Portfolio · Salesforce
Salesforce, configured around how the business actually runs.
Sales Cloud and Service Cloud implementation for operations-led teams: data model, automation (Flows, validation, assignment logic), and the AI/document layer that connects Salesforce to the rest of a client's stack.
Case studies
Real, completed engagements. Company names and identifying details are anonymized to protect confidentiality; the figures are accurate.
M&A data migration and a PII exposure catch
A multi-location veterinary services group growing through acquisition needed its fragmented M&A pipeline (three separate spreadsheets) consolidated into Salesforce, and a legacy debug trigger reviewed as part of a data audit.
Contract and payment data integrity
A digital marketplace platform managing contractor and creator payment agreements had contract end-dates and payment schedules drifting from what the signed documents actually said, plus a metric that had been quietly diluted by duplicate records.
Case escalation fix and a silent email failure
A manufacturer running Salesforce Service Cloud for customer support had reopened cases re-escalating incorrectly, and had no way to know that a core reply feature had a defect.
A revenue rollup that was correct, until the data wasn't
A retail technology platform (RFID-based inventory tracking) needed a custom Sales Hub built to replace standard Salesforce layouts, plus ongoing platform maintenance.
The rollup logic wasn't the problem. The underlying data was. Finding that distinction before shipping a fix is the point.
Where this covers Salesforce
Sales Cloud
Pipeline, forecasting, and territory/assignment logic configured around how deals actually move.
Service Cloud
Case management, SLAs, and escalation paths that hold up once support volume actually grows.
Account Engagement
B2B marketing automation (formerly Pardot) sized for an SMB budget, not the enterprise B2C Marketing Cloud product.
Agentforce
Salesforce's AI agent platform is still early for SMBs. Genuinely promising, but not yet a proven volume play, so it's worth scoping deliberately instead of defaulting into it.
Specialty: healthcare, payor/claims & benefits
Ash brings 10 years of hands-on experience in healthcare payor/claims, and a separate 10 years in benefits administration. That's why Health Cloud implementation and benefits-workflow automation is a genuine specialty here, not a generic add-on.
AI-assisted benefits plan intake
Benefits brokerages, PEOs, and self-insured employers running Salesforce routinely re-key plan data by hand from carrier-provided PDFs (coverage tiers, rates, eligibility rules), first into quoting, then again into a separate enrollment platform. Each re-entry point is a place for errors and lost hours to creep in.
It's a human-in-the-loop pattern, not a black box. A working, scaled-down prototype of this extraction pattern already exists. This is a capability demonstration, not a completed client engagement.
What's usually the actual constraint
For a 10–200 person team, the hard part of a Salesforce implementation is rarely the platform itself. It's making Salesforce reflect how the business actually operates, and keeping it from becoming an island. That's the same integration-layer problem that runs across every platform this practice works in: get the data model right, then make sure Salesforce isn't the fourth place someone has to manually re-type the same information.