Automate Product Operations with AI.

We map your existing product operations workflow, identify high-volume and high-structure tasks, build an AI agent that handles those tasks, and route low-confidence cases to a human reviewer. The build connects to the systems your industry already runs on, runs against a labelled test set, and ships behind a reviewer queue before it sees production traffic. We measure feedback cycle time, roadmap confidence, launch readiness, and adoption from day one and improve weekly.

There is no single off-the-shelf "best" agent for product operations — the right architecture depends on the systems and data of your industry. We typically combine a frontier LLM (Claude, GPT-4-class, or Gemini) with a retrieval layer over your approved sources, tool-use for your stack, and a reviewer queue. We benchmark candidates against a labelled test set during Discovery and pick the model with the best accuracy/cost ratio.

Three phases, billed separately. Discovery sprint: $6k. Build engagement: $22k–$30k. Run retainer: $3k–$5k / mo. ~$34k–$60k typical year 1 (60% take the run option for ~6 months). Pricing varies slightly by industry — see the industry-specific pages below.

Thin-slice in production in ~6 weeks after Discovery, full Build phase over 7-10 weeks. By day 90, feedback cycle time, roadmap confidence, launch readiness, and adoption is instrumented and you have a baseline against which to expand to adjacent workflows.

0 industries listed below have a scoped engagement page for product operations, each with industry-specific systems, controls, and KPIs. Common starting industries include , and others. Don't see yours? We build for any sector — tell us about your workflow and we'll scope it.

We own workflow design, prompts, retrieval architecture, evaluation harness, and weekly improvement. You own data access, policy, exception approval, and final commercial decisions. At the end of the engagement, every prompt, eval, and config is handed over — no lock-in.