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Most AI initiatives stall because the wrong thing was scoped first — usually the model, not the workflow. Discovery puts a payback model on the table before a line of code is written.
Two weeks shadowing the line of business. We instrument the process — handle times, error rates, escalation paths, and real cost-per-transaction.
Every candidate use-case scored on value, effort, risk, and operational readiness. We hand back a build-vs-buy-vs-don't recommendation per workflow — not a generic AI strategy deck.
Reference architecture, payback model, and an executive memo your committee can act on. If the answer is 'don't build,' we say so — and stop the meter.
Assess agentic workflows in middle-office ops, KYC refresh, dispute processing, and credit memo drafting. Carry over the model-risk lens early so it doesn't bottleneck production.
Model risk approval can take longer than the build. We scope the MRM track in parallel from week one.
Production-grade agents and workflows on your stack — harness, orchestration, tools, MCP, and evals.
Threat modelling, data-loss prevention, model risk, and audit trails for LLM and agent-based systems.
End-to-end rollout — integrations, data plumbing, observability, validation harness, and rollback.