The Institute for AI Outcomes works with enterprises and large institutions to move AI work past pilots and proofs-of-concept into systems that produce measureable results enhanced by harnesses, context, and recent AI research — on the balance sheet, in the warehouse, across opportunity-focused education contexts.
A specialty carrier had a credible structured-data risk model and a quiet suspicion that most of the signal on each submission was sitting in broker emails, inspection narratives, and scanned loss runs the model never saw. We built a document-understanding layer that parses, grounds, and attributes unstructured submission data into engineered features the existing GLM consumes — with full lineage back to the source paragraph.
A national distributor was stocking thousands of perishable SKUs across regional DCs against a forecast that hadn't kept pace with shifting restaurant traffic, weather, and menu cycles. We replaced a planner-driven weekly process with a hierarchical demand forecast feeding directly into replenishment and lane-level routing — refreshed nightly, and built to surface its own uncertainty so it isn't trusted when it shouldn't be.
A national organization working on the US teacher pipeline needed to meet career-changing candidates where they actually are — confused about which alternative-certification programs apply to them, in which states, with which prior degrees. We built a guided-discovery agent that maps each candidate's background to eligible programs across 10 priority states, surfaces fit signals (cost, time, subject area), and hands warm leads to program partners with consent.
Contributors have built and shipped AI at Google, Microsoft, and VC-funded startups. We assemble the right people for each engagement and are explicit about who is doing the work.
Before a line of code, we write down with your team what success looks like on the P&L or the balanced scorecard — and what we'd rather not ship than compromise on.
Engagements run 8–16 weeks with a contributor on every call. We prototype in your data, not a sandbox, and resist the urge to rebuild systems that already work.
Every engagement ends with a runbook, an evaluation harness, and a named owner on your side. We'd rather make ourselves unnecessary than build a dependency.
We take on a small number of engagements each quarter. A 30-minute call with a contributor is the fastest way to find out whether we are a fit.
Or write directly · hi@outcomesinstitute.ai