AviKaiDo builds production AI products and guides teams through implementation, agentic automation, and conversational systems with clear ROI.
Capabilities
AviKaiDo provides AI implementation consulting, agentic automation, custom AI product architecture, and conversational CX systems for mid-market and enterprise teams. Engagements end with production workflows, owned artifacts, and clear KPIs — not a recommendation deck.
Readiness assessment, build-vs-buy calls, architecture blueprints, and sequenced roadmaps tied to measurable business outcomes.
Production agents wired into your real systems — Slack, CRM, EHR-adjacent workflows, support, ops — with human review and evals.
RAG platforms, LLM selection, observability, and delivery standards so internal AI products launch faster and run reliably.
Chat, voice, and omnichannel assistants grounded in NLP/LLM best practices — designed for CSAT, containment, and trust.
Outcomes
Across healthcare, finance, retail intelligence, and travel, AviKaiDo’s founder has led AI products from strategy through production — oncology assistants, bank-scale voice/chat, pharmacy CX, and ML portfolios — with documented cost, speed, and experience gains.
AstraZeneca
Led the move from vendor chatbot to a customized internal platform with RAG across OpenAI, Llama, Claude, and SageMaker — improving NLU, content generation, and transcript intelligence over 1TB of historic data.
Wells Fargo
Directed multi-disciplinary teams shipping proprietary chatbot/voice/NLP platforms, then led the transition to a cloud vendor platform through POC, RFP, and governance for tens of millions of users.
CVS Health / Aetna
Standardized conversational design across VoiceFlow, Dialogflow, Nuance Mix, and Watson; applied LLM analysis on 2TB+ of support transcripts to accelerate intent work and launch omnichannel experiences.
Engagement model
AviKaiDo engagements follow a three-phase model so strategy and delivery stay with the same accountable builder. Each phase is a separate decision: take the artifacts in-house after any one of them, or continue into production operations.
Map the workflow, baseline human performance, pressure-test build-vs-buy, and produce a fixed-scope statement of work. If AI is the wrong lever, we say so.
Ship a thin production slice on real traffic early, then expand. Models, retrieval, agents, integrations, evals, and observability — owned by your team.
Monitor KPIs against baseline, refresh prompts as data drifts, tune reviewer thresholds, and train your operators so the system keeps paying off.
Industries
Deep experience in regulated and high-scale environments — including Epic EHR–adjacent healthcare workflows, oncology support, pharmacy CX, and bank-grade conversational AI.
Bring the workflow that is costing the most time or money. We will tell you honestly whether AI is the right lever — and what a first shippable slice looks like.