Choosing an AI Development Company in Delhi: A Technical Checklist

Abhi Roy — Mon, 12 Oct 2026 19:18:24 +0000

When someone searches for the “best AI company in Delhi,” a ranking or slogan cannot tell them whether a team can deliver the system they need. A better comparison starts with technical evidence: how the vendor scopes the problem, handles data, tests outputs, integrates the system, and supports it after launch.

Use these questions before choosing an AI development partner.

1. Can they turn the idea into a bounded first release?

Ask what the first version will and will not do. “Add AI to customer support” is broad. “Answer these 20 product questions from the approved manual and hand off anything else” is testable.

A capable team should help identify the user, source data, success criteria, and failure path. If the proposal starts with a model name before understanding the workflow, slow down and ask for a clearer problem statement.

2. How will the system use your data?

Ask which documents, databases, APIs, or tools the system needs. Clarify who owns the source data, where it is processed, what is stored in logs, and how access permissions are enforced.

For a retrieval-based assistant, ask how documents are updated and removed from the index. For an agent that can take actions, ask how it validates inputs, limits permissions, and handles an action that fails halfway through.

3. How will you know the AI is working?

Ask for an evaluation plan using realistic examples from your workflow. It should include successful cases, ambiguous requests, and questions the available data cannot answer. For a RAG system, measure retrieval quality separately from answer quality and check that citations point to the right source.

Agree on acceptance criteria before building. “The demo looks good” is not a measurable launch condition. You might define what counts as a correct answer, when the system must abstain, and which cases need a human review.

4. What needs to integrate with the product?

List the systems the AI must read from or write to: CRM, support software, internal databases, document stores, or a customer-facing app. Ask how the integration will be authenticated, tested, monitored, and versioned.

Start with the smallest useful connection. Read-only access is often enough for an initial knowledge assistant; writing to business systems should have explicit permissions and verification.

5. What happens after launch?

Models, prompts, data, and user needs change. Ask who will monitor quality, review failures, update source material, manage incidents, and maintain the application. Get clarity on documentation, ownership of code, deployment access, and handover.

A production plan should include a fallback when a model or third-party API is unavailable. It should also explain how the team will roll back a change that makes answers worse.

6. Can they show relevant evidence?

Look for case studies that explain the original problem, the scope delivered, the systems involved, and the result. Ask what was measured and over what period. A percentage without a baseline, sample size, or context is hard to evaluate.

If a client cannot be named, the vendor can still explain the architecture and constraints without exposing confidential information. Ask for a technical walkthrough or a small, paid discovery phase with clear deliverables.

7. Compare the full cost, not just the build estimate

Include hosting, model usage, vector search, monitoring, maintenance, support, and future changes. Ask which costs scale with traffic and how usage limits will work. A lower initial estimate may leave important operational work out of scope.

A practical way to choose

Give each shortlisted team the same short brief. Ask them to identify assumptions, risks, a first milestone, how they will test the result, and what they need from you. Compare the quality of those answers—not only the number of features or the promise of a quick launch.

If you are comparing Delhi NCR teams, Innovative AI Solutions is one local AI and software development option to review alongside others. Check its services, project examples, team fit, and proposed evaluation plan against your requirements.

Disclosure: I’m the founder of Innovative AI Solutions. This checklist is meant to help buyers ask better questions; it is not a claim that one vendor is right for every project.

A Practical Pre-Launch Checklist for a RAG Chatbot

Abhi Roy — Mon, 12 Oct 2026 19:16:46 +0000

A RAG chatbot is a pipeline, not just a prompt with a vector database attached. Before it reaches users, test whether each stage works: source preparation, retrieval, answer grounding, access control, and fallback behavior.

Here is a practical checklist for a first production release.

1. Start with a bounded use case

Write down who will use the assistant, which questions it should answer, and which sources it may search. “Answer questions about the current product manuals” is testable. “Know everything about our company” is not.

Choose one team and one or two trusted sources for the first version. This keeps the initial system easier to evaluate and reduces the risk of mixing unrelated or restricted information.

2. Prepare documents for retrieval

Before creating embeddings:

Remove duplicate and obsolete files.
Preserve titles, section headings, dates, and source URLs as metadata.
Split content around meaningful sections rather than arbitrary character counts alone.
Keep an identifiable source reference for each chunk.
Define what happens when two documents conflict.

There is no universally correct chunk size. Test different chunking strategies against questions that real users are likely to ask.

3. Test retrieval separately from generation

Create a small evaluation set of realistic questions. For each question, record which source or passage should be retrieved. Then check whether the retriever returns that evidence before looking at the model's wording.

Include paraphrases, questions that combine two facts, misspellings, and questions for which no source contains the answer. A fluent answer based on the wrong passage is still a failed result.

4. Require evidence and a safe “I don’t know”

Pass retrieved context to the model with clear instructions to use that context and say when it is insufficient. Where possible, show the document or section behind an answer so users can verify it. Add a route to a person for ambiguous or high-impact questions.

Do not assume that prompting alone guarantees correctness. Keep a way to review failure cases and update the retrieval system or source material.

5. Check permissions and freshness

A chatbot must not reveal a document to someone who could not access it directly. Apply access rules during retrieval, not only in the user interface. Decide how updated, deleted, or access-restricted documents are reflected in the index, and test that process.

For an initial release, read-only access is usually easier to control than allowing the assistant to change records or trigger actions.

6. Measure the workflow

Track whether users find the right source, when they rephrase, how often they escalate, and whether answers are useful. Review a sample of conversations with appropriate privacy safeguards. A demo that answers a few prepared prompts does not show how the tool performs across daily questions.

A simple end-to-end test

Ask a question whose answer is in a source document. Confirm that the system retrieves the correct passage, returns an answer supported by it, links back to the source, and refuses or escalates when the needed evidence is missing. Repeat this test after changing the documents, chunking, embedding model, or prompt.

If you want to see a compact implementation path, this Django + FAISS RAG chatbot guide walks through the ingestion and query flows.

Disclosure: I’m the founder of Innovative AI Solutions, a Delhi NCR-based software and AI development company. The checklist above is intended to help teams evaluate the system, not promise that RAG fits every use case.

DEV Community: Abhi Roy