Claude Consulting for enterprise AI strategy and adoption

Focus20 Labs delivers Claude consulting in both markets and for globally distributed teams. It is a Claude Partner Network member with a Claude Code partner badge verified on Credly, and the practice spans strategy and governance through to production delivery — Claude corporate training, Claude Code training, adoption consulting, workflow implementation and AI agent builds.

A Claude consultant decides where Claude should be used first, writes the governance it runs under, and gets the first workflows into production. In practice that means observing how your teams actually work, ranking candidate workflows by what they would return, setting data and approval rules, and building an evaluation set from your own cases so nothing ships on a demo. At Focus20 Labs the same engineers who write the roadmap can build what it recommends.

It depends on the stage. Claude training runs 2–5 days. Discovery and governance, which produces the ranked workflow list and the governance model, typically takes 2–4 weeks. Implementing the prioritized workflows in production typically takes 4–12 weeks or more, depending on the systems involved. Each stage stands on its own, so you can stop after any of them.

Yes. A rollout is where most governance decisions become real: which surfaces are approved (Claude apps, Claude Code, the API and each MCP server separately), which data classes may reach the model, how audit logging and retention are set, and which teams are trained first. We work through those before licences go wide, then run role-based training and discovery so usage turns into changed workflows rather than a handful of enthusiasts.

General AI consulting usually stops at the model-selection question. Claude consulting assumes the platform decision and goes deeper into what that platform actually requires: prompt and context design, Claude Code workflows for engineering teams, MCP integrations into your existing systems, evaluation harnesses, and the guardrails that let an agent act without a human in the loop. The depth is the point.

You do not have to, but partner status is one of the few externally verifiable signals in this market. It means Anthropic has recognised the practice rather than the practice recognising itself. Ask any prospective partner for the credential URL and check it.

A prioritized use case portfolio with ROI estimates against each item, a board-ready governance model covering policies, risk review and decision rights, an adoption plan with the change management attached, and a 90-day execution sequence. It is a document you can act on with or without us.

A strategy firm hands over the recommendation. We are an engineering consultancy, so the people who ranked the workflows can also ship them — which tends to change what gets recommended, because nobody wants to be handed their own unbuildable roadmap.

Claude is the default because it is what we know deepest and it holds up in production. But discovery starts from your workflows, not from a model choice — if something else fits your constraints better, we will say so.

Four written artefacts and the controls that enforce them. An acceptable-use policy saying what Claude may be used for by role; a data classification standard saying which data may reach a model and which must be redacted first; a decision-rights matrix naming who proposes, who approves and who can switch each workflow off; and a guardrail and escalation specification per workflow. Those are then enforced at the Claude layer — tool and MCP allowlists, permission modes, model pinning, redaction before the request and audit logging — because a policy that is not enforced in the runtime is a slide.

You do, per workflow, in writing, before launch. Our part is making the question concrete enough to answer. The general position we recommend: an agent may read, retrieve, summarise, draft and propose freely, and may act end to end on reversible low-impact steps inside a bounded scope. Anything that posts to a ledger, a payment rail or a system of record, anything that reaches a customer under your name, anything below the workflow's confidence threshold, and anything irreversible waits for a person. Cases the workflow has not seen before queue as exceptions rather than being guessed.

Against an evaluation set built from your own historical cases, including the awkward ones — not against a public benchmark. We write down what a correct result is before building, baseline how the workflow scores next to how a person scores on the same set, and agree an accuracy threshold and a tolerable failure mode in writing. Below the bar it does not go live. The set is then re-run on every prompt, tool or model change, so a model upgrade is a test rather than a hope.

A cadence rather than a checklist. Weekly review of exception queue depth and override rate through the first month; monthly re-run of the evaluation set with accuracy and cost reported against the agreed threshold; a mandatory re-run before any model upgrade, with the pinned version staying until the new one passes; a quarterly check of whether policy and decision rights still match how teams actually work; and an annual scope review of which workflows earned more autonomy and which should have less. Every live workflow has one named owner accountable for it.