Salesforce AI Engineering
Salesforce AI, engineered: Agentforce 360, Data 360, and custom models.
DHI Dynamics is a Salesforce implementation team of computer-science engineers. We build on Agentforce 360 and Data 360 with an evidence-based method, plus the custom models when the platform needs more. Your AI runs inside Salesforce's governed trust boundary, so your data isn't shipped to third-party AI tools. Audit-ready by design.
Predictive
Forecast outcomes & rank what's next
Generative
Draft & answer, grounded in your data
Agentic
Plan and complete multi-step work
All grounded in your governed Salesforce data.
Why pilots stall
The model is rarely the bottleneck.
An MIT study found roughly 95% of enterprise GenAI pilots fail to reach measurable P&L impact — the root cause is the integration and data gap, not model quality.1 Frontier models are already good enough; what breaks is feeding them trustworthy data, wiring them into real systems, and governing what they're allowed to do.
So we start where the failures actually live. Before an agent ships, we map the data model, profile its quality, and establish the permission and audit boundary it will operate inside. The foundation first — then the AI on top of it. (Market context, cited; not a DHI result.)
Who we are
Salesforce engineers who build the AI.
We're computer-science engineers who do the Salesforce work, and an agentic organization that puts its own AI agents to work alongside us, so delivery moves faster.
We attack hard problems the way computer-science engineers do: method, evidence, rigor. Our own AI agents work alongside us to research faster and build better. What you get is the speed, depth and result.
Our mission is direct: we implement Salesforce's AI stack (Agentforce 360 and Data 360) with the rigor of an engineering team, foundation-first. Every model and agent is grounded in governed data, scored against acceptance criteria, and reviewed by a human before it touches production.
Method
A repeatable engineering approach — named, evidence-gated phases, not a one-off prototype.
Evidence
Decisions driven by what the data shows and what an eval set scores — not what sounds new.
Rigor
Grounded, governed and tested against pass/fail criteria before anything ships.
Three modes of AI
From prediction to autonomous action.
We engineer across all three and combine them with method: predictions that inform generation, generation that powers agents, every layer grounded in Data 360.
Predictive
Forecast outcomes and rank what matters next: Einstein models grounded in Data 360.
- Lead & opportunity scoring
- Churn & propensity models
- Demand forecasting
Generative
Draft content and answers grounded in your governed Salesforce data — not a generic model guessing.
- Drafting & summarization
- Natural-language interfaces
- Retrieval-augmented generation
Agentic
Agents that plan and complete multi-step work — always under human-in-the-loop control, governance and audit.
- Multi-step task execution
- Tool & system actions
- Human approval & kill-switch
Use cases
Where AI earns its keep.
High-impact patterns we engineer with Agentforce 360 and custom models, chosen on evidence of what moves the outcome, then built on a governed data foundation to ship.
AI-powered chatbots
Resolve service and sales questions across every channel.
Predictive lead scoring
Focus reps on the opportunities most likely to close.
Process automation
Remove manual steps with intelligent, event-driven flows.
RAG knowledge agents
Answer from your documents with citations and guardrails.
Computer vision
Classify images and documents for faster operations.
Next-best-action
Recommend the right move in the flow of work.
Why Salesforce for enterprise AI
One platform where people and agents share the same ground.
The hard part of enterprise AI isn't the model — it's the architecture around it. We engineer AI on Salesforce, so humans and agents work from one governed source of truth and every agent inherits the permissions, sharing rules and compliance you already trust instead of becoming a separate system to secure.
- Data 360 as one governed source of truth — the same data feeding Tableau and your agents
- Agents inherit existing permissions, sharing and audit — built-in zero-trust security
- Humans and agents share workflow, not just data — handoffs stay in one system
- Deep ecosystem — MuleSoft, Slack, Tableau — instead of stitching CRM + iPaaS + AI + BI yourself
Honest nuance: the platform removes the plumbing, but the value still comes from clean data, the right integrations and change management, which is exactly the work we do.
Platform & developer access
Agentforce 360 and Headless 360: the platform as an API.
With Headless 360, Salesforce has made the platform itself API-first: your data, workflows and business logic are reachable as APIs, MCP tools and CLI commands, so agents and your own apps act on Salesforce without a UI. We use that to wire Agentforce 360 into the systems and surfaces your business already runs on, governed the whole way.
