We build real AI capability, not demos. Reviewers check every agent call early on, and autonomy grows only after it’s earned. First production outcome in 12 weeks.
Take a prioritized use case. We build it to production, with operators trained to review decisions, eval gates on every deploy, and a staged rollout your team controls.
Input: Prioritized use case, access to production systems, a sponsor staking an outcome.
Goals: Land first outcome. Prove the pattern. Earn autonomy evidence. Build operating muscle.
Type of impact: First outcomes, time-to-value, proven pattern, trained operators, evidence-based rollout.
Workflow, UX, data, and architecture mapped together and signed off before a line of code gets written.
Working agents with a living eval suite that gates every change from dev through production.
Shadow mode first, then approve mode, then autonomy. Each step only when the numbers justify it.
Trained reviewers, escalation playbooks, and review UX ready for day-one production oversight.
Solution blueprint, UX, hybrid architecture baseline. Eval scaffolding live. First agent building in dev.
First agent in shadow with HITL review. Evals gating every deploy. Operators trained and ready.
First agent in approve mode with measurable business outcome. Pattern captured for reuse across the org.
Reviewers check every decision early on. Autonomy earns its place. We don’t assume it going in.
Processes prior auth requests end-to-end, with reviewers in the loop until confidence is solid. On live payer deployments, cycle times have come down from days to hours.
Automated Trial Master File audit readiness. Classifies, validates, and flags missing documents against ICH-GCP requirements.
Pricing and assortment decisions driven by live demand signals: markdown timing, supplier negotiations, and inventory calls, without waiting on weekly reports.
Spend categorization, supplier acceptance models, and savings tracking across indirect procurement, all running with minimal human review once the models are dialed in.
For one of the ten largest tech companies in the US, we rebuilt indirect procurement around AI. Manual review gave way to acceptance models that improve with every decision. Avoidable spend fell from 6% toward a 3% target. The system now covers $1B+ in enterprise spend.
We don’t start unless there’s a clear use case and a sponsor who owns the number. If you have both, let’s talk.