A boutique AI advisory for operations-heavy businesses, where a wrong answer carries a real cost. We design and build the applied AI systems that run the physical world.
We spend as much time defining the problem as solving it, running discovery with your team to land on the use case worth doing, with a clear value assessment and the economics of ROI before any code.
We build the solution alongside your own experts: the math, the data plumbing, and the interface people use. We run small experiments to lower the cost of being wrong, and keep what proves out.
Stand it up in production and stay until your planners and operators run it themselves. No status theater or polished slides, just real progress on the hard part.
Predictive maintenance, network optimization and forecasting for flight ops, MRO and supply chain: fewer delays, longer asset life, FAA / EASA standards.
View →Demand forecasting, assortment and logistics optimization: less waste, fuller shelves, FSMA-compliant end to end.
View →Forecasting, agentic workflows and optimization for clinical ops, revenue cycle and member services: faster throughput, cleaner claims, HIPAA-safe.
View →Computer vision and analytics for broadcast, performance and officiating: sharper insight, richer coverage, integrity intact.
View →Feasible values true expertise, and always questions assumptions.
A senior AI and operations-research bench on the problem in weeks, not the months it takes to recruit and ramp your own. And independent advisors bring what a hire can't: competitive intelligence and patterns from across the field.
A clear value assessment and the economics of ROI up front, so you fund the initiatives worth doing and stop the ones that aren't, before the budget is gone.
The people who scope it are the people who build it. No junior hand-off, no learning on your dime, no consultant carousel.
Straight answers to the questions decision-makers ask first. Turn the dial to a topic.
Two kinds of operations-heavy teams. AI-native startups exploring different technologies who need a clear-eyed partner on what to sequence and what's actually worth building. And the innovation and R&D arms of enterprises in regulated, operations-intensive domains. Both share a trait: the work runs the real world, so they benefit most from deep domain expertise, not generic AI advice.
No. We do the strategy work a consulting firm would do, and we ship the production systems an engineering team would ship. Most firms pick one; we do both, because the work needs both. We hire in-house engineering talent to deliver, partner with specialist teams when the skills sit outside our bench, and are happy to provide vendor recommendations. If we are owning the outcomes, the responsibility lies with us.
Four to six months end to end: a four-to-six-week Discovery that produces a Build Plan, an eight-to-fourteen-week Build that ships a working system into production, and a thirty-day handoff.
Scope drives it, though. A tightly scoped single workflow moves faster than a multi-system program, so we phase the work to land value in stages rather than all at the end. We don't sell open-ended engagements; the artifacts and gates are agreed at kickoff.
Three engagement shapes, each fixed-fee: a Discovery sprint, a Build, and an optional post-handoff advisory retainer. Every engagement is scoped around a specific outcome, a working system in production, against a measurable baseline, with named success criteria agreed at kickoff. That outcome is what we're on the hook for.
We also offer outcome-linked upside against the Value Realization Plan: when the system performs above target, we share in it. What we don't do is sign contractual guarantees on specific accuracy numbers or business KPIs months before the system is built. Those guarantees distort engineering, and you don't want a partner/vendor whose only incentive is to claim the number. Specifics come in the first conversation.
Yours. Your data, your workflows, your competitive insights, the systems we ship, all yours. We retain rights to our methodology, our reusable patterns, and the right to reference the engagement once it's sanitized and you've approved it. We also keep the right to serve adjacent customers in your vertical. Standard professional-services posture, written into the MSA before kickoff so it's never a surprise later.
Three ways, none of which are about size.
The honest version: if you need a 200-person team for a multi-year transformation program, we're not it. If you need a lean senior team to ship one production system in a regulated environment without theater, we are.
We already work alongside them; we're a member of their partner programs, and the frontier labs build the best models and developer tools in the world. We use them daily.
The difference is what we optimize for. They're rightly focused on advancing model capability: more tokens, more agents, more reasoning. We're focused on whether the workflow gets a better answer at the end, which sometimes means a frontier model, sometimes a classical optimizer, and sometimes questioning whether the agent needs to be there at all. They're partners, not competitors, and we've found the labs themselves prefer it that way.
Not yet, and not by accident. Feasible is a services practice first. But as we run enough engagements inside a given vertical and keep seeing the same problems, we plan to build vertical accelerators: packaged evaluation and grounding sets, curated domain data, and regulatory rule libraries tuned to a specific industry. When that's ready it'll be an optional SaaS subscription that sits alongside the advisory work, never a replacement for it.
Deliberately small, deliberately senior. Every engagement has a named primary and secondary owner, both senior practitioners. We partner with specialist firms for domain depth and with frontier labs for architecture support, and we don't take more engagements than we can staff at the depth our work requires. The smallness is a constraint we manage, not one we're trying to grow out of.
In workflows where the R&D depth genuinely matters, like novel optimization problems, frontier operations-research methods, and applied ML research that hasn't yet hit production, we partner with academic affiliates who have spent careers in those problem spaces. It compounds in both directions: their research gets a real deployment surface, and our engagements gain depth no consulting firm can build internally without a decade of head start.
Evaluation is the spine of the engagement, not the last chapter. Before we build the agent we specify the eval harness: golden sets, rubrics, regression bench, autonomy-band promotion gates, kill conditions, audit logging.
The way AI projects fail in regulated industries is almost always ship the agent, struggle to evaluate it, can't justify production autonomy, abandon the project. We invert that order. The harness ships before the agent does, and the agent only earns higher autonomy by passing evaluation evidence.
We ship. POCs that never reach production are how most enterprise AI investments quietly die. Every engagement is scoped from day one to ship a working system your team can operate without us: named on-call owner, runbook, observability, regression bench. The slice we ship is real and lives in production. Demos are not the deliverable.
As an embedded advisory team, not an arm's-length vendor. We work inside your workflows, in continuous iterative feedback rather than staged hand-offs, and we'll tell you whatever needs to be said to make the work succeed.
We're collaborative by design: custom work means more collaboration, not less. We err on the side of more time together than not, and we work curiously, treating the problem as the thing to understand before it's the thing to solve.