Every project starts with the same question: what should this do for the business? AI goes in where it earns its place. Where it doesn't, we leave it out.
Production LLM applications: autonomous agents, retrieval over your own documents, classification and extraction pipelines, copilots inside the tools you already use. Built with evals and guardrails, and honest about what the model can't do. LLMs are the newest thing in the toolbox. The craft under them is twenty years old.
The workflows quietly draining your week — intake triage, document handling, reporting, follow-ups — rebuilt as reliable automations with a human in the loop where judgment matters.
Twenty years of it. Web platforms, APIs, integrations between systems that were never meant to talk, and migrations off software you've outgrown. We've built the same thing enough times to know where it breaks.
Dashboards and visualizations people actually open: real-time rendering in the browser, external data sources woven in live, designed to make the state of your business legible at a glance.
Concept to launched is half the story. We stay for growth: performance, analytics, conversion instrumentation, and the iteration loop that turns a launch into traction.
Month-to-month care for systems we build — monitoring, updates, and a direct line to the people who wrote the code. You own everything we build custom for you.
Twenty years of custom development teaches you things no framework does. We've launched multiple SaaS platforms from a blank repo. We've kept health records HIPAA-compliant, built systems used inside the DoD to FedRAMP standards, and automated financial record keeping for systems that were built to resist automation. None of it was magic. It just looked that way from the outside.
Several products went from an empty folder to a paying platform. Multi-tenant from the first commit, billing that reconciles, and the operational plumbing that keeps a service up at 3 a.m. We ran some of them after launch, which changes how you build them.
The first technical team for founders with a deck and a deadline. Version one, built fast and a little rough. Then the version after it, once real users showed up and had opinions.
Records management for health data, built to hold up under HIPAA: access controls, audit trails, encryption at rest and in transit, and the paperwork that goes with it. Patients never knew we existed, which is how it should work.
HIPAA and FedRAMP were the strict ones. Once you've built to that bar, you don't unlearn it: audit trails and access control go in by default, and the paperwork gets written to pass review, even for clients who never asked.
A long run of financial services work: record keeping, reconciliation, and automated processing for systems that were never designed to share data with anything else. Now they do.
Vendor APIs, nightly file drops, exports from software nobody supports anymore. We build the layer in between and keep it reconciled.
We host what we build when the client wants us to. Migrations to the cloud and occasionally back out of it, monitoring, and the on-call rotation that goes with all of it. Systems stay up mostly because someone sat down and planned for the day they wouldn't.
Public and partner APIs, versioned and rate-limited, with documentation written so people could get through it without calling. Some are still answering requests from clients who signed up years ago.
Systems that got started, got handed off, then handed off again. Eventually nobody would look at them without declaring them impossible and asking to start over. Those are the ones we take on. When you and your team have been called wizards enough times, it stops seeming like a silly phrase and starts feeling like a worthy label.
Some systems we built a decade ago are still running, and we still answer the phone about them. Long support contracts are the least glamorous thing we offer and the one we're proudest of.
Some of that work was inside the DoD: systems built and used there following FedRAMP standards. FedRAMP is the government's own bar for clearing cloud services, hundreds of controls, each one documented and tested, and "good enough" gets you sent back. Hardened and logged, then reviewed by people paid to find reasons to say no.
Another was deep learning built into a specialized system for genome sequencing. We walked in with a bunch of high-level thoughts. Within a few months the system was in use in labs. Then we helped build the internal team that would take it over, trained them, and bowed out with grace. We count that as a win.
A working session to name the outcome that matters — revenue, hours saved, users served — and the shortest credible path to it. If AI doesn't serve the goal, we'll say so.
A thin, end-to-end version in weeks, not quarters. Real data, real users, real feedback — the fastest way to find out what the product actually is.
Hardening what proved itself: tests, security, accessibility, and the edge cases that separate a demo from a product.
Performance, observability, and infrastructure sized to real load — then steady support as the system grows into its ambitions.