AI Consulting ServicesDiscover · Rank · Cost · Pilot

When is AI consulting worth paying for, and when is it not?

It is worth it when the AI decision is expensive to get wrong: a customer-facing assistant that could give wrong answers, a feature that sends client data to a third-party model, a budget that has to be defended to a board, or a long list of ideas and no agreed place to start. Independent AI strategy consulting earns its fee when it stops one pilot that was never going to work, or points the first one at the use case with real value.

It is not worth it when the use case is small, internal and reversible. If a team wants to try an AI writing or meeting-notes tool on a monthly subscription, a month's trial teaches more than a report. A good adviser says so at scoping, before any fee is agreed.

How are AI use cases found and ranked?

AI use case discovery starts with the work, not the technology. We interview the people who run each process, read samples of the tickets, documents and forms they handle, and list every task where a model could read, draft, classify, search or answer. The long list usually mixes strong ideas with ones a simple rule or a better search would solve.

Each candidate is then scored and plotted, so the order is visible rather than argued over:

• Value: hours saved, revenue affected or errors avoided, in your own numbers
• Feasibility: whether the data exists, is clean, and may be used
• Risk: what a wrong answer costs and who would notice
• Effort: a subscription tool, a ready-made base, or a custom build
• Alternative: the non-AI fix and what it would cost

What does an AI readiness assessment check?

An AI readiness assessment asks whether your data, systems and team can support the use cases at the top of the list. Many AI projects stall at exactly this point, after a demo that ran on clean sample data.

Where the data falls short, the report prices the fix, which is sometimes the real first project and belongs with data engineering rather than AI. The assessment checks:

• Where the data lives and who controls access to it
• Whether it is current, complete and consistently labeled
• Whether consent and contracts allow its use with a model
• Whether documents are in a form a retrieval system can read
• Which systems an AI feature would have to connect to
• Who in your team would own the feature after launch

Should you build AI, buy a ready-made product, or subscribe to a tool?

Build vs buy AI comes down to how specific the use case is. A subscription tool fits a common task many companies share, such as drafting or summarizing: it is the fastest start, but the vendor controls pricing, features and where your data goes. A ready-made base such as the Miracuves ChatGPT clone or Bolt.new clone fits when you want your own branded AI product with full source code; the base platform launches in 6 working days, and changes on top of it are custom work.

A custom build fits when the value sits in your own data and workflows, for example answers grounded in your documents through RAG development, or models wired into existing systems through AI integration. Custom work at Miracuves runs 2-8 weeks, with complex scope quoted in writing. The report compares all three routes over the same period, running costs included.

How do you estimate what an AI feature will cost to run?

Build cost is the easy number. Running cost is the one that surprises people, because most hosted model usage is billed per token and grows with every user and every longer prompt. We estimate it from the use case itself: requests per day, how much text goes into and comes out of each one, which model tier the task really needs, and how often a request is retried.

The estimate is shown at three volumes, today, expected and peak, and covers the costs beyond the model:

• Model usage, priced from each provider's rate card on the date of the report
• Hosting for the application, plus a vector database if retrieval is used
• Monitoring, logging and regular evaluation runs
• Staff time reviewing outputs the model is unsure about

Which risks and governance questions should an AI roadmap answer?

Four at minimum, each with a named owner in your business. Privacy: which personal or client data would reach a third-party model, under what retention terms, and whether it can stay in one region. Bias: whether outputs differ unfairly across customer groups, tested on real examples before launch. Intellectual property: who owns prompts, outputs and generated code, and whether any reference data carries license limits. Disclosure: when users must be told they are dealing with AI; laws such as the EU AI Act already require it for some systems.

None of this needs a certificate to start, but it has to be written down before a pilot touches real customers. The roadmap lists each risk, how it is reduced, and the check that shows the reduction is working.

What makes a good AI pilot, and when should it stop?

A good pilot is small, real and measured. It runs on real data with a limited group of users, for a fixed period, against a measure agreed before it starts: time per support reply, share of documents processed without correction, or answer accuracy on a test set of real questions. The current figure is recorded first, so there is something honest to compare against.

Stop criteria go in the same plan. If accuracy stays below the agreed line, if running cost per task exceeds the saving, or if users route around the tool, the pilot ends and the roadmap moves to the next use case. A pilot that cannot fail is a demo. When an AI feature is already live and struggling, AI app rescue is the closer fit.