Who we work with

Four industries. Not a fifth.

We turned down "everyone" on purpose. Each of these four environments has its own data shape, its own compliance weight, and its own reasons agentic AI succeeds or fails — generic advice doesn't survive contact with any of them.

Financial services

Where the cost of a wrong agent decision is immediate

Agentic AI in Financial Services has to operate inside audit trails, regulatory constraint, and data lineage requirements that most agent frameworks weren't built for. The data foundation work here isn't optional groundwork — it's the compliance boundary the agent operates inside.

Relevant practice areas: Data foundations, Agentic enablement

Raw transaction data passes through a compliance shield with real-time audit trails before reaching secure agent operations

Health

Where the data model has to earn trust before an agent touches it

HIPAA-bound data, fragmented systems of record, and clinical or operational workflows where an agent's mistake isn't a bad recommendation — it's a patient-facing error. Health data foundations require a different governance posture than a standard CRM implementation, and we build accordingly.

Relevant practice areas: Data foundations, Agentic enablement

A patient record passes through a HIPAA governance gate before an agent can act within the approved boundary

Service

Where agentic AI has the clearest near-term payoff — and the clearest ways to fail publicly

Service is the environment most enterprises are already piloting agents in, which means it's also the environment with the most visible failure modes — an agent that mishandles a customer interaction fails in front of the customer, not in a dashboard. We build the evaluation layer that catches that before it ships.

Relevant practice areas: Agentic enablement, Claude & AI integration

A customer request is routed through one orchestrator to three specialized agents and returned as one coordinated reply

Core Salesforce

Where the foundation work pays off across every other use case

Org health, data model discipline, and platform architecture inside core Salesforce — Apex, Flow, SFDX — are the substrate every Data 360, Agentforce, or Claude integration eventually depends on. Getting this right first is what makes everything else faster and safer to build.

Relevant practice areas: Data foundations

Four Salesforce architecture layers show governance supporting the data model, automation, and interface

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