How Avipra Is Different from a Typical AI Consultation

Almost every leadership team I meet has already had an AI consultation. Most came away with the same three things: a workshop, a deck full of use cases, and a proposal to build them. Six months later, they are still asking which of those ideas was worth the money.

That is the gap we built Avipra to close. Here is how our consultations work, and how they differ from what you have probably been offered before.

What a typical AI consultation looks like

Most AI consultancies follow a similar path: find use cases, assess your data, design a solution, build a prototype, deploy it, then train your team. None of those stages is wrong. The trouble is what happens between them.

The use cases are picked in a workshop rather than measured. The prototype is judged on a demo rather than on your real work. And the firm recommending the build is usually the firm that will bill for it.

The results show it. RAND estimates that more than 80% of AI projects fail, twice the rate of IT projects that don't involve AI. Gartner predicted that at least 30% of generative AI projects would be abandoned after proof of concept by the end of 2025, citing poor data, weak risk controls, rising costs and unclear value. And MIT's 2025 study of enterprise AI found that around 95% of organisations saw no measurable return from their generative AI pilots.

My observation: Most AI projects don't fail during the build. They fail in the decision before it: the wrong job was picked, it was priced at demo volume, and nobody checked the data.

1. We measure before anyone builds

A decision made twice is a build done twice. So before any code is written, we test each candidate use case on your own data.

We pull real work from your ticket queue and internal channels, grade a sample by hand, and score the tools you already run against it. That is your baseline, and most AI proposals never establish it. Then we run the AI approach on the same test set, with the same grading, and price both sides at your real volume, including what it costs to keep running after the demo is over.

The gap between those two scores is what your budget would actually buy.

2. "Don't build this" is a valid answer

Most candidate use cases don't clear the bar once you price them against the tools you already have. When that is what the numbers say, that is what our findings say. It is the cheapest result we can give you, and often the most valuable one.

My observation: A consultant who will tell you to skip an idea is the only kind worth paying for the ideas they tell you to build.

3. We plan, your team builds

We don't lock you into a long development contract. By default, we measure and specify, and your engineers build. The build plan is written for them, with the accuracy bar and the failure signals spelled out.

You also keep everything we produce: the test set, the grading notes and the runbook. Your team can re-run the measurement after we have gone.

And our advice is independent. We take no vendor commissions and hold no reseller agreements, so recommending a tool earns us nothing. Where work needs doing, we scope it, your team or a vendor carries it out, and we review the result against the requirements we wrote. That keeps us from being the people who both define the work and bill for it.

4. Every person in the room gets their own answer

AI projects stall when the people around the table want different things. The CEO wants to know whether it will move the business. The CFO wants the cost and the payback date. The CTO wants an architecture that won't need replacing next year. Engineers want to know how they will prove it works and keep it safe.

So we split our work by the decision each person has to make:

• AI Readiness: whether your data, access and people can carry a build at all
• AI Strategy & Roadmap for the CEO and CFO: which jobs pay back, what they cost, and in what order
• AI Stack for the CTO: the architecture, and the right tools for each layer
• AI Evaluation and AI Governance for developers and testers: how accurate it is, and the controls around it
• AI Security for security engineers: what an attacker could reach through it

Most teams need just one of these right now, the one that matches the decision they are stuck on. It is not a package you have to buy all of.

5. You get the arithmetic, not a deck

Our findings are a ranked table, not a slide deck. Every AI opportunity is scored on your own data and ranked by payback, with the numbers shown on each row and a build plan for the one at the top. Your engineers can check the work and disagree with it. A deck you have to take on trust is a different product.

6. We keep testing after the engagement ends

The frontier moves every few weeks. An AI job that was possible but too expensive last quarter may clear the bar this quarter, because models keep getting cheaper and better.

So we keep re-running your list against new models as they are released, and tell you the moment something crosses the line from "too expensive" to "worth building."

My observation: Most consultancies treat a recommendation as finished on the day it is delivered. In AI, a "not yet" can turn into a "yes" within a few months.

How an engagement starts

1. An intro call. You describe the workflow that is costing you the most. It costs nothing, and plenty of these calls end with us saying you don't need us yet.
2. A scoping note. We write back with the decision that is actually blocking you, the data we would need, and a fixed price. Not an hourly rate, and not a range.
3. A go or no-go. Nothing starts until the scope and the price are agreed in writing. If your security review needs to happen, it happens here, before any access is granted.
4. The sprint. Usually two to four weeks, on the narrowest read access we need, under an NDA. Your data never trains anything.

What I'd ask any AI consultant before signing

Whether you work with us or not, these questions will tell you a lot:

1. What will you measure, and on whose data?
2. Will you tell us if something isn't worth building?
3. Do you earn anything from the tools or vendors you recommend?
4. Who builds it, and who owns the code afterwards?
5. Can we re-run your analysis without you?
6. What happens when a better or cheaper model comes out next quarter?

A good AI consultation shouldn't leave you with more ideas. It should leave you with fewer, better ones, and the numbers to prove which one comes first.