AI strategy & roadmaps
Assess where AI creates measurable ROI in your operations, define success metrics, and sequence builds that ship to production — not endless discovery phases.
Services
AI systems consulting and implementation: underwriting automation, dynamic pricing engines, intelligent document processing, conversational agents, and MCP integrations.
Assess where AI creates measurable ROI in your operations, define success metrics, and sequence builds that ship to production — not endless discovery phases.
Design extraction pipelines for policies, submissions, and loss runs with validation loops, provenance, and accuracy targets your underwriters will trust.
Integrate machine-learning signals with actuarial rating grids in low-latency APIs — with explainability and governance built in from day one.
Connect AI assistants to your data sources and workflows via Model Context Protocol servers, skills, and production-grade tool design.
Representative production outcomes from Insly AI programs Ando led — full methodology on each case study page.
Production systems for insurance and regulated enterprises: intelligent document processing, dynamic pricing APIs, conversational agents, and MCP integrations. Engagements are hands-on from architecture through deployment — not advisory-only retainers.
Pilot pipelines often reach production validation in 8–12 weeks when data access and underwriting stakeholders are available. Pricing and document-AI modules at Insly scaled to multi-tenant production in roughly one quarter after shadow-mode approval.
Yes, when the problem involves production AI with measurable accuracy, latency, and governance requirements — similar to regulated document workflows or real-time decision APIs. Insurance depth is the primary domain.