A practical path from AML value workshop to governed deployment.
Infinitive’s implementation model starts with measurable outcomes, validates data and controls early, and scales only after investigators and control owners trust the workflow.
90-day pilot structure.
Timing should be refined based on source system access, bank security requirements, and model governance review cycles.
Discover and align
Define the target use case, success metrics, stakeholders, existing controls, required data, constraints, and pilot decision process.
Build foundation
Land representative data, configure governance, set up environments, establish evidence lineage, and prepare policy corpus.
Configure SherlockAML
Deploy investigation surfaces, scoring/agent flows, graph views, narrative drafting, and human-in-loop review steps.
Validate and scale
Measure value, gather investigator feedback, test controls, document production requirements, and create the scale roadmap.
Business and technical work move together.
What the client should receive.
A successful pilot should create tangible business proof and reusable production assets.
Executive value case
Baseline, pilot outcomes, expected scaling value, risks, roadmap, and investment options.
Production architecture
Data flows, security model, integration design, environment design, operations model, and backlog.
Governance package
Lineage, access controls, MRM evidence, decision audit trail, human review gates, and testing approach.
Configured pilot
Working SherlockAML experience with selected data, typology, workflows, dashboards, and narrative capability.
Adoption materials
Training, role guides, SOP changes, feedback process, and investigator enablement materials.
Scale roadmap
Next use cases, data expansion, operating model, staffing, release plan, and change management milestones.
Do not treat AML AI as a demo. Treat it as a control transformation.
The pilot should prove measurable value while building the data, security, governance, and adoption foundation required for production.