A structured review for Puget Sound companies that want an honest account of their Salesforce org before adding AI, new clouds or a new partner.
A common trigger for a Seattle health check is an AI initiative that has just moved from idea to budget line. Before an agent works on customer data, leadership wants to know whether permissions, data quality and automation will hold up. Our consultants examine sharing and security, record quality, automation overlap, Apex and its test coverage, integrations and usage patterns, and they sort what they find by risk and by the effort to fix it. Engineering-minded teams get detail they can act on, and business leaders get a short list of what to fix first.
Artificial intelligence is a Greater Seattle Partners target sector, and AI companies are often the most eager to put agents to work on their own Salesforce data. A health check tests whether that data is ready: duplicate rates on accounts and contacts, completeness of the fields an agent would draw on, how current the knowledge base is and whether integration users and agents would receive only the access they need.
Software and cloud companies make up much of the region's large information sector, and their orgs often carry significant technical debt from rapid growth. We inventory unused fields and objects, inactive or overlapping automation, packages installed for trials and Apex with weak test coverage, then estimate the effort to address each item, so cleanup can be planned alongside product work instead of competing with it. Findings are phrased like engineering tickets, ready for a sprint.
Life-sciences companies need confidence that research, clinical or patient-related information in Salesforce is protected. The review tests whether sensitive objects are locked down at field level, whether scientists and sales staff see only their own side of the house, whether audit settings capture changes and which connected systems can read protected records. Findings are ranked so privacy risks are addressed before any expansion of the org or its use of AI.
AI agents and integration users need their own access review. Confirm each can read only what its tasks require, because an agent with broad access can surface records that a human user asking the same question would never be shown.
Engineering-heavy companies often have more Apex, triggers and custom components than typical orgs. Test coverage, error handling and hard-coded values deserve the same scrutiny as permissions, since fragile code tends to break during Salesforce releases. Brittle triggers are a frequent source of surprise.
Metadata shows what exists; interviews show what people rely on and what they avoid. A few conversations with sales, service and operations users turn a technical inventory into a priority list the business actually agrees with. It also builds support for the fixes.
How our salesforce health check works →
Client names are anonymized, and these projects are not specific to Seattle.
Test coverage and quality, error handling, hard-coded IDs or values, triggers that overlap with Flows and code that duplicates standard features. We also check whether classes run with appropriate sharing settings. Findings list each class or trigger with its risk and the effort to fix it, so your engineers can plan the work themselves or hand it to us.
It narrows the pilot to a use case your data can support. We check the objects, knowledge and permissions the agent would rely on and flag blockers such as inconsistent categories or overly broad access. You can then fix those items, choose a tightly scoped first use case and measure results against a baseline instead of discovering data problems through customer conversations.
It can be, but some companies repeat a shorter version on a regular cycle or before major changes, such as a new cloud, an acquisition or an AI rollout. Comparing results over time shows whether technical debt is growing or shrinking. Under managed services, we fold those checks into ongoing work so issues are caught between full reviews.
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