What an engagement looks like

Representative, illustrative examples of how AI implementation reduces burn and delays hiring. Patterns are real; specifics are anonymized and generalized.

Seed SaaS · Operations

Delaying the first ops hire

Before: A founder spending 15+ hours a week on reporting, status chasing, and onboarding, about to hire an ops generalist.
What we built: A founder copilot, automated reporting, a knowledge base, and onboarding workflows.
Result: Reclaimed the bulk of those hours and pushed the ops hire out by two quarters.

Pre-seed · Marketing

Bringing the agency in-house

Before: A $6k/mo agency producing inconsistent content with slow turnaround and little ownership.
What we built: An AI marketing engine for briefs, drafts, repurposing, and reporting, plus fractional direction.
Result: Cut external spend sharply while increasing publishing cadence and consistency.

Series A · Finance

A clean monthly close, automatically

Before: A manual, late monthly close and reporting assembled by hand before every board meeting.
What we built: Finance ops automation for reporting and variance flags, with fractional CFO oversight.
Result: Faster, more reliable reporting and board materials produced in a fraction of the time.

Seed · Support

Support that scales without headcount

Before: Rising ticket volume threatening to require an early support hire.
What we built: A support triage agent drafting replies from the knowledge base, with human review on edge cases.
Result: Absorbed the volume increase and deferred the support hire while protecting quality.

These examples are illustrative composites for explanatory purposes and do not describe specific named clients or guarantee any particular outcome. Results depend on your stage, scope, and execution.

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