Best AI consultancies for startups, 2026

Every firm on this list will tell you it delivers business outcomes. Not one of them will tell you what it costs, and not one of them commits in writing to checking whether the outcome arrived.

We opened all seven websites on 12 September 2026 to write this. Six publish no price. The seventh publishes a range and explains what moves it. On measurement, the score is worse: several promise return on investment as a service, and none of them says what happens if the number does not move.

That is not a scandal. Consulting has always priced by scope. It does mean the two things a founder most wants to know are the two things you will not learn before a sales call, so this list is organised around getting them answered quickly.

First, work out which thing you are short of#

The firms below sell two different products under one word.

Capacity. You know what to build. You need people who can build it, in your stack, without a hiring round. Most of this market is this, and it is the easier problem, because you can judge the output.

Judgement. You do not know which of nine ideas is worth the money. You need someone to go through how the company works and come back with a short list, and more usefully, a list of what not to build. This is rarer and harder to buy, because the deliverable is a decision rather than software.

Buying the first when you needed the second is the common and expensive mistake. It produces a working system nobody needed, on time and on budget.

How we evaluated#

Every claim below comes from the company's own website, read on 12 September 2026. Where a firm publishes no pricing, this says so rather than guessing a number. Nobody paid to be included, and one firm on this list is us, which is disclosed again at the entry itself.

We did not rank them one to seven. A ranked list implies one answer, and the honest finding is that these firms are for different situations.

The seven#

BCG X#

The tech build and design unit of BCG, with nearly 3,000 experts across 80 cities. It partners with what its own page calls the world's largest organisations.

Genuinely good at: operating at a size nobody else here can. If the work spans several business units and needs board sponsorship to survive contact with the organisation, this is the category that does that.

The honest problem for a startup: you are not the client this is built for. A seed-stage company bringing in a unit of 3,000 people is buying a process designed to move a company with 30,000 employees, and paying for the machinery that makes that possible.

Pricing: not published.

LeewayHertz#

Fifteen years old, serving startups and Fortune 500 companies, with its own generative AI platform, ZBrain, alongside the services business.

Genuinely good at: breadth and longevity. Fifteen years in software predates the current cycle by a decade, which means the company has shipped things that had to keep running.

The honest problem: a firm with its own platform has a preferred answer. That is not automatically wrong and it is worth knowing before the first call, so ask early what the recommendation would be if ZBrain did not exist.

Pricing: not published.

HatchWorks AI#

Describes itself as a pure-play AI partner. Its three named services are GenROI for prioritising and sequencing AI opportunities, GenDD for building to production, and GenEQ for adoption. It uses embedded senior engineers it calls Forward Deployed Engineers, with US and nearshore talent.

Genuinely good at: the sequencing step. GenROI is the closest thing on this list to what we do first, and a firm that names the prioritisation stage as a product is thinking about the right problem. Embedding senior engineers rather than shipping a spec over the wall is also the model that works.

The honest problem: the client logos are enterprises. Ask whether the same engineers work on a startup-sized engagement.

Pricing: not published.

Addepto#

A Warsaw company doing AI consulting, generative AI, machine learning and data engineering, with named verticals including private equity and venture capital.

Genuinely good at: the data layer. A large share of failed AI projects fail underneath the model, in pipelines and governance, and a firm whose origins are data engineering will spot that before it becomes six wasted months.

The honest problem: if your problem is genuinely not a data problem, you may be buying depth you do not need.

Pricing: not published.

Azumo#

Nearshore engineering, primarily South America and largely Argentina, time-zone aligned to US hours. It has shipped production AI since 2016, which predates the current wave of tooling.

Genuinely good at: capacity with real overlap in the working day. It is also the most honest of the six on price, stating outright that cost depends on scope, seniority and engagement model, from one embedded engineer to a team. That is a more useful sentence than silence.

The honest problem: it is a build shop, by its own description. If you are unsure what to build, this is the wrong end of the problem.

Pricing: not published, with an explanation of what moves it.

Toptal#

A marketplace rather than a firm. It publishes its screening funnel in detail: 26.4% pass the language evaluation, 7.4% the skill review, 3.6% the live screen, 3.2% the test project, for a stated top 3% of applicants. Engagements are hourly, part time or full time, with a trial of up to two weeks that you pay for only if satisfied.

Genuinely good at: speed and reversibility. A two-week trial you can walk away from is the lowest-risk way on this list to find out whether someone is any good, and it is the only entry with a published number attached to its own filtering.

The honest problem: you are hiring a person, not a team, and you carry the management. It works when the scope is clear and you can judge technical work yourself. Our own piece on choosing between a freelancer and a firm covers where that breaks.

Pricing: rates not published. The page cites Glassdoor salary ranges rather than its own fees.

AISynq#

Disclosure: this is us, and this is our website. Treat the entry accordingly and check it against the others.

We do three things in a fixed order. Identify, which means sitting with the people doing the work and finding where the hours go, then returning with the short list worth building and the longer list that is not. Build, into the systems already running. Prove, which means agreeing the number before any code is written, taking the baseline, and reading it again afterwards.

Genuinely good at: the part before the build. Most of this market starts at the build, because the build is what invoices cleanly.

Published pricing: the 7-Day AI Workflow Sprint is $2,500 fixed for one workflow, and the pricing page carries the rest. This is the only entry on the list where you can see a number before speaking to anyone.

Measurement in the contract: if the number does not move, the closing report says so.

The honest problems, and there are three. We are small, so if you need fifteen engineers next month we are the wrong call and BCG X or Azumo is the right one. We have no published client reviews yet, which means you are taking the work on the strength of the record rather than on other people's ratings. And our training figures come from two companies, which is a sample of two and is described that way wherever it appears.

The record: work delivered for DHL, AT&T, DirecTV, Accenture, the Singapore Government and Simons Group. The DHL programme contributed roughly $100M in annual operational value at DHL scale. A figure that size is never one person's work, and the case study says which part was.

Which one, for your situation#

You need engineers and you know what to build. Azumo for time-zone overlap, Toptal if one specialist will do. Neither will tell you the idea is wrong, so be sure it is not.

Your problem is underneath the model, in the data. Addepto.

The programme is large and needs board sponsorship. BCG X. The size that makes it wrong for a seed-stage company is the thing you are buying.

You have nine ideas and budget for two. This is the judgement problem. HatchWorks AI names it as GenROI and we name it as Identify, and you should talk to both of us.

You have been burned already and want the number checked this time. Ask every firm on this list what happens if the metric does not move. The answer, rather than the pitch, is the thing worth listening to.

Questions people ask#

Is a bigger firm safer? It is more predictable, which is not the same thing. A large firm will produce a professional process and a senior name on the engagement letter. It will not necessarily put that name on your work. Ask who is doing the building and how often you will see them.

Should we just hire instead? Sometimes, and the arithmetic is not obvious. We wrote out what an in-house AI team really costs because the comparison is usually made against a salary rather than against a loaded cost.

What should the first engagement be? Small, fixed and reversible. Anyone asking a startup for a twelve-month commitment before they have seen your systems is managing their own risk rather than yours.

How do we tell judgement from a good deck? Ask what they would tell you not to build. A firm that sells judgement has a ready answer and will give you an example from another engagement. A firm that sells capacity will change the subject to what it can build.

Thirty minutes is usually enough to find out whether a workflow in your company is worth fixing, and the answer is sometimes no. Book the call and bring the job your team complains about most.