DEV Community: Williams

Salesforce AI vs. Your Own AI Stack: RAG, LLMs & Enterprise Architecture

Williams — Wed, 16 Sep 2026 11:37:56 +0000

Should your enterprise AI architecture live entirely inside Salesforce—or should Salesforce connect to your own AI stack?

In Episode 2 of the Case Rezolver Podcast, we break down one of the biggest architecture decisions facing Salesforce customers today: native Salesforce AI vs. bringing your own AI stack.

We explore how enterprises can keep Salesforce as the system where service teams work while maintaining flexibility over the AI infrastructure underneath—including LLMs, vector databases, RAG, orchestration layers, private cloud infrastructure, and enterprise knowledge sources.

In this episode, we discuss:

• Native Salesforce AI vs. external enterprise AI architecture
• Why “native to Salesforce” doesn’t have to mean AI vendor lock-in
• How Salesforce, Case Rezolver, RAG, vector databases, and LLMs can work together
• How Retrieval-Augmented Generation (RAG) uses enterprise knowledge to improve AI responses
• Using OpenAI, Azure OpenAI, Anthropic, or privately hosted LLMs with Salesforce
• How abstraction and orchestration layers reduce AI integration complexity
• Enterprise AI security, permissions, data residency, logging, and data retention
• Why permission-aware retrieval matters for AI security
• How model selection affects enterprise AI cost
• Why different AI tasks may require different models
• How enterprises can design an AI architecture that can evolve as models change

We also walk through a real-world case-resolution architecture:

Salesforce → Case Rezolver → Retrieval / Enterprise Knowledge → LLM → Grounded Recommendation → Salesforce Agent Workflow

The goal isn’t to argue that native Salesforce AI or an external AI stack is universally better.

The better question is:

What combination of Salesforce, enterprise data, retrieval, orchestration, security controls, and AI models gives your organization the right balance of user experience, intelligence, security, cost, and flexibility?

If you're a CIO, CTO, Salesforce Architect, Service Cloud leader, enterprise AI architect, or technology leader evaluating Agentforce, RAG, LLMs, or AI-powered customer service, this episode will help you think through the architecture decisions behind production enterprise AI.

About Case Rezolver

Case Rezolver is a Salesforce-native AI case-resolution assistant designed to help service teams use enterprise knowledge, historical case resolutions, and AI to resolve customer issues faster—while giving enterprises flexibility over their underlying AI architecture.

Subscribe for upcoming episodes covering enterprise AI, Salesforce, RAG, AI agents, knowledge management, service automation, and intelligent case resolution.

Salesforce #SalesforceAI #EnterpriseAI #Agentforce #RAG #LLM #GenerativeAI #ArtificialIntelligence #ServiceCloud #AIArchitecture #SalesforceArchitect #CustomerServiceAI #AIAgents #VectorDatabase #CaseRezolver

Check out more: Mirketa

How Granite Recovery Automated Patient Pre-Intake with Elixir Contact Center & KIPU EHR | Case Study

Williams — Fri, 04 Sep 2026 11:38:25 +0000

Automate your pre-intake workflows and seamlessly bridge the gap between your CRM and EHR with Elixir Contact Center on Salesforce.

In this customer success story, discover how Granite Recovery, a New Hampshire-based behavioral healthcare provider with 100+ employees specializing in addiction treatment and recovery, transformed its intake operations and eliminated manual re-entry between Salesforce and KIPU EHR.

Key Highlights Featured in this Video:

🔄 Bi-Directional KIPU Sync: Automatically creates patient records in KIPU upon transfer, keeping demographics and utilization data in sync across both systems.

📥 Unified Lead Capture: Ingests and tracks leads from Call Rail and Web-to-Lead into a single, centralized intake queue.

📋 Custom Pre-Intake & VOB Forms: Tailored pre-assessment, Verification of Benefits (VOB), cost-of-care, and travel management workflows built directly into the platform.

⚡ Less Screen Time, Fewer Errors: Reduces administrative friction for care teams while significantly boosting intake conversion rates.

