Bengaluru-based · Independent
AI Consulting Services in Bangalore
An independent AI consultant for Bangalore businesses — strategy first, a paid pilot on your own data second, and a build only once the pilot has earned it. You talk to the person doing the work, not an account manager.
- AI strategy — which problems are worth solving, and which are not
- GenAI & LLM adoption — model choice, retrieval, evaluation, cost control
- Automation & document AI — the highest-return work, and the least discussed
- Chatbots, agents & voice agents — grounded, logged, with human handover
- ML, forecasting & data foundations — including when not to use a model at all
The short answer
An independent AI consultant in India bills roughly ₹25,000–₹75,000 a day, a scoped proof of concept runs ₹1,50,000–₹4,00,000, and an ongoing retainer ₹40,000–₹1,50,000 a month — against ₹1,50,000–₹3,00,000 a day for a Big 4 advisory team on comparable work. Figures are indicative market ranges; your price comes from scoping.
The first engagement should be discovery, not a build. Most businesses arrive with a solution already chosen — usually a chatbot — and the useful work is establishing whether that is the problem worth spending on. I would rather sell you a two-week discovery that kills three ideas than a three-month build of the wrong one.
The actual problem
Indian businesses are not failing to try AI. They are failing to finish.
The gap below is the whole job. Roughly two-thirds of Indian firms are already experimenting with generative AI, but only a quarter have integrated it into how work actually gets done, and adoption among SMEs sits at about 15%. Nothing in that drop-off is caused by the models being incapable — the losses happen at use-case selection, at data readiness, and at the unglamorous production work of evaluation, guardrails and monitoring that a pilot never needs and a live system cannot survive without.
Who this is for
The right first move is different at every size
Startups
You need one AI feature that genuinely differentiates the product, shipped before the next raise, without a research project attached. Usually that means an API model, aggressive scoping, and a hard cost ceiling per user — plus an honest answer about which parts of your pitch deck are not buildable yet at your price point.
SMEs
You have people doing repetitive document and enquiry work, and no in-house AI skill to judge the vendors calling you. The highest-return project is almost never the chatbot on the website — it is extraction, routing or reconciliation somewhere in the back office where nobody has thought to look.
Enterprises
You have pilots that never reached production and a governance question nobody has answered. The work here is triage of what already exists, a data and access review, evaluation harnesses so results are measurable, and a defensible answer on what may be sent to which model under which contract.
What is covered
AI consulting services, end to end
AI strategy and use-case selection
A ranked shortlist of what to automate, costed, with the ones you should not do and why. This is where the money is saved.
GenAI and LLM adoption
Model choice on cost, latency and data terms; prompt and retrieval design; evaluation so you can tell whether a change made it better or just different.
Process automation
The unglamorous, highest-return work: document and invoice extraction, form routing, reconciliation, internal copilots over your own systems.
Chatbots and support assistants
Grounded answers with citations, refusal when no source exists, and human handover — on your site, in WhatsApp, or inside your helpdesk.
AI agents and voice agents
Systems that take actions rather than answer questions, and phone agents that handle the calls your team repeats all day.
Machine learning and forecasting
Demand forecasting, churn and lead scoring, anomaly detection — classical models where they beat an LLM, which for tabular data is most of the time.
Data foundations
The prerequisite nobody sells: getting your data out of PDFs, spreadsheets and WhatsApp into something a model can actually use.
How an engagement runs
Six stages, and you can stop after any of them
Each stage produces something you own and could hand to someone else. The exit points are deliberate — a process that only pays off if you complete all six is a process designed for the consultant, not the client.
- Discovery · ~1 week
- Interviews and process observation. What actually consumes time, where the data lives, what has already been tried and failed.
- Use-case selection · ~1 week
- A ranked, costed shortlist — including what to reject. You get this as a document whether or not you continue.
- Proof of concept · 2–4 weeks
- One use case, your real data, real volumes. Built to fail cheaply if it is going to fail.
- Build · 4–8 weeks
- Production version: retrieval, guardrails, evaluation harness, logging, and a cost model per transaction.
- Deploy · 1–2 weeks
- Integration, staff walkthrough, handover documentation, accounts in your name.
- Optimise · Ongoing
- Prompt and retrieval tuning against logged failures, model swaps as prices fall, monthly cost review.
