Naperian was founded on the belief that the most valuable work in artificial intelligence isn't happening in research labs — it's happening, quietly, in companies that finally have the right tool for a problem they've lived with for years.
Napier's logarithms turned multiplication into addition. They let astronomers and navigators do in an evening what used to take a month. The mathematics didn't change the universe; it changed how much of it a person could reach in a day.
The number that bears his name — Euler's e — describes how things grow when nothing gets in the way. It shows up wherever a quantity feeds back on itself: compound interest, population dynamics, neural networks, the half-life of friction inside a company.
That's the name. The work follows from it. We're interested in the part of your business where each improvement makes the next one easier — the part that compounds. That's where AI earns its keep, and that's where we focus.
These aren't slogans we trot out at sales meetings. They're the operating rules we apply when we decide whether to take a project, how to design the work, and what to recommend when the honest answer is "not yet."
Hossein holds a PhD in Civil and Environmental Engineering from Carnegie Mellon University, where he worked in the Multiscale Mechanics Research Group under Professor Kaushik Dayal. His research modelled how materials behave across vastly different scales simultaneously — the kind of work that trains a specific instinct: understanding not just whether a system works, but why it works, and where the seams are. That habit of thinking at multiple levels of abstraction turned out to be directly useful when applied to AI systems and the businesses that run on them.
Before founding Naperian, he worked in enterprise cloud and technology at Trusted Tech Team, where he built deep familiarity with the Microsoft stack — Azure, M365, and the infrastructure most small and midsized companies already depend on. The move into AI consulting came from a recurring observation: the firms doing serious AI work were built for Fortune 500 clients, with timelines and price tags to match. The businesses between 20 and 500 people — the ones with real operational problems and no tolerance for a six-month discovery phase — were left to figure it out themselves.
Naperian is the firm he kept wanting to hire but couldn't find. The name comes from John Napier, the Scottish mathematician whose invention of logarithms turned work that used to take months into something a person could do in an evening. That's the ambition: not AI for its own sake, but AI that quietly changes how much your team can reach in a day.
Occasionally, when the engagement is shaped like SMB work — one accountable team, a clear scope, and a willingness to move quickly. We don't pursue Fortune 500 transformations. The center of gravity for our practice is companies between roughly 20 and 500 employees, where a single project can meaningfully change how the business runs.
Whichever stack earns its keep inside your environment. In practice that means frontier models from Anthropic, OpenAI, and Google for reasoning; smaller open-weight models where latency, cost, or privacy matters; orchestration with the lightweight framework that fits the job; and integrations into the SaaS tools your team already uses (CRM, ticketing, docs, communication, billing).
We have no platform partnerships, so the recommendation is made on merit. We'll happily explain the trade-offs we considered for your specific case.
Fixed-fee for scoped projects, retainer for ongoing work, hourly only for narrow advisory. We'll quote on the scope and the outcome, not on a body count. Strategy engagements typically run in the low five figures; implementation work depends entirely on the surface area, and we'll be specific once we understand it.
A first conversation within a few days, a written proposal within a week of that, and kickoff inside two weeks of signature for most engagements. We keep our pipeline deliberately narrow so we can move when a client is ready to move.
No. We work exclusively with API providers and configurations that exclude your data from model training, and we'll document that in writing as part of every engagement. For sensitive workloads we'll design around data residency and access controls before we write a line of integration code.
Cash, generally. We've taken equity once or twice when the alignment was right and the company was clearly the kind of business we wanted to be associated with for years. It isn't the norm and we don't market it.
We tell you. Sometimes the answer is a smarter form, a workflow change, a better hire, or a tool you already own that nobody finished configuring. Recommending AI when something simpler would do is how consultants lose clients over the long run, and the long run is the only version of this business we're interested in.