AI agents, support chatbots, document extraction and retrieval over your own files — scoped as a fixed-price pilot against one measurable number, evaluated on your real data, and handed over with the source code.
The demo is the easy part. A language model wired to a prompt will impress a boardroom inside a week. What breaks three months later is everything the demo skipped — what happens when the model is confidently wrong, who reviews its output, what the monthly token bill looks like at real volume, and whether anyone can tell if last week's change made it better or worse. When businesses search for an AI agency near me or an AI development company, that gap is usually what they have already been burned by.
We build the unglamorous half. Every project starts with one process and one number it has to move — hours saved on quotation drafting, percentage of support tickets deflected, invoices keyed per day. It runs as a fixed-scope pilot against your real data, including the awkward cases, and it is scored against an evaluation set before it meets a customer. If it clears the bar, we harden it and hand over the source. If it does not, you have spent a small fixed sum to learn that cheaply — which is a far better outcome than discovering it after a platform commitment.
Ordered roughly by how reliably they pay for themselves in an Indian SME, not by how impressive they look in a pitch deck.
Not a chat window — a process. An agent that reads an incoming RFQ email, extracts the line items, checks them against your price list and drafts the quotation for a human to approve. Agents earn their keep when they remove a repeated, rule-heavy task, and we scope them against the hours they actually save.
Trained on your documentation, catalogue and past tickets rather than a generic model — so it answers from your pricing and your policies. Deployed to WhatsApp, your website or both, with a clean handover to a human the moment the model is unsure. We instrument deflection rate from day one; a bot nobody measures is a bot that quietly annoys customers.
Retrieval-augmented generation over the documents your team wastes hours searching — contracts, drawings, SOPs, compliance files, service manuals. Answers come back with citations to the source page, which is the difference between a tool your team trusts and a novelty they stop opening after a fortnight.
Turning PDFs, scans and photographs into structured data that lands in your ERP or spreadsheet. Purchase orders, invoices, lorry receipts, test certificates, KYC documents. This is the least glamorous AI work and reliably the highest return for Indian manufacturing, logistics and finance teams.
Demand forecasting, lead scoring, churn prediction, quality classification from images. Often these need classical machine learning rather than a large language model, and we will say so — an LLM is an expensive way to do arithmetic a regression handles better.
Getting your business cited inside ChatGPT, Gemini, Perplexity and Google AI Overviews is a different discipline from building AI. It has its own page — see our answer engine optimization services — but it is the request we most often receive under the words 'AI agency'.
None of them are about which model is best this month. All four are made before a line of production code is written.
We are not tied to one vendor. Frontier models for reasoning-heavy work, smaller and cheaper models for classification and extraction, and open-weight models running on your own infrastructure where data cannot leave the building. Most production systems we ship use two or three models at different points, chosen on cost and latency.
Most businesses asking to 'train our own model' need retrieval, not training. Fine-tuning is expensive, ages badly and has to be redone every time your content changes; a well-built retrieval layer updates the moment you update a document. We fine-tune when the task is a narrow, stable format — and we will tell you when it is not worth it.
Before anything faces a customer we build an evaluation set from your real queries and score every change against it. Without that, 'the AI got worse after the update' is an argument nobody can settle. Prompt-injection defence, refusal behaviour and PII handling are designed in, not bolted on after a bad week.
Token cost is a running operational expense, not a one-time build cost. Caching, routing cheap queries to cheap models, and trimming context are the difference between a pilot that scales and one that gets switched off when the first full month's bill arrives. We model this before you commit.
We would rather lose the project than sell one that gets quietly switched off in six months.
If there is exactly one right output and a clear rule that produces it, write the rule. A validation script is faster, cheaper, auditable and never hallucinates. We have talked more than one client out of an AI project and into a fortnight of ordinary automation.
Prediction needs history. If nobody has recorded outcomes — which leads closed, which parts failed, which tickets resolved — no model can be built, and the honest first project is instrumentation, not AI. This is the most common blocker we find in Indian SMEs.
An AI system that drafts quotations needs a person who approves quotations. Where no one owns the process today, adding AI adds an unreviewed output nobody trusts. We ask who signs off before we ask what to build.
A surprising share of AI enquiries are solved by a proper database index, a dashboard or fixing how files are named. Cheaper, faster, and it does not need a GPU budget.
Scoping benefits from being in the room. Almost nothing after it does — so we take projects nationally and travel when a build genuinely needs it.
Our base. Manufacturing and engineering work across the Chakan, Ranjangaon and Bhosari MIDC belts — document extraction, quality vision and demand forecasting — alongside SaaS and product teams in Hinjewadi, Kharadi and Magarpatta wanting agents inside an existing product.
