Systems, shipped.

The problem

Pipeline generation was a coin flip. Some sellers prospected well, most didn't. The team had already spent on CRM, sales enablement, and an offshore SDR experiment — none of it produced a system. Senior leadership was being asked by the board what their AI strategy was, and the only honest answer was "people use ChatGPT to draft emails."

What we built

An autonomous SDR engine on a seven-dimension flywheel. One activation phrase runs the full daily cycle: prospects researched, scored against ICP signals, personalized outreach composed, sent through LinkedIn, every action logged, learnings captured and fed back into the next cycle, all surfaced on a live dashboard the operator checks from their phone.

The flywheel

System components

Outcomes

Time-to-first-touch dropped from 5–10 days of manual seller cadence to under 24 hours. Cost per qualified meeting landed roughly 70% below the typical mid-market SDR benchmark. Pipeline created in the first 90 days reached $400–600K in qualified opportunity value. The learning loop has produced 12–18 documented iterations to scoring weights and message variants since launch.

The problem

Manual assessment grading was the bottleneck on throughput, turnaround time, and consistency. Grading queues meant learners waited days for feedback that lost compounding educational value the longer it took. Quality varied across human graders. Cost-to-serve per assessment was structurally too high to scale.

What we built

An AI assessment system that ingests submissions, evaluates against a rubric, generates structured feedback at human-grader baseline accuracy, and routes edge cases to humans for review. Runs continuously rather than in batches — feedback lands in hours instead of days.

The operating principle

System components

Outcomes

Feedback that used to land in days now lands in hours. Accuracy sits at 98% against the human-grader baseline, well inside the agreement band the operator's quality team held human graders to. Throughput is structurally unbounded because the system runs continuously.

The real leverage: the human team got redeployed into learner-facing work where human judgment compounds — coaching, curriculum design, and intervention on at-risk learners. Grading stopped being the bottleneck. Humans moved where they matter most.

The problem

A merger of two billion-dollar product orgs creates a predictable failure mode: duplicated tooling, parallel roadmaps, cultural drag, and a content and expertise base trapped inside the heads of senior people who can't be cloned. In special education, the expertise gap is the product — clinicians, curriculum specialists, and accessibility experts are the scarce resource.

The org needed throughput and personalization at the scale of 1B learners, with no degradation in clinical quality, compliance posture, or accessibility standards. The traditional answer — hire more experts, build more content, ship more features — doesn't math at that scale.

The architectural thesis

System components

Outcomes

Same team, 5× output, no quality or compliance loss. AI shipped to the classroom. Multi-lingual expansion unlocked previously inaccessible international markets. Best-in-class product and program management maturity stood up in under 24 months across a 100+ person org spanning two legacy companies.