BH Capital Funding — the operating layer behind a high-volume capital firm.
New York. BH Capital Funding is a founder-led capital firm in alternative finance and lending that ran a high-volume underwriting operation. OpsPal didn't ship one tool and leave — we became the operating layer underneath the funding workflow, from the AI underwriting engine to the data hygiene, lender-history extraction, and standardized flows around it.
BH Capital underwriting outcomes
Operator-reported, from the named underwriting engine engagement.
minutes per file
Underwriting review time
to an approval
Down from days
lower decline rate
Cleaner, lender-aligned files
Who they were
A founder-led capital firm in business finance and lending, running deals through Salesforce at volume. The business made money by getting applications from intake to a lender submission quickly and cleanly — so the speed and quality of the underwriting operation was the business.
What was breaking across the operation
The constraint wasn't one task — it was the whole pipeline from application to submission. Files arrived from multiple sources and were reviewed by hand, roughly 30 minutes each before a human even made a decision. Reps submitted incomplete, weakly matched applications, so underwriting absorbed the rework and chased missing data. Years of lender offer and decline emails sat outside the CRM, in inboxes nobody could query, blocking performance analysis. Throughput was capped by reading speed, approvals stretched into days, and too many files were declined for reasons that were knowable up front.
How OpsPal came in
We mapped the underwriting workflow end to end, turned the findings into a sequenced roadmap, and built in milestones — then stayed on for ongoing optimization as volume and lender criteria evolved. The result wasn't a one-off deliverable: OpsPal became the operating layer the firm's funding workflow runs on, with each build reinforcing the others.
The journey
The builds that make up the engagement — each links to its full case.
AI underwriting engine
The flagship build: intake → parse → score → route → package → submit → track, wired into the firm's CRM, taking files from ~30 minutes to ~5.
Read the build →Data hygiene & lender-fit pre-checks
Salesforce hygiene gates and lender-fit logic moved checks to the point of submission, dropping the decline rate roughly 35%.
Read the build →Email → CRM extraction
An ETL pipeline turned roughly 29,000 lender offer and decline emails into structured Salesforce records, making lender history queryable in-system.
Read the build →Underwriting workflow automation
Salesforce flows and completeness gates standardized the manual checks, cutting underwriting time from ~30 minutes to ~5 per file.
Read the build →The trust angle
This is a finance firm trusting an outside team with applicant financials and its own underwriting logic. We treated it that way: the engine automates the mechanical steps — extraction, scoring, lender-fit routing — while a human stays on every approval decision, and the whole workflow runs inside infrastructure the firm already controlled. The point of automation here was to free underwriters for judgment, not to remove them from it.
What's honest about this case
This work was completed during Daniel's tenure as co-founder of the firm; the metrics are operator-reported; and nothing in flight or proprietary is published here. There is no client testimonial on this page because there isn't an approved one.
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