Start by defining what you are buying
“AI development” covers very different work, and the right partner depends on which kind you need. Be clear on this before you speak to anyone:
- Applied AI products: assistants, agents, chatbots and document automation built on existing large language models and connected to your data.
- AI features in a software product: search, summarisation or recommendations inside an app you already run.
- Machine learning and data science: custom predictive models, forecasting or computer vision trained on your data.
- AI-accelerated software delivery: using AI tools to build conventional software faster.
A firm that is excellent at one may be weak at another. Write down the business outcome, who will use the system, what data exists, and which systems it must connect to. That one page makes every later conversation sharper.
What to evaluate in an AI development partner
| Area | What good looks like | What to be careful of |
|---|---|---|
| Evidence of delivery | Working demos you can try, and projects described in terms of outcomes and method | Logo walls and case studies with no detail about what was built |
| Team | Named senior engineers who join the calls and write or review the code | A senior sales team and a junior delivery team you meet later |
| Evaluation method | A test set built from your real data before development, with accuracy measured | Promises that the model is “highly accurate” without measurement |
| Engineering depth | Integration, security, monitoring and deployment treated as core work | Prototypes that only work in a notebook |
| Data handling | Processing in your cloud, written data agreements, clear retention rules | Vague answers about where data goes or which models see it |
| Ownership | You own code, prompts, models and data, with no platform lock-in | Proprietary platforms that hold your logic hostage |
| Communication | Written scope, weekly demos, a named contact and an overlap window for your time zone | Long silences followed by large status reports |
Twelve questions to ask every AI development company
- Who exactly will work on my project? Ask to meet them, and ask about their experience in production AI, not only research.
- Can I try something you have built? A working demo tells you more than a presentation.
- How will you measure quality before launch? Look for an evaluation set drawn from your real cases.
- What happens when the AI is wrong or unsure? The answer should involve confidence thresholds, human review and clear handoff.
- Where will my data be processed and stored? Ask for the region, the providers and whether any data is used to train models.
- How do you handle personal data under India's DPDP Act and our own regulators? They should discuss minimisation, consent, retention and access controls without hesitation.
- Which models and tools will you use, and why? A good partner chooses per task and can switch models as the market changes.
- How is the work structured and reviewed? Look for phases with a measurable goal and an exit gate, and for a weekly demo.
- What will I own at the end? Code, prompts, fine-tuned models, evaluation sets and documentation should all be yours.
- How do you monitor the system after launch? Quality drift, failed calls and incidents need an owner.
- What is your plan for handover? You should be able to run it yourself, or hire someone else to.
- What would make you advise us not to build this? An honest partner will tell you when an automation or an off-the-shelf tool is the better answer.
Green flags and red flags
- Green: they ask about your data and workflow before proposing a solution. Good partners start with your problem, not their product.
- Green: they define success as a number you choose, such as time saved, resolution rate or error rate.
- Green: they are comfortable proposing a small proof of value first, with a decision point afterwards.
- Red: guaranteed accuracy before they have seen your data.
- Red: no mention of evaluation, security or monitoring. These are the work that makes AI reliable.
- Red: a proprietary platform required to run what they build.
- Red: unwillingness to let you speak to the engineers.
Working with an India-based team from another country
Many global companies work with Indian AI teams successfully. A few practical points make it smoother. Agree a daily overlap window for your time zone and a weekly demo at a fixed time. Insist on written scope and written decisions, so nothing depends on a conversation that only one side remembers. Use your own repositories and cloud accounts from day one. Clarify security expectations and your contract terms up front, including NDAs and data processing agreements. Our page on AI development from India for global teams explains how we set this up.
Run a low-risk pilot before you commit
The most reliable way to evaluate a partner is to work with them on a small, real problem. A good pilot has these features:
- One narrow use case with a clear owner and real data.
- A baseline that records how the work is done and measured today.
- A fixed length of two to four weeks and a defined deliverable.
- A written success measure agreed before work starts.
- A decision point where you choose to continue, change direction or stop, with everything produced already yours.
You will learn how the team communicates, how they handle surprises and whether their evaluation claims hold up on your data. That is worth more than any pitch.
How to compare shortlisted vendors fairly
Give each vendor the same brief, the same sample data and the same questions, then compare answers rather than presentations. Weigh the quality of the questions they asked you as heavily as the answers they gave. If you are located in Gujarat, an in-person workshop is a useful test as well: see how a team behaves in your environment. We work with companies across India and abroad, and you can see our approach on our AI development in Ahmedabad page, try the live demos, or talk to us with the same list of questions.