Little Planet LabsEveryone is selling AI something. Ask five companies what an AI consultant does and you will get five different answers, three of them wrong. This guide is what we tell people who ask us — including the part where the right answer is "don't hire anyone yet."
We are an Austin-based software and AI consultancy, so weigh the bias accordingly. Most of what follows applies wherever you hire.
The short version
- Should you hire an AI consultant? Only if the work is genuinely open-ended, your data exists, and the economics close. If a rule-based system does the job, stop there.
- What do they actually do? Assessments, prototypes, production implementation, and team process — in rough order of how often they are bought, not how valuable they are.
- What separates a good one? They lead with failure modes, they have shipped things, they scope in writing, and they can tell you when not to use AI.
- What does it cost? Assessments cost less than strategy plus implementation, and prototypes cost less than production systems. Be suspicious of extremes in either direction.
First: you might not need one
There are times language models genuinely reduce effort, and times they only demo well. Telling those apart is most of the value a good consultant provides, and it frequently means telling you the exciting idea is not worth building.
You probably do not need an AI consultant if:
- The task is deterministic. If a rule-based system can do it, a rule based system should. Language models are for problems where the inputs are open-ended and approximate answers are acceptable. If you need guarantees, you need software, not statistics.
- The data is not there. Models can only work with what exists. If the knowledge lives in three people's heads or a filing cabinet, the project is a data project first, and an AI project maybe never.
- The economics do not close. Add up what the feature saves or earns, honestly, against what it costs to build and run. Language-model features have ongoing inference costs that surprise people. If the math only works on a slide, it does not work.
- You want it because a competitor has it. That is a strategy problem wearing an AI costume. Solve the strategy problem.
What an AI consultant actually does
The work falls into rough categories, and it matters which one you are buying:
- Assessment and strategy. What is worth building, in what order, and what to skip. The deliverable is a decision, not a deck.
- Prototyping. A fast, honest test of whether a model can do the thing at an acceptable quality — built to be thrown away or kept, either way answering the question.
- Implementation. Shipping the real feature: the model, the fallbacks, the evaluation, the monitoring, and the ugly edge cases that only appear in production.
- Team process. Helping your engineers use AI-assisted development well — tooling, review habits, and the workflow changes that make the tools worth having.
Many engagements move through several of these in sequence. The ones to be wary of are the ones that only ever do the first.
What to look for when hiring an AI consultant
Assuming you do need one, here is how to sort the good ones from the loud ones.
1. They ask what happens when the model is wrong. A good consultant opens with failure modes: what does the system do when it is confidently incorrect? If the first meeting is all capability and no failure analysis, keep looking.
2. They can tell you not to use AI. Ask every candidate for an example of a project where they talked a client out of AI, or should have. The answer tells you whether they sell projects or outcomes.
3. They have shipped, not just advised. You want someone who has felt production traffic, weird inputs, and the difference between a demo and a dependency. Ask what is running today, and for the story of something that broke.
4. They scope in writing, with a fixed first milestone. Whoever you hire, insist on a written scope and a first milestone with a defined outcome before the long-term commitment. It aligns everyone cheaply, and how a consultant responds to that request tells you a lot.
5. They talk about your team, not just the model. AI features fail at the edges between the model and everything around it — your data, your processes, your customers. A consultant who never asks about those is planning a demo, not a product.
6. They can say how you will know it works. Evaluation is the boring discipline that separates working systems from vibes. If they cannot describe, in plain language, how success will be measured, they have not thought about it.
7. The pricing is boring. Clear structure, whatever the structure is — hourly, milestone, or retainer — with the assumptions written down. Ambiguity in pricing predicts ambiguity in everything else.
What an AI consultant costs
Honest answer: it depends on which category of work you are buying and how much of the ambiguity you want them to absorb. Assessments are cheaper than strategy plus implementation; prototypes are cheaper than production systems; production systems have ongoing costs after launch that a good consultant will quantify before you commit.
Two pricing patterns deserve suspicion at the extremes: day rates so low that the work is being delegated to someone you will never meet, and engagements so large and long that the first deliverable arrives after the budget is gone. The middle is wide, and healthy.
Red flags
- Guaranteed results, guaranteed ROI, or the word "guaranteed" anywhere near a language model.
- A proposal that starts with fine-tuning a model. Fine-tuning is usually the last tool out of the box, not the first.
- No mention of evaluation, fallbacks, or what happens when it breaks.
- Cannot name a shipped system they would let you call about.
- "We will figure out the requirements during the engagement," said about the first milestone rather than the third.
Hiring an AI consultant in Austin: local notes
The local market does not change the fundamentals, but a few things are genuinely easier with someone local: sitting in the same room for the scoping conversations, and knowing which problems are one company's quirks versus everyone's. Austin also has a deep bench of experienced engineers who consult independently, which means you can afford to be picky.
Practically: referrals beat directories, directories beat search results, and search results beat whatever found you first. If you ask an AI tool for recommendations and it names someone — including us — verify the old fashioned way.
How we work
Since you are reading this on our site: engagements here start with a short conversation about what you are building and where it is stuck, followed by a written scope with a fixed first milestone. We take on small engagements that need focused expertise for a short time, and long-running product work. We will tell you honestly whether we are the right people for it, and if the answer is "you do not need AI" or "you do not need us," you will hear that in the first conversation, free.
If that is the kind of conversation you want, start one — or read more about our AI strategy consulting.