AI integration in Mumbai
Lenders and insurers in Mumbai process a great many documents: bank statements, salary slips, tax returns, claim forms, scanned policies. Language models can read them far faster than a back office can, and the demos prove it. What the demos skip is what a finance business needs in production: knowing how often the extraction is wrong, having a person review the cases that matter, keeping a record of what the model saw and said, and choosing carefully where the documents are sent.
We build document AI with those parts included. An evaluation set from your real documents, so accuracy is a number rather than an impression; confidence thresholds that route doubtful cases to a person; a log of every extraction; and a fallback when the provider is unavailable. Which model and which provider handle customer documents is agreed with you before the first prompt is written, rather than after the pilot.
Mumbai is India's financial capital. Both major stock exchanges are here, and so are many of the country's banks, NBFCs, brokers, wealth managers, insurers and fintechs. For these companies software is judged first on whether the money is right. A loan ledger that drifts from the bank statement, a payout released twice because a partner timed out, a portfolio screen showing yesterday's price: each one lands on an operations desk the next morning. What they need built is rarely a new product from nothing. It is a lending or payments backend that reconciles, a platform where each role sees exactly what it should, and approvals that need two people before money moves. Engineers who have built those systems are in demand across the city, so the work often waits for a hire.
The city's second industry is attention. The Hindi film industry, most of the country's television networks and many of its large publishers are based here, and their software problems are specific: traffic that arrives all at once on a release or a match day, rights and metadata that decide what can be shown where, and archives that are valuable only if someone can search them. A slow page on a publisher's site is lost revenue, because the readers came from search and will go straight back to it.
The third is consumer business in every size. Mumbai has D2C brands selling beauty, fashion and food through their own stores and every marketplace at once, and it is home to large family-owned groups and conglomerates whose older divisions still run on spreadsheets and exports from their accounting software. In the bigger groups the buyer is usually a digital team with a mandate, a budget and a procurement process, and the work has to survive that process as well as production.
We are in Bengaluru and share your time zone, so the working day is the same on both sides and a question asked in the morning is answered the same morning. You talk directly to the engineer who writes the code. The work happens in your repository and a shared chat channel, where every decision is written down, and the runbooks and architecture notes are written as we go rather than at the end.
We are the right fit when a Mumbai team has a defined piece of work and wants a senior engineer on it within days: a ledger that has to reconcile, a migration with an end date, or extra capacity while you hire. Start with a fixed-price two-week piece, or bring us in white-label for your agency's clients under your name.
Four ways this arrives.
A prompt that behaved on twenty hand-picked inputs meets ten thousand real ones. We build the evaluation set from your actual traffic first, so a change can be judged rather than argued about.
Usually a large context sent on every call, or a big model doing a job a small one can do. We measure where the tokens go before recommending anything.
Logging that captures the prompt, the retrieved context, the model version and the output, so a support question has an answer that is not a guess.
A fallback path, a timeout that is shorter than your user’s patience, and a degraded mode that is honest about being degraded.
A weekly maintenance pulse where Claude writes only the prose, checked against a JSON schema and a whitelist of numbers, with a template that takes over when the model fails.
Read the write-up →Asked by Mumbai teams.
How do you work with teams in Mumbai?+
We are in Bengaluru and share your time zone, so calls, reviews and releases fit inside your normal working day. We work in your tools, Slack, GitHub and Linear or whatever your team already uses, and every decision is written down in the repository or the channel. You talk directly to the engineer writing the code, from the first 30-minute call through to handover, with runbooks and architecture notes written as we go.
Do you build for Mumbai lenders, brokers and fintechs?+
Yes. We build the parts where the money has to be right: loan and payment ledgers that reconcile against the bank's records every day, retries that cannot turn a timeout into a double payout, maker-checker approvals where a second person signs off before money moves, and a full history of every change of state. We work inside the system you already run, in Node, Python and Next.js, and ship in increments.
How does your rate compare to hiring in Mumbai?+
PayScale puts a mid-career software engineer in Mumbai at about ₹15 lakh a year. Our published rate is $35 an hour, with a $5,000 minimum. That buys a senior engineer who writes the code, works in your Slack, GitHub and Linear, and starts within days, with no recruiting round, notice period or employment overhead. Start with a fixed-price two-week piece at $2,800 and judge us on the output before committing to more.
Can you work white-label for our Mumbai agency?+
Yes. Mumbai's digital and media agencies bring us in when a client needs a backend, a mobile app or a data pipeline alongside the design and campaign work. We work under your name, in your client's repositories and your channels, and sign an NDA before specifics. The code belongs to the client from the first commit. You keep the relationship, we write the code and the runbooks, and your team presents the work.
Will you tell us if we do not need a model?+
Yes, and it happens often. Several requests we have taken turned out to be a search problem, a rules engine, or a form with better defaults. We would rather say that in week one than bill for a year of prompt tuning.
Whose API keys?+
Yours, in your accounts, with the spend visible to you. We never proxy your traffic through infrastructure we control.
What about our data going to a provider?+
We map exactly what leaves your systems and where it lands before anything is wired up. Where that is not acceptable, self-hosted open-weight models on your own hardware are a real option and we will price both.
Do you fine-tune?+
Rarely, and not as a first move. Retrieval and prompt structure fix most of what people bring to us as a fine-tuning problem, at a fraction of the cost and with none of the retraining treadmill.