The honest comparison
In-house hire, agency, or independent consultant?
All three are right in different situations. This is the comparison including the cases where I am the wrong answer — if you need five specialists in parallel on a hard deadline, or contractual cover that survives one person being unavailable, hire an agency.
| In-house AI hire | AI agency | Independent consultant (me) | |
|---|---|---|---|
| Cost | Mid-level ML/AI engineer in Bengaluru runs ₹15–25 LPA, plus equity, hardware and recruitment — fixed from month one | Retainers with a sales, account-management and delivery-manager layer priced in | ₹25,000–₹75,000/day, or a retainer you can stop. You pay for the work, not the overhead |
| Speed to start | 2–4 months to hire, longer for senior GenAI skills in a market that is bidding them up | 2–4 weeks through procurement and proposals | Days. Discovery can start this week |
| Flexibility | Hardest to reverse. Wrong hire costs a year and a difficult conversation | Scope changes go through a change request and a new quote | Scale up for a build, down to a half-day a month, or stop entirely |
| Accountability | Clear, but a single junior person carries risk nobody senior is checking | Diffuse. The person who pitched is rarely the person building | Single point. One name on the work, the advice and the outcome |
| Best when | AI is core to your product and you need it permanently in the building | You need several specialists in parallel and want institutional cover | You need judgement, a pilot, or a first build — and want the knowledge left behind |
Indicative pricing
What an AI consulting engagement costs
Every figure below is an indicative Indian market range, not a rate card. Your number comes from scoping, and I will tell you before we start if your project sits outside these bands.
| Engagement | Indicative range | What it covers | Typical fit |
|---|---|---|---|
| Advisory day rate | ₹25,000–₹75,000 per day | Workshops, architecture review, vendor and model selection, team training on your own use cases | You have in-house engineers and need judgement, not hands |
| Discovery sprint | ₹75,000–₹2,00,000 | 1–2 weeks. Process observation, data review, ranked and costed use-case shortlist you keep either way | You are not yet sure what is worth building |
| Proof of concept | ₹1,50,000–₹4,00,000 | 2–4 weeks. One use case on your real data, with a measured accuracy and cost-per-transaction figure | You need evidence before committing a budget |
| Production build | ₹4,00,000–₹20,00,000+ | Retrieval, guardrails, evaluation, integration, logging, handover. Scales with systems touched | The pilot worked and it needs to survive real use |
| Monthly retainer | ₹40,000–₹1,50,000 per month | Ongoing tuning, cost review, model migrations, a set number of days a month | Something is live and needs to keep working |
| Model & cloud running cost | Billed to you directly, at cost | OpenAI, Anthropic, Google or AWS usage — forecast from your real volumes and capped before launch | Every engagement. Never marked up |
Day-rate and retainer bands reflect published Indian boutique and independent consulting rates; Big 4 comparison figures are ₹1,50,000–₹3,00,000 per day for equivalent advisory work. Build ranges follow the same sourcing as the rest of the site — see pricing. Model and infrastructure costs are always billed to your own accounts at cost, never resold at a margin.
Industries
Where this work has actually landed
Bengaluru's mix means the same technique lands very differently by sector. These are the industries I work across, with the use case that tends to pay for itself first in each.
Startups & SaaS
One differentiating feature, with a hard cost ceiling per user
Manufacturing & factories
Purchase-order and invoice extraction; quality-report summarisation
Schools, colleges & trusts
Admissions enquiry handling and fee-query deflection
Hospitals & clinics
Appointment triage and discharge-summary drafting, with clinician review
Retail & showrooms
Catalogue enrichment, demand forecasting, WhatsApp order capture
Logistics & warehousing
Document matching across PODs, e-way bills and invoices
Real estate & apartments
Lead qualification and site-visit scheduling from portal enquiries
Conferences & events
Abstract screening, delegate enquiry handling, sponsor matching
Why me
11+ years, 100+ clients, one name on the work
11+ years across the whole stack
AI work that ignores where the data lives, how the network is built and who supports it on Monday tends not to survive contact with the business. I have spent 11+ years on the infrastructure, software and support side as well, which is why the data-foundations conversation happens early here rather than at the point it derails a build.
100+ clients, and a habit of saying no
The engagements that went well have one thing in common: something got cut early. I will tell you which of your ideas is a rule engine, which is a reporting problem, and which is genuinely worth a model — before you have paid for a build rather than after.