Financial services, logistics and D2C. Document-heavy compliance work, KYC extraction and customer-support automation, plus the AI-automation and chatbot briefs that come out of Vashi, BKC and the Navi Mumbai corporate parks.
Product and SaaS teams embedding AI features into software that already has users — which is an engineering integration problem far more than a model problem, and is scoped as such.
Manufacturing, automotive ancillaries and enterprise IT along the OMR and Oragadam corridors — extraction, forecasting and internal knowledge search over large document estates.
Engineering, tea and leather export businesses digitising paperwork for the first time, and Sector V product teams adding AI-driven features to existing software.
Textile, diamond and chemical exporters. Catalogue-heavy businesses where image classification, specification search and multilingual customer chat return the most, fastest.
An hour on what the process looks like today, who performs it, how long it takes and what an error costs. We come out of that call with either a defined pilot or an honest 'this is not an AI problem'. Both are useful answers and neither costs you anything.
One process, one measurable number, four to six weeks, a fixed price. The pilot exists to answer a single question — does this work well enough on your real data to be worth building properly — before anyone commits to a platform.
We build an evaluation set from your genuine cases, including the awkward ones, and report accuracy honestly against it. If the pilot does not clear the bar we agreed, we say so. A pilot that fails cheaply is a good outcome; a pilot that is declared a success and then quietly abandoned is not.
Monitoring, logging, cost dashboards, fallback behaviour and documentation. You get the source code and the prompts. We are happy to stay on a support retainer, but you should be able to leave, and nothing about the build should make that hard.
In practice, three things. It identifies which of your processes are worth automating and which are not. It builds the system — usually a combination of retrieval over your own documents, a language model, and ordinary software plumbing into your existing tools. And it evaluates that system against real data so you can see whether it works before it faces a customer. Most of the effort is the first and third; the model itself is rarely the hard part, which is why any agency selling you a model rather than a process is selling the easy bit.
Our fixed-scope pilots start at ₹1.5 lakh for one process over four to six weeks, which is deliberately small enough to be a decision rather than a project. Full production builds typically land between ₹4 lakh and ₹20 lakh depending on how many systems have to be integrated and how strict the accuracy requirement is. Running costs are separate and matter more than people expect — token and hosting charges for a well-optimised system usually sit between ₹5,000 and ₹60,000 per month depending on volume, and we model that number before you commit rather than after.
Yes. We are based in Pune and Pimpri Chinchwad and we run AI projects for clients in Mumbai, Navi Mumbai, Bangalore, Chennai, Kolkata, Hyderabad, Surat, Vadodara and Nashik. AI work is almost entirely remote once scoping is done — the discovery call benefits from being in person and very little after it does. Where a project needs on-site time, such as camera placement for a vision system, we travel.
Generative AI produces new content — text, code, images, summaries — and is what people usually mean by AI today. Traditional machine learning predicts or classifies from historical data: which lead will convert, which part will fail, whether this photograph shows a defect. They solve different problems and cost very different amounts to run. A good AI development partner will tell you when your forecasting problem needs a regression model rather than a language model, because the regression is cheaper, faster and more accurate for that job.
Usually, yes, with one condition — the process has to be describable. If you can write down the steps a competent new employee would follow, an agent can be built against it. If the process lives entirely in one person's judgement and changes case by case, an agent will produce confident nonsense. The agents that work in Indian SMEs tend to be narrow: quotation drafting, RFQ triage, order-status answering, invoice matching, first-line support. Broad 'do anything' agents demo well and fail in production.
Not on our builds. We use enterprise API tiers where the provider contractually does not train on submitted data, and for genuinely sensitive work we run open-weight models on your own infrastructure or a private cloud instance so nothing leaves your control at all. We will tell you plainly which of the two a given design uses, because the second costs more and you should know what you are paying for.
A pilot runs four to six weeks. Production hardening after a successful pilot typically adds six to twelve weeks, most of which is integration with your existing systems, evaluation and edge-case handling rather than model work. Anyone quoting a two-week production AI system is either rebuilding something they have already built or has not asked what happens when the model is wrong.
It is the healthiest place to start. A good part of our scoping calls end with a recommendation that is not AI — a database index, a report, a form, or two weeks of conventional automation. We would rather give that answer for free than sell a project that gets switched off in six months. If AI genuinely is the right tool, the same call produces a pilot scope and a fixed price.
Talk to us
Tell us what you need. We reply within 24 hours — or call now for an instant answer.