Create high-performance mobile apps for iOS and Android.
Develop fast, responsive, and optimized websites.
Craft intuitive and engaging user experiences.
Leverage AI for automation, insights, and smarter solutions.
Build AI agents that automate decisions, workflows, and operations.
Create content, code, and insights with large language model solutions.
Build secure online stores with easy payments.
AI-powered SEO, social, paid, and content that drives growth.
Extend your team with vetted engineers, flexible and cost-effective.
Everyone is telling you to add AI. Almost no one is telling you where it actually pays off in your business. That is the gap we close. We help you pick the one use case worth building, put a real cost and payback on it, and hand you a plan you can act on this quarter.
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."
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.
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.
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.
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.
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.
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.
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.
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.
No forty-slide deck. No vague roadmap. You get four things, in order, and then you decide what happens next.
Cut a cost, win back hours, close more deals. We name the number you want to move before we talk about any model.
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.
You get a realistic range for the build, the data you need, and what a good result looks like, with no rounding up.
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.
A short written plan you can take to a budget conversation, not a slide deck that sits in a drawer.
Use these whether you work with us or not. They separate a real engagement from a slideshow.
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.
Real machine learning, generative, and agentic AI work running in production. Each card opens the full case study.
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.
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.