach, the knowledge base architecture, and how Claude and Copilot would plug into it. Happy to talk through hourly rate, availability, and scope on the call.