Canonical reference for Sophon Consulting

ai-opportunity-triage

Triage one proposed AI use case into a go-deeper or hold recommendation by scoring bottleneck severity, value capture, integration readiness, and risk posture, then ranking the follow-up questions that would most change the decision. Use when someone asks whether an AI idea is worth pursuing, which of several AI proposals deserves discovery time, or what evidence is missing before committing further effort. Not for organization-wide readiness reviews or production rollout planning.

ai-readiness-assessment

Assess an organization’s readiness to run AI in production across data quality, workflow ownership, governance, delivery capacity, and executive sponsorship, producing a scored readiness report with a prioritized use-case shortlist, a risk register, and a 30-60-90 day sequencing plan. Use when someone asks whether their company or team is ready to adopt AI, where to start with AI, or which AI use cases to prioritize before committing budget. Not for triaging a single AI idea or planning an existing pilot’s rollout.

ai-pilot-to-production-plan

Turn an existing AI pilot into a gated production rollout plan on a roughly 90-day arc — evaluation baselines, hardening, controlled rollout, and cutover — with measurable exit gates, named owners, and rollback triggers. Use when a pilot or prototype already works and someone asks how to ship it to production, scale it safely, or judge whether it is ready to launch. Not for choosing a first use case or assessing organizational readiness.

human-in-the-loop-governance

Design human oversight for an AI system by classifying its actions by impact and reversibility, producing a governance matrix that states what runs autonomously, what requires human review before action, and what stays human-decided, plus escalation, override, and audit-logging policy. Use when someone asks where humans should approve AI actions, how to add review gates without collapsing throughput, or how to make AI oversight auditable. Not for rollout scheduling or use-case selection.

ai-evaluation-sprint

Plan a bounded 48-hour evidence sprint that turns an imminent AI decision — vendor choice, build approval, workflow commitment — into a recommendation memo with an explicit confidence level, by assigning one owner per risk area and keeping every evidence request tied to the decision. Use when a decision must be made within days, momentum is real, but the supporting evidence is scattered across demos, notes, and inboxes. Not for first-pass screening of a new AI idea (use ai-opportunity-triage) or for open-ended evaluation programs with no decision deadline.

ai-cost-reliability-review

Review the cost and reliability of an AI/LLM stack already serving production traffic: inventory request classes, baseline cost, latency, failure, and human-rework metrics, then recommend routing, caching, fallback, and measurement policies — with every vendor price or discount verified fresh, never recalled. Use when someone says model spend is volatile or unexplained, asks how to cut LLM costs without losing quality, or needs reliability policies for production AI traffic. Not for choosing a provider (use ai-model-selection-eval) or planning a pilot rollout (use ai-pilot-to-production-plan).

rag-vs-fine-tuning-decision

Decide between retrieval-augmented generation, fine-tuning, or a hybrid for grounding an AI system in internal knowledge or domain behavior, producing an architecture decision memo that weighs knowledge freshness, citation and permission requirements, and the behavior gaps that remain after retrieval and prompt optimization. Use when someone asks whether to use RAG or fine-tune a model, how to ground a model in company knowledge, or whether training on internal data is worth it. Not for choosing a provider (use ai-model-selection-eval) or assessing overall AI readiness (use ai-readiness-assessment).

ai-model-selection-eval

Design a workload-grounded evaluation for choosing an AI model or provider, producing an evaluation design plus a selection memo template based on your own task classes — output quality, tool-call reliability, latency and cost distributions, and governance fit — instead of headline benchmarks. Use when someone asks which model or provider to use, whether to switch models, or how to compare platforms for a production workload. Not for ongoing cost tuning of a stack already in production (use ai-cost-reliability-review) or for the RAG-vs-fine-tuning architecture question (use rag-vs-fine-tuning-decision).