Our Approach
An evidence-based method, so your Salesforce build is engineered, not demoed.
DHI Dynamics is a Salesforce implementation team of computer-science engineers. We implement Agentforce 360 and Data 360 through five named, evidence-gated phases, and nothing advances until its gate clears. Most enterprise AI fails on data and governance, not models, so that work comes first.
Data foundation
Design
Build
Evaluate
Govern
Work only advances when the gate clears.
A method you can audit
A process you can audit, not a demo you have to trust.
We're computer-science engineers who treat a Salesforce roll-out the way engineers treat any hard system: evidence at the start, a scored result before go-live, and a checkpoint between every phase that the work has to pass before it moves on. You can inspect every step.
We're also an agentic organization: our engineers put their own AI agents to work, so delivery moves faster. You buy a de-risked, governed result at a fixed scope, not a timesheet. The method is ours. The outcome, the artifacts and the audit trail are yours.
- Five named phases, each with an evidence gate it must pass
- Agents scored against a held-out eval set before go-live, not demoed
- Every deliverable is one you can inspect, verify and keep
- Agentic and accelerated, always human-in-the-loop and governed
The engineering method
Five phases. Five evidence gates.
The same engineers carry every phase, and nothing advances until its gate clears. This is what foundation-first looks like in practice: the data and governance work happens before the agent, not after the incident.
Data Foundation & Governance Baseline
We map the data model, profile quality, and establish the permission and audit boundary first. Most enterprise AI fails here, not on the model, so this is where we start. You leave the phase with a baseline you keep.
Gate · A scored readiness baseline, before any agent ships.
Design with an explicit trust boundary
We design the architecture so your AI runs inside Salesforce's trust boundary, and we name and scope every MuleSoft and Data 360 flow that moves data in. Where data travels — and under whose governance — is a decision, never an accident.
Gate · A trust-boundary map every flow is named in.
Build to acceptance criteria
Every agent behavior has a written specification with explicit pass/fail criteria before it is built. Outputs are constrained to a schema and validated — no free-form text quietly entering your systems. We build in tight, correctable iterations on a maintainable foundation.
Gate · A written pass/fail spec for every agent behavior.
Evaluate against a held-out eval set
Agents are scored against a versioned, held-out eval set — labeled inputs with expected outcomes, graded for accuracy, safety and regression — before they go live. You see the score and own the suite. We never ship on the strength of a good demo.
Gate · A score you see before go-live, not a demo.
Govern & hand off
You take ownership of a documented kill-switch, a permission matrix, an audit-log review and a runbook. The system is agentic, but always human-in-the-loop and governed, so you can stop, inspect and explain any action.
Gate · Kill-switch, permission matrix and audit log, all yours.
What you can verify
Four deliverables you can hold up and check.
Engineering rigor isn't a claim. It's an artifact. Each of these is something you receive, inspect and own. We publish the discipline and the deliverable; the way we manufacture it stays ours.
Agent eval sets
Versioned, labeled inputs with expected outcomes, scored for accuracy, safety and regression. You see the score before go-live and own the suite. Eval-driven development is industry standard — and the strongest signal a technical buyer can ask for.
Schema-validated outputs
Agent outputs are constrained to a defined schema and validated before they touch your systems — so nothing free-form enters your CRM by surprise. Structure you can inspect, not a black box you have to trust.
Governance & kill-switch artifacts
A permission matrix, an audit-log review and a documented kill-switch — the controls that directly answer exfiltration-class fear (an industry-documented issue we engineer against, not a risk we claim to eliminate).
Data-quality baseline
Your data is profiled and remediated first, then reported back as a baseline you keep. The unglamorous foundation that decides whether everything built on top of it actually works.
Where your AI runs
The trust boundary is engineered in from phase one.
“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.
- Designed for the trust boundary in phase 02, not bolted on at the end
- Agents inherit your permissions, sharing rules and audit trail
- Data flows in through governed, audited connectors — by design
- Exfiltration-class risk is engineered against, not claimed away
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.
See the method before you commit to it.
Bring us your hardest Salesforce question. In a single working session we'll frame it as evidence and map the first gate: the data and governance baseline your Agentforce roll-out has to clear before any agent ships.