Case Studies
How we engineer the foundation first. Worked Salesforce scenarios.
DHI Dynamics is a Salesforce implementation team of computer-science engineers. Each scenario below is engineered, not anecdotal: how we'd architect Agentforce 360 and Data 360 for a real problem, leading with the data foundation and governance, and exactly what we'd instrument to prove it. Representative by design: the rigor is real, and the client names and numbers aren't invented.
Representative scenarios — read this first
These are representative engineering scenarios— design rationale, not case files. Each shows how our engineers would architect Salesforce's AI stack for a real problem and what we'd instrument and measureto prove it worked. We label them representative on principle: we don't attach invented client names or fabricated metrics to our method. One thing stays constant across all three — your AI runs inside Salesforce's trust boundary, reasoning over your governed data under your permissions and audit trail rather than inside a third-party AI tool. Where integration (MuleSoft, Data 360) moves data, it moves through governed, audited connectors under your control — by design. The full principle and its honest caveat are set out below.
Agentforce 360 · Service
Service deflection & agentic triage
Context. A mid-market SaaS / subscription-services company on Service Cloud, with a lean support team absorbing a high volume of repetitive, low-complexity tickets.
The pain
- Agents spend most of the day on Tier-1 questions that already have documented answers, so complex cases sit queued.
- After-hours and weekend coverage gaps create slow first responses and SLA risk.
- Knowledge is scattered across a help center, internal wiki and past case notes, keeping deflection low.
Our approach
Stand up an Agentforce 360 service agent whose planning runs on the Atlas Reasoning Engine (Salesforce's planning engine for Agentforce agents), scoped to a defined set of low-risk intents. Ground it in the company's knowledge through Data 360 / Intelligent Context (help center, wiki, prior cases) so answers are retrieval-backed, not invented. The agent classifies intent, attempts self-service resolution, and takes safe, governed actions — order and status lookups, a documented plan change — through Apex/Flow actions and MuleSoft for Agentforce: Topic Center for cross-system calls. Explicit human-handoff criteria escalate to a live agent with full conversation context when confidence is low or the intent is out of scope. Engineer-led throughout, human-in-the-loop by design: topic and instruction design, an action-security and permission review, and a deflection-quality eval set scored before go-live.
Components we'd use
- Agentforce 360
- Atlas Reasoning Engine
- Data 360 (Intelligent Context)
- Apex / Flow governed actions
- MuleSoft for Agentforce: Topic Center
- Service Cloud
How we'd prove it. What we'd instrument.We engineer the success metric in from day one, not after launch: a deflection-quality eval set scored before go-live, then live tracking of intent-level deflection rate, first-response time across business hours and after hours, escalation accuracy, and customer-satisfaction on agent-handled contacts. The design target is to lift deflection on documented Tier-1 intents while keeping escalation precise — so senior agents get the complex work back. Representative scenario; the instrumentation is exactly how we'd prove the result on your data.
Document AI · Data 360
Inbound documents → structured data → action
Context. A mid-market distribution / professional-services company receiving high volumes of inbound vendor invoices and new-customer onboarding documents as PDFs and email attachments.
The pain
- Staff manually re-key invoice line items and onboarding details into Salesforce — slow and error-prone.
- Inconsistent formats across vendors make rules-only parsing brittle.
- Data-entry lag delays approvals and downstream records; errors surface late.
Our approach
Build an intelligent document-processing pipeline on Document AI in Data 360, which uses LLM-backed extraction to pull structured fields from variable, unstructured invoices and onboarding docs — the right fit where formats differ vendor to vendor. Where documents are fixed Industries form types, Intelligent Document Reader (Industries, Amazon Textract OCR) is the alternative extraction path. Extracted fields are validated and normalized against expected schemas, then written to the right Salesforce records natively from Data 360 or via a governed MuleSoft route — with a confidence threshold that flags uncertain extractions for human review. Downstream automation fires only on validated data; exceptions go to a review queue. The human-in-the-loop checkpoints are engineered deliberately, so automation never silently writes low-confidence data into your governed records.
Components we'd use
- Document AI (Data 360)
- Intelligent Document Reader (Industries / Textract) — alternative
- Data 360
- MuleSoft
- Flow approval automation
- Confidence-thresholded review queue
How we'd prove it. What we'd measure.The confidence threshold is the engineered control point: we benchmark extraction accuracy per field against a labeled validation set, then tune the threshold so the straight-through-processing rate climbs without letting error rate past the gate. Live, we'd track field-level accuracy, STP rate for clean documents, document-to-record turnaround, and the share routed to human review — and move the threshold as the evidence justifies. Representative scenario; the measurement plan is the deliverable.
Data 360 · Einstein · MuleSoft
Unified customer view & custom AI
Context. A mid-market manufacturing / field-operations company whose customer and operational data is fragmented across Salesforce, an ERP and a separate field/inspection system.
The pain
- No single, trustworthy customer view — RevOps and service reconcile records by hand and distrust the data.
- Predictive signals like churn risk and next-best-action are impractical while data stays siloed.
- A specialized need — inspection-image classification — lives entirely outside Salesforce, so results never reach the people acting on them.
Our approach
Use Data 360 to unify and identity-resolve customer and operational data into a coherent profile, with explicit data-quality and matching rules. Layer Einstein predictions — propensity, next-best-action — on the unified data and surface them directly in the Salesforce UI for RevOps and service. For the specialized need, build a custom ML / computer-vision model and wire its outputs back to Salesforce through MuleSoft, so predictions land on the right record and drive automation — and can be exposed to agents as MuleSoft for Agentforce: Topic Center actions if an agentic workflow is added later. Engineers own it end to end — data model, integration and the custom AI as one governed system — amplified by our own AI agents rather than handed off to a billable junior bench.
Components we'd use
- Data 360 (identity resolution)
- Einstein predictions
- Custom ML / computer-vision model
- MuleSoft (+ optional Topic Center)
How we'd prove it. What we'd validate.Trust in a unified profile has to be proven, so we instrument it: identity-match precision and recall against a hand-labeled sample, a data-quality score on the resolved records, and model evaluation — AUC and calibration — on the Einstein and custom-vision predictions before anything surfaces to a user. From there we track manual-reconciliation effort and decision cycle time. Every signal lands and is scored inside Salesforce; the data is unified on-platform, not exported. Representative scenario; the validation gates are how we'd earn the trust.
Sectors and formats above are chosen to make each engineering pattern concrete. Bring your own context and we'll apply the same method — architect the solution on Salesforce's AI stack, define the metric up front, and name where your data, integration and adoption are the real critical path. Evidence over assertion, every time.
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 a scenario like one of these?
Bring the real version — your tickets, your documents, your fragmented data — and we'll engineer the approach: the Salesforce AI-stack components that fit and the metric we'd instrument to prove it, in a single working session. Your AI runs inside Salesforce's trust boundary, by design.