Featured Work
The engagements below are representative scenarios that illustrate how we approach a problem — published as our named client case studies go live. Every project starts the same way: a real constraint, a scoped system, and a measurable outcome.
A logistics operator's support team manually routed and prioritized hundreds of daily requests, causing delayed responses during peak periods.
Designed a workflow automation layer with an AI classification step in front of their existing ticketing system, plus human review for edge cases.
Manual triage removed from the critical path; the team now spends its time resolving requests instead of sorting them.
A mid-market software company's support and sales teams relied on scattered internal docs, Slack threads, and tribal knowledge to answer product questions.
Built a retrieval-augmented assistant scoped to approved internal sources, with citations back to the source document on every answer.
Faster, more consistent answers with a clear audit trail — and no answer presented without a traceable source.
A growth-stage company's CRM, billing, and product analytics lived in three separate systems that didn't agree with each other.
Designed a composable integration layer with explicit contracts and a single source of truth for customer state.
Teams work from one consistent customer record instead of reconciling spreadsheets before every leadership review.
An internal operations tool had grown feature-by-feature for years; new hires needed weeks to become productive in it, and mistakes were common.
Ran task-based research with the operations team, rebuilt the information architecture around their actual workflows, and introduced a lightweight design system to keep it consistent as it grows.
New team members reach baseline proficiency in days instead of weeks, and the most error-prone actions now require explicit confirmation.
A ten-year-old monolith had become too risky to change confidently, but the business couldn't absorb a multi-quarter rewrite freeze.
Introduced a strangler-fig migration plan: new features shipped in a modern service layer behind a shared gateway, with automated regression tests covering the legacy core before any of it moved.
The business kept shipping throughout the migration, with each extracted service independently tested, deployed, and owned.
A B2B marketing site had strong content but poor technical SEO/AEO health — slow renders, thin metadata, and no structured data — so it rarely got cited by AI answer engines.
Fixed rendering and Core Web Vitals issues, restructured pages around clear entity definitions and FAQs, and added structured data without touching the visual design.
Pages became both faster for human visitors and easier for search and answer engines to parse and cite accurately.
A services company wanted an AI assistant to handle appointment scheduling, but couldn't accept a system that might double-book or cancel without oversight.
Scoped the assistant to a narrow, reversible action set (propose, hold, confirm) with human approval required for cancellations, and logged every decision for audit.
The team trusts the assistant with the repetitive 80% of scheduling, while every consequential action still passes through a person.
Let's talk through the constraints before we talk through the solution.