Everyone says "add AI", nobody names the project
Your board, your investors, and your inbox all agree you should be doing something with AI. None of them can tell you which workflow to start with, what it costs, or how you would know it worked.
AI Consulting
AI consulting that finds the one use case worth building
For startups and small teams who keep hearing "add AI" with no idea where it actually pays off. We review your data, systems, and workflows, rank the use cases by payback and effort, and hand you a costed AI roadmap for the first build. When the right move is an off-the-shelf tool or waiting a quarter, we say that instead.
The hard part is not the model. It is knowing where AI pays off
Most teams we speak to are not short on AI ideas. They are short on a way to tell which idea is worth a quarter of engineering time. If any of this sounds like your week, that is the gap an AI consulting engagement is meant to close.
A vendor quote you cannot sanity-check
Someone has quoted you a large number for an AI build. You have no way to judge whether the scope is right, whether your data supports it, or whether a tool you can buy off the shelf does the same job for a fraction of it.
Data spread across systems you do not fully trust
It lives in a CRM, a warehouse, and a dozen spreadsheets. Before anyone promises you a model, somebody has to say honestly whether that data is clean enough and complete enough to build on.
One build's worth of budget this quarter
You can fund a single project. Spending it on the wrong use case costs you the money and the year, because the next AI request is a much harder sell internally once the first one misses.
What AI consulting actually means
AI consulting is the work of deciding what to build with AI before you spend on building it. A good consultant looks at your business, your data, and your day-to-day workflows, then tells you which problems machine learning can solve well, which ones it cannot, and what the first project should be.
It is not a sales pitch for a model, and it is not the same as buying a few AI seats and hoping something sticks. Consulting is the step that turns "we should use AI" into "here is the one use case that pays for itself, here is what it costs, and here is how we will know it worked."
A real engagement covers
- An AI readiness assessment across your data, systems, and team
- A ranked list of use cases, scored by payback and effort
- A costed AI roadmap for the first build, with timeline ranges
- A feasibility check on the shortlisted use case before you commit
- The metric that proves it worked, and the target to hit
- The risks, from thin data to model drift and AI governance, and how to handle each
- A build, buy, or wait call on every option we looked at
What it is not
- A generic AI strategy deck you could have downloaded
- A push to build the most impressive thing in the room
- A recommendation to buy tools you will not use
- A promise that AI fixes a process that is already broken
- A one-size plan copied from another company
Where AI consulting pays off, with examples
The value is spotting the use case that fits your data and your goal. Here are patterns we see often, and the question each one answers before a dollar of build budget is spent.
Support that cannot keep up
A SaaS team fields the same forty questions every day, and support is the ceiling on growth. Consulting reads a few months of tickets, finds which types repeat, and estimates how many a scoped assistant could resolve without a human. If deflection looks like thirty percent, a chatbot earns its place. If every ticket is an edge case, it does not, and you hear that.
Planning done by gut feel
A distributor sets stock levels by instinct and pays for being wrong in both directions. Consulting checks whether you have enough clean sales history for a demand model to beat the current guess. Sometimes you do, and forecasting pays back quickly. Sometimes the data is too thin, and a simple rule change wins first.
A document-heavy back office
An accounting or insurance team keys data out of PDFs by hand. Consulting measures the volume, how varied the layouts are, and what an error costs, then sizes whether model-based extraction beats an off-the-shelf OCR tool for your case. The answer is a number, not an opinion.
Sales chasing the wrong leads
Reps give every lead equal time when a handful are worth ten of the rest. Consulting looks at your closed-won and closed-lost history to see whether a lead-scoring model can rank them, and whether your CRM data is clean enough to trust the score once it exists.
Knowledge stuck in people's heads
New hires take months to get productive because the answers live in old docs and Slack threads. Consulting scopes whether a retrieval assistant over your internal knowledge would actually save time, what it takes to keep answers current, and where a wrong answer would be costly.
Personalization you cannot do by hand
An ecommerce catalog is too large to merchandise by hand. Consulting weighs whether a recommendation model moves revenue enough to justify the build, or whether better search and a few smart rules get you most of the result for a fraction of the cost.
Signs it is time to bring in an AI consultant
Consulting earns its fee when it saves you from a wrong build. It is wasted when the answer is obvious or the timing is off. Here is how to tell which side you are on.
