Use-case backlog
A prioritized portfolio of AI opportunities scored on business impact, feasibility, and data readiness — with sequencing logic, not just a list.
Service · AI Adoption
AI Adoption That Earns Its Place in Production
From use-case discovery to live LLM and agent systems. We focus on AI that moves business metrics — not demos that look great in a deck but never reach an operator's desktop.
What you get
The deliverables
Concrete artifacts and working systems — not slideware. Each engagement scopes a subset of the list below based on what you actually need.
AI architecture
Reference designs for LLM, RAG, and agent systems on your cloud — model routing, retrieval, evaluation, observability, and cost controls.
LLM & agent build
Working systems delivered end-to-end: prompts, retrieval pipelines, tool integrations, agent orchestration, and human-in-the-loop where it matters.
Evaluation & guardrails
Automated evals tied to business outcomes, plus content safety, prompt-injection defenses, and red-team testing before launch.
Governance framework
Lightweight, auditable processes for use-case approval, model risk, data handling, and ongoing review — built to satisfy your risk team without slowing teams down.
Observability & ops
Tracing, cost monitoring, quality drift detection, and on-call runbooks so the AI system you ship is the AI system you can operate.
How we work
Four phases, weekly checkpoints
Expert-led delivery with clear go/no-go decisions at the end of each phase. You'll always know where the work stands and what's coming next.
Phase 01
Identify
Workshop with business and tech leaders to surface candidate use cases and assess what's worth doing first.
Phase 02
Prototype
Build a sharp, working prototype within 4–6 weeks — validated against real user tasks, not toy demos.
Phase 03
Productionize
Harden, evaluate, integrate, and pass the governance gates needed to put the system in front of real users.
Phase 04
Scale
Replicate the pattern across additional use cases and embed AI capability inside your teams.
Outcomes
What success looks like
Reasonable ranges from comparable engagements. We commit to specific numbers per program after discovery.
3–6×
Faster time-to-value vs. traditional pilot-to-production cycles, because we engineer for production from day one.
Measurable
ROI tied to a single business metric per use case — not vague claims about "productivity."
Governance
Approval workflows that protect the business without becoming the reason innovation dies in committee.
Have an AI use case that's stuck in pilot purgatory?
We'll diagnose what's blocking it and propose the shortest path to a production-ready system.
Schedule a Consultation