Single point of accountability
You get one person: the one who scoped it, built it and will answer the phone when it misbehaves. No account manager, no handover to a delivery team you have not met, and no junior quietly assigned after the pitch.
You own it, and you can leave
Your repository from the first commit, your model and cloud accounts, documentation written as the work happens. Moving to another consultant or bringing it in-house stays a real option — which is the only thing that keeps a retainer honest.
FAQ
AI consulting in Bangalore — questions I get asked
What does an AI consultant in Bangalore actually do?
Three things, in this order: work out which of your problems is worth solving with AI, prove one of them on your real data before you commit a budget, and then build and run it. The first is the part most people skip and the part that decides whether the rest is wasted. A good engagement often ends with fewer AI projects than you walked in with — two of the five ideas usually turn out to be a report, a rule, or a fixed process.
How much does AI consulting cost in India?
Independent and boutique consultants in India bill roughly ₹25,000–₹75,000 a day, against ₹1,50,000–₹3,00,000 a day for a Big 4 firm doing comparable advisory work. Most of my engagements are not sold by the day though — a scoped proof of concept is ₹1,50,000–₹4,00,000 and a monthly retainer ₹40,000–₹1,50,000. The table above breaks all three down, and every figure is a market range rather than a fixed rate card.
Do I need to be in Bangalore to work with you?
No. I am Bengaluru-based, so if you are in the city we can do discovery and workshops in your office — which genuinely helps for the first session, because the useful information usually comes from watching people work rather than from a call. Everything after that runs remotely, and most of my clients are not in Bengaluru at all.
Is my data safe if you build on OpenAI or Claude?
It depends entirely on which contract you are under, and this is worth getting right before anything is built. Consumer tiers may train on your inputs; the business and API tiers of OpenAI, Anthropic and Google contractually do not, and offer zero-retention options. Part of scoping is deciding what may leave your network at all — some things should not, and for those the answer is an open-weight model running on your own infrastructure, which I will tell you costs more and performs less well than the API you were hoping to use.
What if AI is not the right answer for my problem?
Then I say so, in the first week, and you have paid for a discovery rather than a build. This happens often enough that it is worth stating plainly: a lot of what gets pitched as an AI problem is a data problem, a process problem, or a reporting problem wearing a better suit. Automation without a model is usually cheaper, more reliable and easier to hand over — and if that is your situation, I would rather sell you that.
How long before I see something working?
A proof of concept on your own data in two to four weeks. That is deliberate: the point of a PoC is to fail cheaply if it is going to fail, so it runs on real documents and real volumes rather than a curated demo set. Production hardening — evaluation, guardrails, monitoring, handover — is another four to eight weeks depending on how much it touches.
Will this replace my staff?
In almost every engagement I have run, no — it removes the part of their job they liked least. The honest version: AI is good at the repetitive, high-volume, low-judgement layer, and bad at the exceptions, which is where your experienced people actually earn their salary. If your plan is a headcount cut, say so at the start, because it changes what you should build and I would rather know.
What do I own at the end?
Everything. Code in your repository from the first commit, models and prompts documented, cloud and model-provider accounts in your name and paid by you directly. If you want to move the work to another consultant or bring it in-house, nothing technical stops you. That is a deliberate design choice — a consultant whose value depends on you being unable to leave is not giving you advice, they are building a moat.
Do you do AI training for our team?
Yes, and for a lot of Bengaluru SMEs it is the cheaper first step. A half-day session on what current models can and cannot do, run against your own use cases rather than generic slides, usually kills two or three bad ideas and surfaces one nobody had thought of. It is billed at the day rate.
Can you work with our existing tech team?
That is the more common arrangement. Your engineers usually know your systems far better than I will; what they typically have not done is shipped a retrieval system, written an evaluation harness, or costed inference at production volume. I do the AI-specific parts and the design decisions, they keep ownership, and the knowledge stays in the building after I leave.
Tell me what you are trying to automate.
One message describing the process that is eating time. I will tell you whether AI is the right tool, what it would realistically cost, and what I would do first — within 24 hours. If the answer is that you do not need a model, you will get that instead.
100+ clients served·Free, no-obligation quote·NDA on request·You own everything at handover