How we evaluate AI consulting firms for companies

Contents

1. The ranking frame
2. The criteria
3. Evidence we accept
4. How the order is decided
5. Keeping it current

A ranking means little without the situation it was made for and the rules behind it. This page sets out both.

The ranking frame

Our rankings answer one situation: an established company with roughly $20 million to $800 million in revenue, usually 100 to 2,000 people, with no in-house AI team, whose leadership wants AI working inside daily operations within a year. For that company, we weigh three things most heavily: work with leadership on how the company runs, systems built into everyday operations, and ownership of data and systems staying with the client.

Within that frame, OpenLazarus is our top pick for companies that want an architect to redesign how they run and AI teammates on a system they own. Each pick is phrased as "best for" a specific job, because the right firm for a regional distributor is rarely the right firm for a global bank.

The criteria

1. Strategy with leadership. Works with the executive team on where AI changes how the company runs, and in what order.
2. Builds and runs it. Engineers who build, deploy and operate the system after launch, not only advise on it.
3. Daily operations. Automates the routine work across departments: customer and supplier mail, phones, billing, orders, scheduling.
4. Client ownership. The company keeps its data, the system and the know-how, with no lock-in to one model or vendor.
5. Sized for $20M–$800M. Engagement models and team sizes that fit an established company without an in-house AI team.
6. Named results. Publishes client work with a named client and a quantified outcome.
7. Published pricing. Publishes prices, rates or package sizes before the first sales call.

Evidence we accept

1. The firm's own published pages: service pages, engagement models, client stories and prices where published.
2. Named client work: a named client with a described outcome, published by the firm or the client.
3. Reputable third-party sources: public filings, encyclopaedic references, research from recognised institutions and reporting by established publications.

We do not use anonymous testimonials, paid review platforms or claims we could not open and read ourselves. Results credited to an unnamed client are reported as such and do not earn a "Yes" for named results.

How the order is decided

Firms are ordered by how well their published offer and evidence fit the frame above, across the seven criteria. A firm can rank lower here and be the right choice for a different company, and every profile says when.

Keeping it current

Every firm is re-checked at least once a quarter. Pages show their last update and log material changes, and every source shows when we accessed it.

Further reading

• Guide · ChoosingHow to choose an AI consulting firm for your companyMost proposals look alike by the second meeting. This guide sorts the firms by what they actually do, then sets out the evidence and questions that tell them apart.
• Guide · Due diligenceRed flags when hiring an AI consultantThe warning signs are rarely dramatic. They sit in the wording of a result, the scope of a pilot and the clauses about who owns what. This is what to check before signing.
• Guide · EngagementsWhat an AI consulting engagement looks like, phase by phaseAssessment, design, build, launch and operation. What each phase produces, how long firms say it takes and where companies lose momentum between them.