Readiness assessment
Current state: data quality, processes, security, organisation. What is ready, what is not.
Agentforce & Data Cloud
Agentic AI on solid foundations
Readiness assessment, qualified use cases, explicit guardrails: deploy Agentforce and Data Cloud with method. A measurable pilot before any commitment at scale.
The situations we encounter
Between the pressure to "do AI" and the reality of your data, you need a method.
Leadership expects results on AI, but the use cases remain vague.
Current data quality cannot feed reliable agents.
The risks — wrong answers, data exposure, compliance — are not framed.
Demos are convincing, but the path to production remains uncertain.
No one can say whether an agent would actually add value, or how to measure it.
Data Cloud is being considered without a clear view of what it should unify, or why.
What we do
Uncompromising qualification, cautious design, a decision grounded in a measured pilot.
Use-case qualification
Each case assessed on three axes: value, feasibility, risk. Unsuitable cases are explicitly ruled out.
Agent blueprint
Role, scope, permitted actions, data sources, expected and prohibited behaviours.
Guardrails
Action limits, human oversight on sensitive operations, full traceability of decisions.
Data requirements
Quality, unification and governance of the required data, with the exact role of Data Cloud.
Measurable pilot
A limited scope, success criteria defined upfront, a documented extension decision.
Deliverables
From the initial assessment to the pilot roadmap: everything written, everything open to decision.
- AI readiness report
- Use-case matrix: value, feasibility, risk
- Agent blueprint: role, scope, behaviours
- Data requirements: sources, quality, unification
- Action catalog of permitted operations
- Guardrails: limits, oversight, traceability
- Adoption plan for the teams concerned
- Roadmap from pilot to deployment
How we work
Every step produces a decision, not just a document.
Readiness
Current state of data, security and organisation.
Use cases
Qualification and prioritisation with the business teams concerned.
Design
Blueprint, action catalog, guardrails, data requirements.
Pilot
Limited scope, measured results, an extend-or-stop decision.
Typical engagements
Typical situations we address.
A first customer-service agent
An online retailer wants to automate the handling of recurring requests. Framing defines scope, guardrails and success criteria before anything goes to production.
Assessment before investment
A CIO must respond to a board-level question about Agentforce. The readiness report establishes what is realistic today and what should wait.
Unifying customer data
A multi-brand group prepares Data Cloud to make its customer knowledge reliable — the prerequisite for any useful agent.
Frequently asked questions
What CIOs ask us before getting started.
Is Data Cloud required to use Agentforce?
Not systematically, but an agent is only as reliable as its data. The readiness assessment determines what is actually needed for your use cases — no more, no less.
How do you limit the risk of wrong answers?
By design: a restricted action scope, controlled data sources, human oversight on sensitive actions, full traceability. And a measured pilot before any extension.
Which use cases actually work today?
The best-framed ones: recurring requests with verifiable answers, internal team assistance, case preparation. We explicitly rule out cases where the technology is not ready.
What if the pilot does not deliver?
That is a planned and acceptable outcome. The pilot is designed to produce a decision: extend, adjust or stop. A documented stop is better than a deployment endured.
Is our data exposed to third parties?
The framing covers this point explicitly: the scope of data accessible to the agent, platform policies, and compliance with your internal and regulatory requirements.
An Agentforce project to frame?
Let's talk about your candidate use cases and the state of your data.