The hard part isn’t building AI.
It’s making it change how decisions get made.

With commodity costs rising, SKU mixes diversifying, and consumer data multiplying faster than anyone can act on it, it’s harder than ever to manage operations in a consumer goods business. We help leaders drive measurable results, pairing deep consumer goods domain expertise with the same depth in AI. And the ideas below aren’t boilerplate; each one is focused on getting to an outcome you can put a number on.

With tariffs and uncertainty straining global supply chains, relentless pressure on inventory and assortment, increasingly savvy shoppers, and the rise of agentic commerce, it’s harder than ever to run a profitable retail business. All four pressures land in the same place: what to buy, where to put it, what to charge, and what to promise the customer. We help leaders get those decisions right, pairing deep retail and e-commerce domain expertise with the same depth in AI. And the ideas below aren’t boilerplate; each one is focused on getting to an outcome you can put a number on.

With rate cycles squeezing margins, tariffs redrawing trade lanes, shippers expecting real-time visibility as table stakes, and AI agents starting to quote and book freight, it’s harder than ever to run a profitable logistics business. All four pressures land in the same place: what capacity to commit, what to charge for it, and what service you can actually promise. We help leaders get those decisions right, pairing deep logistics and transportation domain expertise with the same depth in AI to create solutions that work for their business.

With domestic know-how retiring faster than it can be replaced, tariffs repricing inputs and supplier networks, demand swings whipsawing schedules built for steady runs, and boards expecting AI-driven results on the plant floor, it’s harder than ever to run a profitable manufacturing business. All four pressures land in the same place: what to make, when to run it, what to buy, and how to compete in a world where policies seemingly change daily. We help leaders get those decisions right, pairing deep manufacturing domain expertise with the same depth in AI. And the ideas below aren’t boilerplate; each one is focused on getting to an outcome you can put a number on.

With globally constrained capacity, exploding demand, export controls redrawing who you can sell to and where you can make it, product cycles compressing from years to months, and order books whipsawing on every AI buildout headline, high tech businesses face unprecedented challenges. Winning here comes down to a complex decisions made over and over: what to build, who gets the allocation, and what to promise a customer who’s planning two years out. We help leaders make those calls with confidence, pairing deep high tech and semiconductor domain expertise with the same depth in AI.

For twenty years the load curve barely moved; now data centers, electrification, and new industry are bending it upward faster than infrastructure built for flat demand can follow. The grind shows up in unglamorous places: transformers with multi-year lead times, vegetation cycles that never quite match the risk, storm rosters assembled by phone tree, and a work plan that has to survive both the weather and the rate case. That middle layer, forecasting, scheduling, materials, and field logistics, is where we work, building the models and agents that stretch crews, capital, and inventory further. The poles and wires are yours; the plan that keeps them ahead of demand is where we earn our keep.

Every barrel depends on a supply chain that rarely makes the headlines: rigs, crews, water, sand, pipe, and chemicals arriving in the right order at sites that change weekly, with prices swinging enough to redraw the plan mid-quarter. That’s where margin quietly leaks, in schedules that slip, trucks that run half empty, materials expedited at a premium, and maintenance that waits for a failure to make the calendar. This layer is our home turf: forecasting, scheduling, routing, and optimization, with agents that keep watch between planning cycles. We won’t tell your engineers anything about the subsurface, and we don’t need to; the money we find sits above ground.

Consumer demand keeps shifting underneath the product plan, the pressure to take out cost never lets up, dealer lots swing between too many units and the wrong ones, and tariffs keep repricing a supply chain that stretches across borders and three tiers of suppliers. None of the four can be managed alone; they collide in the plan, the mix you build, the costs you lock, the units you push to dealers, and the parts crossing borders to make them. We work in the layer where that plan gets made: demand and mix forecasting, supply and capacity planning, dealer inventory and incentives, and agents that catch a shortage while it’s still an email instead of a line-down call. The plants and the products are your expertis.., making the plan around them smarter is ours.

You’ve probably lived some version of it: short-dated stock written off in one market while the same molecule sits on backorder in another, held apart by country packs and release timing. Meanwhile the shelf-life clock burns while batches wait on release, a tender win doubles a country’s demand overnight, trial supply gets made for enrollment that may never materialize, and the capacity behind all of it was booked years before the results came in. We work inside that math: demand and allocation planning, campaign scheduling, cold chain logistics, and agents that answer “can we cover it” in hours instead of a two-week spreadsheet drill. The science is yours; our job is making the supply chain behind it as rigorous as the molecule.

Every portfolio company has an AI opportunity somewhere: development cycles that could move faster, industrial operations with margin still on the table, data that customers would pay for if anyone productized it. The hard part isn’t finding ideas; it’s picking the ones that pay back inside the hold period and show up where the exit multiple gets applied. That’s the lens we bring. We scope and build AI the way an operating partner would, sized to the value creation plan, sequenced for time to cash, and measured in EBITDA instead of demos, so the work returns for the company and compounds for the thesis.