We embed with your team, map how the work really gets done, and ship the automation with the highest return into production. Not a demo. A system your people use every day.
The documented workflow skips the workarounds, exceptions and tribal knowledge your team relies on. Automate the paper version and it breaks on day one.
A prototype works on the happy path. Production means messy data, odd inputs, system outages and the decisions that need a person to sign off.
AI systems drift. New exceptions appear and models change. Without someone watching quality and cost, the value quietly erodes.
We interview the people who do the work and mine the data in the systems you already use, so the map includes the exceptions people forget to mention. Then each step goes into one of four buckets.
Steps that exist out of habit: duplicate entry, reports nobody reads, approvals that never get rejected. The cheapest automation is removing the work.
Steps that follow fixed rules: syncing systems, validating fields, routing by known criteria. Plain code is faster, cheaper and more reliable than AI here.
Steps that need reading, judgment or language: triaging requests, extracting data from documents, drafting responses. Built with evaluations so quality is measured, not assumed.
Steps where a wrong call is costly. The system prepares everything and a person approves with one click, so your team keeps control without doing the busywork.
Each phase is a separate, fixed-scope decision. You never commit to more than the next step.
Interviews, system data analysis and the four-bucket map of your process. You get a ranked list of opportunities with estimated return, feasibility and effort, plus baseline metrics to measure against.
The single highest-return opportunity that can realistically ship, taken all the way to production with your real users, real data and the integrations it needs. Priced from what discovery found, so there are no surprises.
Monitoring, new edge cases, model updates and cost control, with a monthly report on the return delivered. When you're ready, the next opportunity on the roadmap.
Discovery usually surfaces many opportunities. The biggest one isn't always the right first move, because a stalled project helps no one. Every opportunity is scored on four things.
Across healthcare, fintech, logistics and research, the pattern is the same: messy real-world data and processes, turned into software people rely on.
A multi-location clinic group needed one system for telehealth, in-house and in-person care, plus a separate product for QME evaluations.
Officers were manually processing handwritten consent forms in Amharic and English, slowing down data ingestion.
A payments company needed reliable analytics on a high volume of transaction data.
A delivery company needed to turn high-volume GPS and order data into insights its teams could act on.
Recruiters needed to screen CVs written in both English and French against job descriptions.
A lending platform needed to assess borrower risk, liquidity and affordability inside its clients’ products.
You contract with our US limited liability company. US contracts, invoices in USD, standard payment methods.
5 hours of guaranteed overlap with US Eastern time every working day, plus async updates you can read in five minutes.
Code, prompts, evaluations and documentation are yours, in your repositories and your cloud accounts.
Least-privilege access, your data stays in your systems, and we're happy to work through your security review and sign an NDA.
I've spent my career turning messy, real-world processes into software that runs in production. Today I'm Head of Product at a US multi-specialty, multi-location healthcare company, where I lead all technology projects and manage two products: a QME platform and a hybrid EHR that supports telehealth, in-house and in-person visits, and every clinical workflow from credentialing to billing. We delivered the EHR in four months and are now taking its integrations live.
Before that, I led the deployment of an AI assistant that processes handwritten consent forms at Carnegie Mellon University Africa, built data warehouses and pipelines processing 40+ million transactions, and shipped NLP models for the African Development Bank. I started out as a founder, building and launching web and mobile products, and I hold an MS in Information Technology from Carnegie Mellon.
I lead every engagement personally and bring in engineers from our team for integrations, data and front-end work as the project needs. You get one accountable lead with the capacity of a team.
Then you'll know that before spending on a build, and you still keep the process map, the steps worth deleting and the baseline metrics. Discovery is useful on its own.
One use case in production with measured results beats five half-finished pilots. It proves the return, builds trust with your team and makes the next project faster.
A decision-maker who owns the outcome, a few hours with the people who do the work, and access to the relevant systems. These are agreed up front with dates, so timelines stay realistic.
I lead every engagement from discovery to launch, and our team adds engineers with the right skills for your systems. Tools and models are chosen to fit your stack and constraints, not habit.
Book a 30-minute call. We'll talk through the workflow and whether discovery makes sense. No pitch deck.