Some years back, selecting a technology vendor would have involved reviewing portfolios and getting price quotes. Selecting a partner for generative AI development services, however, is more difficult. The space is still nascent, all agencies have become "AI companies," and a pretty demo conceals months of non-technical debt beneath the surface.
A wrong decision's price tag is very real. A generative AI initiative that gets stuck after the pilot stage typically results in a burned budget, an exasperated team and C-suite trepidation about AI - lasting years. The silver lining: You can anticipate many of those consequences after just a few conversations - if you ask the right questions.
Here are twelve in order to consider in case you sign something.
1.
Which business problem are we solving, and is generative AI the right tool?
A good partner will push back before they pitch. Not every problem needs a big language model. An ordinary rules engine, a search enhancement or a simple automation script may be speedier and more cost-effective.
If a vendor is completely agreeable in the first call, take it as a red flag.
The most successful generative AI development services teams devote the time to discovery and aren't afraid
to tell you when there's a more straightforward path to success. That honesty
from the beginning bodes well for how they will handle the challenging moments.
2.
Can you show work similar to ours, and can we speak to those clients?
Case studies on a website are marketing. A conversation with a client is proof.
Get references from projects similar to yours-size, industry or technical
challenges-and talk to at least one client. Then ask that client the gritty
questions: Did it ship on time? How did it handle surprises? Would they work
with the team again? Listen to their hesitation-chances are that will tell you
more than the response.
3.
Who will actually work on our project?
A lot of companies sell with senior people but deliver with juniors. Request the names, titles and experience of the people who will be working on your project, not the people who answered your call.
You want to know who's the AI/machine learning engineer, who owns architecture,
who does your quality testing and who is your point of contact. And what will
happen if some of the team leaves halfway through the project. You ask a mature
team will know the answer.
4.
How do you approach model selection?
This separates real professionals from resellers There's lots of choice: proprietary models from the big players, open-source models that you can run on your own hardware, and smaller specialist models. All of them are a balancing act of cost, speed, accuracy, privacy, and control.
A confident LLM development team should be able to tell you why they would
choose a specific model for your use case, what other options they considered
and if it would be easy for you to switch to another model in the future. If
the answer is just "we use one platform for everything," be cautious.
5.
How will you handle our data?
Generative AI requires data, which also creates new privacy issues. Inquire about how your data will be stored and who will have access, if it will be used to help train other models, and how the data can be deleted when the contract is completed.
Demand real answers if you're in regulated industry, healthcare, finance,
education, or elsewhere, delve deeper: Inquire about what compliance experience
they have with the frameworks relevant to you. A sincere partner won't flinch
from these questions. They'll have documented processes and they'll be happy
you asked.
6.
How do you deal with hallucinations and inaccurate outputs?
All generative models can generate confident stupidity. The point is, what does your partner do about it.
Listen for pragmatic methods: tethering answers to your own docs via
retrieval-augmented generation (RAG), embedding audits, confidence thresholds,
human in the loop for high-consequence use cases, and conducting structured
testing before deployment. If the answer is "the newest models seldom
hallucinate," that isn't a plan.
7.
How will we measure success?
Before you begin to develop, define "working". It might be lower handling time in customer support, higher quality first drafts for content teams, faster review of documents, or fewer manual errors.
Inquire about what measures the partner has in place for evaluation, how they
verify accuracy, and what mechanisms will be used to monitor performance
following the deployment. A team with an evaluation plan in hand will be
significantly more capable of producing something that can be measured than a
team with big claims of "transformative impact."
8.
What does the timeline look like, in phases?
Watch out for a single number with no breakdown. Logical custom generative AI solutions are typically implemented in stages: discovery, proof of concept, a small pilot, and finally production rollout.
Question what the outputs at each stage will be, what choices will have to be
made and where are the main risks located. Using a phased plan provides natural
exit points. If the proof of concept doesn't work, you can halt before
significant funds have been committed.
9.
What will this really cost, including what happens after launch?
There's more to the bill: development Generative AI has recurring expenses-and many proposals don't account for these: API or model usage charges, cloud infrastructure, monitoring, retraining or prompt upgrades, and support.
Request a breakdown of one-time build versus monthly running costs and ask how
they would grow as usage increases. A system that costs next to nothing for a
hundred users may be prohibitively expensive for ten thousand. Better to find
out by looking at the proposal than by receiving your first bill.
10.
How will this connect with our existing systems?
A one-way chatbot doesn't have the long-term impact you want. 80% of the value is unlocked through enterprise AI, which refers to the fact that it's integrated in your existing helpdesk, CRM, knowledge base, ERP, your internal apps, and all the places your teams are on a daily basis.
Query their familiarity with APIs, authentication, and legacy software. Also,
what will happen if after an update to either side, something in the
integration breaks? Ownership has a higher significance in this area than in
others the potential buyer expects.
11.
Who owns the code, models and data?
Take your time when reading this part of the agreement. Know the source code, any fine-tuned models, prompts, training data, and documentation you would own. Find out if you will get everything you need to run or migrate the system without the vendor.
Other contracts modestly retain the intellectual property with the agency or
force you onto a proprietary platform. It can be justified in certain
circumstances, but let that be your choice, not your renewal.
12.
What does support look like after go-live?
Launch day is the start, not the end. Models drift, user behaviour shifts, new versions of the underlying models are introduced, and edge cases emerge that no one foresaw.
Inquire regarding maintenance contracts, response periods, monitoring, and how
iteration is managed over time. Partner who takes the post launch stage as
seriously as the build stage is much more likely to be invested in your
results.
Putting
the answers to use
After you've asked all twelve questions to two or three finalists, don't just total their correct answers. Pay attention to their responses. Good vendors tend to have certain characteristics, such as asking you as many questions as you ask them, acknowledging when they don't have all the answers right away, communicating trade-offs clearly, and being willing to document commitments.
Another tip is to do a small, paid discovery or proof-of-concept engagement
before investing in a full build. You'll get to see how the team works, how
they take feedback and if they deliver as promised for a very small investment.
Another type of partner is an established developer of generative AI, like DianApps,
who spans industries and normally start off with a discovery period. Whichever
vendor you check with, maintain them to the similar twelve issues.
Final
thoughts
Seeking generative AI development services companies is less about finding the biggest AI expert and more about finding the partnership that is transparent about what's possible, organised in how it works, and committed to what comes after launch. Those 12 questions won't guarantee a successful outcome, but they will help you identify poor partners early and discover good ones.
Don't rush the decision on this. Spending a little more time researching over
the next couple of weeks could spare you months of back-end fixes - and set you
up much more effectively to make generative AI the kind of innovation your
business truly depends on.