Bring one in when
- You keep hearing "we should use AI" but nobody can name the first project
- You have a real, repetitive cost and suspect software could cut it
- You are about to fund an AI build and want a second opinion on scope
- A vendor quoted a large number and you cannot tell if it is fair
- Data is piling up and you have no plan for using it
You may not need one yet when
- The process you want to automate is not yet defined by a human
- You have almost no data on the problem and cannot collect it soon
- A cheap off-the-shelf tool already does the job well
- The real blocker is a policy or people problem, not a technical one
A short engagement that ends with a clear decision
No forty-slide deck. No vague roadmap. You get four things, in order, and then you decide what happens next.
01
We start with the outcome
Cut a cost, win back hours, close more deals. We name the number you want to move before we talk about any model.
02
We map the use cases
We look at your data and systems and rank the ideas by payback and effort, so you can see which one earns its place first.
03
We size cost and payback
You get a realistic range for the build, the data you need, and what a good result looks like, with no rounding up.
04
You get a plan you can act on
A short written brief and a working session. Build it with us, build it in-house, or park it. Your call, made with real numbers.
What you walk away with
A short written plan you can take to a budget conversation, not a slide deck that sits in a drawer.
- The one use case to build first, and why it beat the others
- The smallest build that proves it, scoped as a PoC or MVP
- A cost and timeline range you can take into a budget conversation
- A data readiness check: what you have, what is missing, how to close the gap
- The success metric and the target that counts as a win
- The risks worth watching, with a plan for each
- A build, buy, or wait call on every option we considered
Questions worth asking any consultant
Use these whether you work with us or not. They separate a real engagement from a slideshow.
- What use cases did you rule out, and why?
- Is our data good enough to start, honestly?
- What does the first build cost, and what does it save?
- How will we know it worked, in one number?
- What breaks if the model is wrong, and who catches it?
- What happens after launch, when the data shifts?
We will talk you out of the wrong build
Sometimes the honest answer is a tool you can buy, a process you can fix, or a project worth waiting on. When that is true, we say so. It costs us a build and it earns your trust, which is the better trade. When a custom build is the right call, the same team that scoped it can design and ship it.
AI Products We've Shipped
Real machine learning, generative, and agentic AI work running in production. Each card opens the full case study.
AI consulting questions
What founders ask before booking a consulting call with us.
AI consulting is the work of deciding what to build with AI before you spend on building it. A consultant looks at your business, your data, and your workflows, then tells you which problems machine learning can solve well, which it cannot, and what the first project should be. It turns 'we should use AI' into a specific use case with a cost, a payback, and a way to measure the result.
Consulting decides what to build and whether it is worth it. Development builds it. Consulting is the short, low-cost step that comes first, so the money you spend on development goes toward something with a clear payback. We do both, and we keep them separate on purpose so the advice stays honest.
It starts with a conversation about the outcome you want, not the technology. We look at your data, your systems, and your team, then map the use cases worth doing. You leave with a short written plan: which use case to build first, a rough cost and timeline, the data you need, and the risks to watch. If it makes sense to build, we can build it. If it does not, we will tell you.
Yes, and we start there. Part of the engagement is looking at your current stack and where your data lives, so the plan fits what you run today instead of assuming a rebuild. If a gap in the data is the real blocker, we show you the cheapest way to close it before you commit to anything larger.
A focused strategy engagement is a fixed, small fee, and the first call is often free. We keep it lean on purpose. The point is to spend a little to avoid spending a lot on the wrong thing. Share your goal and we will quote it before you commit.
No. Most of the teams we work with are new to AI. Our job is to translate, so you can make a good call without a data science degree. We explain the trade-offs in plain language and point you at the smallest build that proves the idea.
The method is the same across industries: find the repetitive, costly problem, then check whether the data supports a model. We have scoped and shipped AI across payments, consumer apps, and data-heavy products. When your problem is well defined and you have data on it, the domain matters less than most people expect.
We would lose your trust fast if we did. Sometimes the right answer is an off-the-shelf tool, a process change, or waiting six months. We say so. When a custom build is the right move, we are glad to be the team that ships it.
Usually one to three weeks, depending on how much of your data and systems we need to review. You get the written plan at the end, plus a working session to walk through it.
Start with a conversation
Book a strategy call. Bring your goal and your questions. In 30 minutes you will know whether AI is worth your time, and what the first step looks like.