AI that invents answers
Ungrounded models hallucinate. We ground them in your data with retrieval and tight scope.
Salesforce service
AI and LLM integration for Salesforce — grounded and governed
Insight
AI and LLM integration connects large language models to Salesforce data and workflows — through Agentforce's model choice or custom integration — grounded in trusted data and wrapped in guardrails. ForceFolks integrates OpenAI, Anthropic, and Gemini with Salesforce so AI features are accurate, secure, logged, and useful, not a disconnected experiment.
What is AI and LLM integration for Salesforce?
Salesforce already supports model choice inside Agentforce and Einstein — OpenAI, Anthropic, and Google Gemini run within its trust layer. Some needs go further: integrating an external model or AI service, building retrieval over your own data, or embedding LLM features in custom apps and portals.
ForceFolks handles both: configuring Salesforce-native model choice and building custom, governed LLM integrations grounded via Data Cloud and RAG, with prompt design, guardrails, and logging.
When do you need custom AI/LLM integration?
Custom AI/LLM integration is warranted when:
- You want AI features beyond what Agentforce or Einstein offer out of the box.
- You need retrieval (RAG) over your own documents or data.
- You're embedding LLM features in a portal or custom app.
- You must control model choice for quality, cost, or data residency.
- Security needs strict grounding, logging, and data boundaries.
Signals
Signs you need AI & LLM Integration
Data-leak and compliance fears
We design grounding, redaction, and logging so security and legal can approve it.
AI bolted on, not integrated
Disconnected bots add little. We embed AI in real Salesforce workflows and actions.
Unpredictable model cost
We design prompts, caching, and model choice so spend is controlled.
Capabilities
What AI & LLM Integration at ForceFolks includes
- Model choice — OpenAI, Anthropic, Gemini within the trust layer.
- Grounding & RAG over Salesforce and external data.
- Data Cloud integration for unified context.
- Prompt & action design for reliable output.
- Guardrails — scope, redaction, and human escalation.
- Custom LLM services and API integration.
- Logging & evaluation of AI behavior.
- Cost & performance controls.
How it works
How ForceFolks delivers AI & LLM Integration
From kickoff to a working, adopted org — senior-led at every phase, with scope and decisions you control.
-
Define the use case
We pick a high-value, contained AI feature with clear success criteria.
-
Ground the data
We connect trusted data — often via Data Cloud and retrieval.
-
Choose & wire the model
We select the right LLM and integrate it within the trust layer.
-
Design prompts & actions
We make output reliable and scope what the AI can do.
-
Guardrail & log
We add boundaries, redaction, escalation, and audit logging.
-
Evaluate & launch
We test behavior, then deploy and monitor cost and quality.
Engagement
Ways to engage ForceFolks
Use one model, or blend them as the work changes.
Fixed-scope project
A defined outcome, timeline, and price set after discovery. Best when requirements are clear.
Time & materials
Senior capacity billed for actual effort. Best when scope will evolve as you learn.
Managed services
Ongoing delivery from a shared backlog with accountability. Best after go-live.
Staff augmentation
Vetted experts inside your team, under your direction. Best when you lead delivery.
Problems we solve
Common problems we solve
Hallucinated output
Grounding and retrieval keep answers tied to your real data.
Security objections
Boundaries, redaction, and logging make AI defensible to security teams.
AI no one uses
We embed features where work already happens, not in a side tool.
Runaway cost
Model choice, caching, and prompt design keep spend predictable.
Buyer questions
AI & LLM integration: questions buyers ask
Does Agentforce use ChatGPT?
Agentforce supports a choice of models — including OpenAI (ChatGPT's models), Anthropic, and Gemini — within Salesforce's trust layer, and Agentforce capabilities can also surface inside ChatGPT. We help you pick and ground the right model.
Can we use our own or external LLMs?
Yes. Beyond Salesforce-native model choice, we integrate external models and AI services where you need specific capabilities, control, or data residency — with grounding and logging.
How do you stop the model leaking data?
We ground tightly, scope what data the model can access, redact where needed, log interactions, and add human review for sensitive actions — so security and legal can sign off.
Fit
Who this is for
Who this is for
- Teams extending Salesforce AI beyond defaults
- Product and IT teams embedding LLM features
- Security-conscious orgs that need grounding and logging
When ForceFolks is a strong fit
- You want AI grounded in your data, with guardrails
- You need control over models, cost, or data residency
- You're integrating AI into real workflows
When ForceFolks may not be the right fit
- Standard Agentforce/Einstein features fully meet your need
- Your data isn't ready to ground AI yet
Buyer objection
When is ForceFolks a better fit than hiring one Salesforce developer?
Insight
ForceFolks is a better fit when the Salesforce problem requires architecture, integration, delivery governance, multi-cloud experience, or senior platform ownership. A single developer may help with tickets, but complex Salesforce environments usually need architects, consultants, developers, administrators, QA, DevOps, and integration specialists working together.
- A failed or stalled implementation that needs to be rescued
- A multi-cloud Salesforce rollout to sequence and architect
- MuleSoft, ERP, or finance-system integration
- Data Cloud or Agentforce readiness and grounding
- An over-customized org that needs cleanup and governance
- CPQ / Revenue Cloud quoting and pricing complexity
- An internal team that lacks senior architecture
- A need for managed delivery plus flexible team extension
Related
Related Clouds, roles, and services
Agentforce
Salesforce's agentic AI platform on the Atlas Reasoning Engine.
Data Cloud
Unify, model, and activate customer data across Salesforce.
Service Cloud
Case management, omni-channel support, and service automation.
Sales Cloud
Pipeline, forecasting, and sales process automation.
Related roles you can hire
Related services
Agentforce Implementation
Design, ground, and deploy Salesforce AI agents on the Atlas Reasoning Engine.
Data Cloud Implementation
Unify customer data in Data Cloud (Data 360) for analytics, activation, and AI.
Salesforce Integration
Connect Salesforce to ERP, finance, support, ecommerce, and data systems.
Senior specialists deliver your AI & LLM Integration — not a junior bench. A Salesforce Consulting Partner with a 200+ person team, a 95% post-launch NPS, ISO 9001- and SOC 2-aligned delivery, and architecture-led, source-controlled work.
FAQ
Frequently asked questions
Is AI/LLM integration the same as Agentforce?
Overlapping but broader. Agentforce is Salesforce's agentic platform with built-in model choice. AI/LLM integration also covers custom retrieval, external models, and embedding AI in custom apps — grounded and governed the same way.
What models do you work with?
OpenAI, Anthropic, and Google Gemini within Salesforce's trust layer, plus external models where a specific need or data-residency requirement calls for it.
Do we need Data Cloud?
For reliable grounding, usually yes. Data Cloud gives AI a unified, trusted view to retrieve from, which is what keeps output accurate.
Make Salesforce work across the business.
Tell us what you need Salesforce to do. ForceFolks will assess your Clouds, integrations, data, automation, team capacity, and delivery risks — then recommend the fastest path to a working implementation.