- Agentforce 360 actions exposed where your teams work: Slack, voice, web and your own apps
- Headless 360: APIs, MCP tools and CLI access, so automations and coding agents reach the platform directly
- MuleSoft for Agentforce turns your existing APIs into governed agent actions
- Every call stays inside your permissions, sharing rules and audit trail
Honest scope: Headless 360 is powerful and new. We implement and integrate on it where it earns its place, not because it is the latest announcement.
From documents & data to action
Turn unstructured documents into work an agent can finish.
Salesforce Document AI extracts structured data inside Data 360 and exposes it as Agentforce actions — so a document becomes a record an agent can validate and act on. Where the documents are non-standard or the extraction is specialised, we train the custom vision and ML models that read them, then wire those into the same action path.
- Document AI extracts structured fields, exposable as Agentforce actions
- Custom-trained models for the non-standard documents off-the-shelf tools miss
- Extract, then validate and act — with human review on what matters
- Patterns: invoice and onboarding intake, claims, identity and form routing
The trust principle
Your AI runs inside Salesforce's governed trust boundary.
Agentforce reasons over your governed Salesforce data under the Einstein Trust Layer, with your permissions, sharing rules and audit trail, so AI becomes something your security team can reason about, not a new attack surface. Integration (MuleSoft, Data 360) still moves and federates data through governed, audited connectors under your control, and we engineer against exfiltration risk rather than pretend it away.
Beyond the platform
AI that goes beyond what Salesforce ships out of the box.
Agentforce 360 and Einstein cover a lot. When the right answer is a custom model or computer vision, most Salesforce shops outsource it or decline. We build it in-house and wire it into your platform, so the AI fits your problem and still lives inside Salesforce.
Custom ML models
We train and deploy models on your own data when off-the-shelf AI won't fit — scoring, forecasting and classification built for your domain.
Computer vision
Image and document understanding — quality inspection, extraction and classification — wired into the systems your team already uses.
Integrated end to end
Models don't live in a notebook. We connect them to Salesforce, your data layer and the apps where the work happens.
Engineered to be trusted
Innovation you can trust in production.
Our method is the same every time: ground every model in governed data, keep humans in the loop, and instrument outcomes — so AI earns its place in your workflows on evidence instead of becoming a demo that never ships.
1. MIT NANDA, The GenAI Divide: State of AI in Business 2025 — ~95% of enterprise GenAI pilots fail to deliver P&L impact; root cause is the integration and learning gap, not model quality.
Grounded
Answers tied to your trusted data.
Governed
Security, lineage and access controls you set.
Human-in-loop
Guardrails and approvals where it counts.
Measured
Instrumented so you can see real impact.
Where your AI runs
A trust boundary you can actually defend.
The hard part of enterprise AI isn't the model. It's keeping the reasoning on your governed data instead of shipping it to tools you don't control. We engineer that in from the first phase, not bolt it on at the end.
“Your AI runs inside Salesforce’s trust boundary. We don’t pipe your data out to third-party AI tools. Agentforce operates on your governed Salesforce data, under the Einstein Trust Layer, with your permissions, sharing rules, and audit trail.”
One honest caveat: Integration (MuleSoft, Data 360) moves and federates data through governed, audited connectors, by design and under your control. What stays on-platform is your AI and its reasoning.
Salesforce engineers, not a bench
Computer-science engineers who do the Salesforce work: the data model, the flows, the sharing rules, the Agentforce build. The people who scope it stay on it through go-live.
Inside the trust boundary
Agentforce reasons over your governed Salesforce data under the Einstein Trust Layer, with your permissions, sharing rules and audit trail. Your CRM isn't copied into a third-party AI tool to do its work.
Evidence, not a demo
A named, evidence-gated method. Agents are scored against a held-out eval set before go-live, and you keep the kill-switch, permission matrix and audit log.
Have an AI idea? Let's pressure-test the foundation first.
Bring the problem you want AI to solve. In one working session we'll tell you honestly whether the data and governance are ready, whether it's a model, a governed agent, or a simpler automation, and what it takes to ship.