An AI consultant helps you decide what to do. An AI agency builds what you have already decided. An in-house team owns AI for the long run. A fixed-price pilot gives you real numbers on one task before you commit to any of them. Most companies starting out need the decision first, and the right choice depends on how sure you are about the task, how much AI work you will have, and who will run it afterwards.
I run Skcript Applied Labs, which sells the fixed-price pilot. So read this with that in mind. I'll be specific about when one of the other options is the better call, including when you should not hire us.
The four options in one table
| AI consultant | AI agency | In-house team | Fixed-price pilot | |
|---|---|---|---|---|
| Main job | Help you decide what to do | Build what you specify | Own AI for the long run | Get one task working on real data, then decide |
| Best when | You don't yet know which task or approach | The spec is clear and you need hands | AI is core to your business or work is steady for years | You have a task in mind and want numbers before a budget |
| What you get | Recommendations, roadmap, sometimes a prototype | Working software to your spec | People who know your systems and stay | Before and after numbers, test set, working first piece, fixed rollout price |
| Typical pricing | Day rate, project fee or retainer | Project fee or time and materials | Salaries, hiring cost, tools | One fixed price, agreed upfront |
| Time to first result | Depends on scope | Depends on scope | Hiring plus ramp-up | Days |
| Main risk | Advice nobody builds | Building the wrong thing well | Slow start, hard to hire for | Scope too narrow for a big programme |
| Who owns the result | Usually you, check the contract | Check the contract | You | Check the contract (with us, you do) |
The labels blur in real life. Plenty of consultancies build, and plenty of agencies advise. The useful question is not what a firm calls itself but which of these jobs you are paying it to do.
AI consultant
A consultant is worth hiring when the question is still "what should we do?" Which tasks are worth automating, which approach fits your data rules, whether AI is the answer at all.
Where it goes well: you get an outside view from someone who has seen many companies try this, and you avoid spending on the wrong task.
Where it goes wrong: the output is advice, and advice nobody builds is a cost with no return. The five gaps where AI pilots stall often start here: a plan with no measured baseline, no test set and no owner.
Pick a consultant when you have several candidate tasks and no way to rank them, or leadership needs a view across the business before committing. Insist that the work ends with measured numbers, not only a document. My guide to choosing an AI consultant has the checklist and red flags.
AI agency
An agency is worth hiring when you know what you want built and need people to build it.
Where it goes well: clear spec, clear deliverable, and you don't have to hire.
Where it goes wrong: an agency will usually build what you asked for. If the spec was wrong, you get the wrong thing, built well. AI work makes this worse, because you often don't know how accurate a model will be on your data until you test it. A spec written before that test is a guess.
Pick an agency when the task is proven, you have measured what it costs today, you know roughly how accurate the system needs to be, and you need capacity to build it. Ask whether they test more than one model on your cases before they commit to an approach.
In-house team
Building your own team is the right long-term answer for some companies.
Where it goes well: your people learn your systems, your data and your rules, and that knowledge stays. Every project after the first gets cheaper and faster.
Where it goes wrong: hiring takes time, and it is hard to know what to hire for before you have done the work once. One hire on their own can also end up isolated, without the systems access or the authority to take anything live.
Pick in-house when AI is part of your product, or you expect a steady stream of AI work for years. Even then, many teams get one task working with outside help first, so they know what skills they actually need and have something live for the new hire to own.
Fixed-price pilot
This is what we sell, so here is the plain version. One task, ten working days, one fixed price. We measure the task as it runs today, build a test set from your real cases, compare several models, build the first working piece inside your tools and run it on real work. At the end you get the numbers and a decision: go live with a fixed price for the rollout, change the approach, or stop.
Where it goes well: you spend a fixed amount to find out whether one task is worth doing, with numbers measured the same way before and after. Nothing built gets thrown away if you go ahead. The AI pilot to production playbook shows how the pilot carries into a rollout.
Where it goes wrong: a pilot covers one task. If you need a view across twenty processes, one pilot is the wrong tool.
Our 10-day AI pilot is a fixed USD 7,500, with fixed local prices for other markets on our pricing page, such as AED 27,500 in the UAE and EUR 7,000 in Europe. The fee counts toward the full rollout if you start within 30 days.
Build vs buy AI
There is a fifth option the table leaves out: don't build anything. Buy a product.
Buy when an off-the-shelf tool already does your task well. "Well" has to mean on your own cases, not on its demo. Take 100 to 200 of your real examples with the right answers attached and run the product through them. If it is accurate enough at a cost per task you accept, buy it. That test set is useful either way; here is how we test AI models on your work.
Build when the task depends on your systems, rules or data in ways a product can't handle: reading from your ERP, following your approval rules, writing back safely, handling the mix of languages and formats your work actually arrives in.
Mix them when, as is common, a product covers most of the task and the gaps are in the connections. Buy the part that works and build only the glue.
When comparing, count the full cost per task, not the licence or the token price. Time people spend checking outputs and fixing mistakes is usually the bigger number.
When not to hire us
I'd rather lose a project than sell the wrong thing. Here is when you should go elsewhere, or nowhere.
- A rule will do. If the right answer can be written down, write the rule. It is cheaper and easier to audit.
- The volume is small. A task that happens a few times a month rarely pays for an AI system.
- The process is the problem. AI won't fix an approval stuck with someone who is travelling.
- A mistake costs too much. Some decisions need a person to own them. AI can gather facts and draft.
- You want a multi-year programme. We start with one task. Large transformation programmes suit a different kind of firm.
- You can't name an owner. The pilot needs one person who knows the task, a set of real examples, system access and about two hours a week. Without that, no outside help will get it live.
- A product already does it. If an off-the-shelf tool passes your test set, buy it.
More on the first few in when not to use AI.
How to decide
Ask three questions.
- Do we know which task? If not, you need a consultant or a short scoping step. If yes, keep going.
- Do we know it is worth doing, with numbers? If not, run a pilot or test a product on your real cases. If yes, an agency or your own team can build it.
- Will we have AI work for years? If yes, plan to hire, ideally after the first task is live so the new team has something real to own.
Most companies I talk to are at question two. They have a task in mind and no numbers yet. That is exactly the gap a fixed-price pilot is built for, and if you are somewhere else, one of the other options will serve you better.
You can see how we work on our AI consulting page. If you have a task in mind, start a chat with us on the site, or book a call through the Applied Labs page. Bring a few real examples, and I'll tell you honestly which of these four fits.
