AI First Development in Mumbai
e10 Infotech is an AI first development company in Mumbai building AI agents, RAG systems, LLM integrations, chatbots and AI workflow automation.
An AI first development company that ships models into production
e10 Infotech Private Limited builds software where a model does real work in the product, not a chatbot bolted onto a finished app. Every engagement starts by finding the tasks a model is genuinely better at than deterministic code, and the tasks it should never be trusted with. That boundary decides the architecture, the evaluation suite, the human review points and the cost per request, so the system stays accurate, auditable and affordable once traffic arrives. Businesses in Mumbai come to us when an AI feature has to work reliably for customers rather than impress a boardroom.
What we build
- Autonomous and supervised agents that plan, call tools and complete multi step work
- Retrieval systems over private documents, tickets, catalogues and code
- Assistants and copilots embedded inside existing products and internal tools
- Document extraction, classification and routing pipelines
- Fine tuned and distilled models where prompting alone stops being economical
- Evaluation suites, guardrails and observability for model behaviour
- Serving infrastructure with caching, routing, fallback and cost controls
- AI assisted engineering practice inside your own development team
How an AI first build runs
Discovery picks one measurable task and defines what a correct answer looks like, because a system without a scoring rubric cannot be improved. We build an evaluation set from your real data before we build the feature. A baseline ships early, usually retrieval plus a strong general model, and every change is measured against the same set rather than judged by demo. Prompts, tools and context windows are version controlled like code. Guardrails cover prompt injection, data leakage, hallucinated citations and runaway tool calls. Cost and latency budgets are set per request, with smaller models and caching used wherever quality allows. Once live, traces, failure clustering and user feedback drive the next iteration.
Where the pieces fit
Most AI projects combine several of these services. AI Consulting and Strategy picks the use cases worth funding, RAG and Knowledge Systems grounds answers in your own content, and LLM Integration and Fine Tuning connects or specialises the model. Interactive products come from AI Agent Development, AI Chatbot Development and Generative AI Development, while back office work is handled by AI Workflow Automation. Everything runs on MLOps and AI Infrastructure, and your own engineers get faster through Building with AI Coding Tools. Conventional application work comes from Software Development and Web Development, and on chain systems from Web3 Development.
Models, tools and techniques
We work across hosted and open weight models including the Claude, GPT, Gemini, Llama, Mistral and Qwen families, and we choose per task rather than per vendor. Common tooling is LangGraph, LlamaIndex and the Vercel AI SDK for orchestration, pgvector, Qdrant and Pinecone for retrieval, Langfuse and OpenTelemetry for tracing, Ragas and custom rubrics for evaluation, vLLM and Ollama for self hosting, and Modal, Bedrock or Vertex AI for serving. Techniques in regular use include hybrid search with reranking, structured output and tool calling, context compaction, speculative and cascaded routing, LoRA fine tuning, and human in the loop review for anything irreversible.
Accuracy, privacy and governance
Model output is treated as untrusted input until it is checked. Irreversible actions sit behind confirmation or a human approver. Private data stays inside your tenancy, with self hosted or zero retention endpoints where policy requires it, and prompts are logged with the same care as any other record containing customer information. We document data flows, retention, model versions and known failure modes so your compliance team can review the system rather than take it on faith.
What you get at handover
Source code in your repository, infrastructure as code in your cloud account, the evaluation set and its scores, prompt and model version history, tracing dashboards, a cost per request breakdown, a runbook for regressions and model deprecations, and a thirty day warranty on behaviour that does not match the specification.
Working with businesses in Mumbai
Work for clients in Mumbai runs remote first: a named team, a scope agreed in writing before anything starts, and demos on a fixed cadence you can hold us to. Working hours overlap your business day and everything is delivered in English.
Data residency rules and sector guidance differ by jurisdiction, so we confirm what applies to businesses in Mumbai before choosing hosted or self hosted models. That decision shapes the architecture rather than following it.
You get one point of contact, senior engineers on delivery, and reporting tied to evaluation scores and shipped features rather than activity. Businesses in Mumbai and the wider region are supported on the same terms.
Ready to put AI to work in your product?
Tell us the task you want handled and we will send an approach, an evaluation plan and a quote.
Serving Mumbai and the wider region.
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Queries raised before signature
Everything worth knowing about AI First Development in Mumbai.
01What does AI first development actually mean?
It means designing the product around what a model can reliably do, then writing conventional code around that core. The alternative, adding a chat box to finished software, rarely changes how the product performs.
02How do you stop the system from hallucinating?
Ground answers in retrieved source material, require citations that are checked against the retrieved text, constrain output to a schema, and score every release against a fixed evaluation set. Anything unverifiable is refused rather than guessed.
03Should we use a hosted model or host our own?
Hosted models are faster to reach quality and usually cheaper below moderate volume. Self hosting wins when data cannot leave your tenancy, when volume is high and steady, or when a small fine tuned model matches a large general one.
04Do you fine tune models?
When it earns its keep. We exhaust prompting, retrieval and routing first, then fine tune to cut cost, lock in a format or teach a genuinely private skill. Fine tuning is a way to make a solved task cheaper, not a way to solve it.
05How much does an AI project cost?
A grounded assistant over your own content is the smallest useful engagement. Agents with tool access, evaluation suites and production infrastructure cost more. Ongoing inference is a separate operating cost we budget explicitly.
06How long before we see something working?
A measurable prototype on your real data usually lands within three to four weeks. Production hardening, evaluation coverage, guardrails and monitoring typically take another six to ten weeks depending on integrations.
07Will our data be used to train someone else's model?
No. We use zero retention or enterprise endpoints, or self hosted models, and we contract for it. Training on your data happens only when you ask for it and only on infrastructure you control.
08Can you add AI to our existing product?
Yes. We start with a review of your data, permissions model and integration points, ship one narrow feature with measurement attached, then widen scope once the numbers justify it.
09How do you measure whether the AI is any good?
With an evaluation set built from your real cases and scored on task specific criteria, tracked per release alongside latency, cost per request, deflection or completion rate, and human review pass rate.
10What about prompt injection and misuse?
Untrusted content is isolated from instructions, tools are scoped to least privilege, irreversible actions require confirmation, and output is validated before it reaches another system. We test with adversarial cases before launch.
11Do you help our developers use AI coding tools?
Yes. We set up the tooling, repository context, review standards and guardrails so AI assistance speeds your team up without degrading code quality or leaking source.
12What happens after launch?
You can take it in house or keep us on a support plan covering evaluation runs, model upgrades, prompt maintenance, cost tuning, incident response and periodic red teaming.
13Do you offer AI First Development in Mumbai?
Yes. e10 Infotech delivers AI First Development for businesses in Mumbai and the wider region, remote first with a named team and a scope agreed before work starts.
14How do you run AI First Development projects for clients in Mumbai?
Working hours overlap the Mumbai business day, communication is in English, and you get one point of contact with reporting tied to evaluation scores and shipped features rather than activity.
Sign off and we start
Tell us what you are trying to build. You will hear back from an engineer, not a sales desk.
- For
- e10 Infotech Private Limited
- Office
- Mumbai, Maharashtra
- Established
- 2011
- Direct line
- +91 86574 40720