Objective review of your stack, data flows, and delivery practices. We identify risk, waste, and fast wins to improve reliability and speed.
Architecture & platform review (cloud, data, security basics)
Cost, performance, and reliability opportunities
Team/process diagnostics and delivery bottlenecks
Typical: 2–4 weeks • Executive report + prioritized roadmap
Practical AI that pays its way—use cases, data readiness, safety, and delivery. From roadmap to pilot to production.
Use-case discovery, ROI modeling, and risk guardrails
RAG, agents, and workflow automation (cloud-native)
MLOps, evaluation, observability, and cost control
Typical: 4–12 weeks • Pilot in 30–60 days, scale as warranted
Detection, classification, OCR, and visual QA—from data labeling to deployment on edge or cloud.
Model selection & training (classical + deep learning)
Dataset strategy, annotation QA, bias & drift checks
Real-time inference pipelines (edge, GPU, serverless)
Typical: 6–16 weeks • Proof-of-value then production hardening
Hands-on execution and interim leadership to unblock teams and ship. We bring calm process and crisp accountability.
Fractional CTO/VP Eng, product ops, and PMO setup
Backlog to roadmap: sequencing, resourcing, governance
Metrics that matter: throughput, quality, cost, risk
Typical: 1–6 months • Part-time or outcome-based engagements
