Real examples of product and workflow improvement
Three project examples showing the kind of product, workflow and decision-system work we've shipped. More case studies will be added as outcomes can be shared.
Automating growth operations for a national school directory
We helped a school directory move from local coverage to national reach by building the operating layer behind growth: automated data collection, AI-assisted curation, useful content workflows, school outreach, CRM communications, billing reconciliation, support drafting, SEO execution, and product/admin improvements.
The work turned expansion into a connected system instead of a larger manual workload. School data, content, registration communication, billing, support and admin workflows got automated into an operating system that could expand without bloating the main directory or requiring a large operations team.
Operating proof: 13,000+ school profiles, AI-assisted content workflows, outreach and CRM automation, billing/support tooling, and SEO work that helped multiply organic traffic and revenue.
View school directory projectEmbedding AI guidance into a developer learning workflow
We helped a developer education platform integrate AI guidance directly into the coding environment, moving assistance closer to the moments where learners get stuck: setup, debugging, and code review.
The shipped Tutor Mode brings adaptive hints, bug detection, and test feedback directly into the editor flow. Alongside it, a curated prompt library translates common project ideas into structured, stack-aligned blueprints for AI coding assistants.
Tech and product layer: Interactive tutor loops, local editor integration, automated test feedback, and AI-ready templates built for an active Ethereum/Solidity student base.
View developer learning projectTurning scattered decision records into a citable knowledge assistant
We helped an organization turn fragmented decision history into a searchable, citable assistant. Proposal discussions, approval records and execution/status tracking were connected so people could ask natural-language questions and verify answers against source material.
The work modeled the decision lifecycle before retrieval: canonical decision metadata, attributed discussion records, semantic search, grounded answers, citations and an evaluation harness for retrieval and answer quality.
Technical layer: source modeling, ingestion, semantic retrieval, cited answer generation, prompt-injection-aware handling, and evaluation by retrieval and answer failure mode.
View knowledge assistant projectIf this looks like the work you need, let's talk about your workflow.
The first conversation is about the process: where time and quality leak, which tools are involved, and what system would make the work easier to run.