Learn how behavioral health centers can eliminate handoff delays, connect lead generation to care delivery, and create an intake process that runs itself!

🌐 Transform your behavioral health intake with Elixir:
Website: https://elixirehr.com/ | https://www.mirketa.com/
Email: info@elixirehr.com | Call: +1 855-647-5382

The ROI of AI in Enterprise Applications: Why Architecture Determines Success

Williams — Tue, 19 May 2026 18:21:48 +0000

Executive Summary

For the last two years, most of us have been experimenting with Copilots and chat interfaces that look great in a demo but don’t actually impact P&L. The problem isn't that the models are getting "smarter", it’s that our enterprise foundations are messy. We’re seeing a massive gap between companies that are just "doing AI" and those that are actually leveraging AI to drive consistent, measurable value.

1. Why most AI initiatives stall

We’re currently seeing a lot of activity but very little measurable outcomes. If you look under the hood of most enterprise AI programs, you’ll find the same three challenges:

Pilots are easy to spin up in a vacuum, but they die the moment they need to scale.

A standalone chatbot is fun until it needs to talk to a legacy database that hasn't been touched in a decade.

Most "Copilots" generate a bit of curiosity for a month, then the usage charts fall off because they don’t solve a core workflow problem.

2. What Enterprise Leaders Care About

The leadership team doesn’t care about how many parameters a model has. What they care about is whether it can actually integrate with the existing technology stack without compromising security and how quickly it will pay for itself.

How quickly are people enabled to take the right decisions is where the value lies. For example, if your team can get to the right answer in two minutes instead of two hours, that has a huge positive impact – both on the P&L as well as the culture. The challenge is none of this happens unless the underlying systems are connected properly.

3. Where the real wins are happening today

The enterprise architecture determines how AI is leveraged. The ROI is currently concentrated in areas where workflows are repeatable and data is accessible. Here are some examples:

Customer Support: Summarizing massive case histories, so a human doesn't have to.

Operations: Automating the "paperwork" of HR and Finance that usually drags productivity down.

Sales Ops: Getting a proposal out in hours and not days.

These are not “AI-first” initiatives. They are workflow problems where AI happens to be the best tool for the job given the current architecture.

4. The Real Constraint: Enterprise Architecture

This is the part that has been ignored because it’s hard. The real bottleneck for AI isn't the model; it’s the architecture. Most enterprises have built AI at the "interaction layer" (the chat box). But they haven't built it at the execution layer. If your AI can suggest a solution but is unable to execute it across your systems, you’re only halfway there.

5. Our Approach at Mirketa

We’ve spent a lot of time thinking about how to bridge this gap. We don't believe in "black-box" solutions that you just plug in. Instead, we’ve been building accelerators; specifically, around agent orchestration and MCP-based data layers. Our philosophy is pretty simple:

Keep it in your house: AI should live in your environment, not ours.

Connect everything: If it doesn't work with your legacy systems and your SaaS stack, it's not a solution.

Security isn't an afterthought: It has to be baked into the architecture from day one.

6. Path Forward

If you’re tired of "experimenting" and want to see actual ROI, here is my advice:

Address Architecture Early: Orchestration and data access should not be afterthoughts.

Stop building from scratch. Use standardized frameworks (like MCP) to avoid reinventing the wheel.

Embed AI Into Existing Systems to ensure adoption. If it’s not embedded in the tools your team already uses, they won’t use it.

Measure productivity, and decision speed—not just usage stats.

Bottom Line

The gap between enterprises that successfully operationalize AI and those that remain in experimentation mode will widen over the next 6-12 months. The winners won't be the ones with the flashiest demos but the ones who fix the architecture gap.

About the Author

Ajay Jalali is the VP Delivery and Operations at Mirketa, a global IT consulting and services firm specializing in Salesforce, Oracle, enterprise integration, security monitoring, Site Reliability Engineering (SRE), data engineering, and AI enablement.

Ajay spends his days helping enterprises stop "playing" with AI and start embedding it into the core of their business. Whether it’s Salesforce, data engineering, or AI orchestration, his focus is always on one thing: making sure the technology works for the business.