MLOps and AI Infrastructure in Mumbai
e10 Infotech provides MLOps services and AI infrastructure in Mumbai: model serving, evaluation pipelines, tracing, guardrails and strict cost control.
MLOps and AI infrastructure for systems that stay reliable after launch
An AI feature that works in a demo and an AI feature that works on a Tuesday afternoon under load are different pieces of engineering. The gap is infrastructure: serving that scales, evaluation that runs on every change, tracing that explains a bad answer, and cost controls that stop a loop from spending a month's budget overnight. e10 Infotech Private Limited builds that layer so your AI features can be operated by the same team that runs everything else. Businesses in Mumbai come to us when a prototype has to become a supported service.
What we build
- Model serving on managed endpoints or self hosted with vLLM and Ollama
- GPU capacity planning, autoscaling and scheduling with spot and reserved mixes
- Evaluation pipelines wired into continuous integration and release gates
- Tracing and observability covering prompts, tools, retries and token spend
- Prompt, model and dataset versioning with reproducible rollbacks
- Caching, batching and routing layers that cut cost without hurting quality
- Guardrails for injection, data leakage, unsafe output and runaway loops
- Feature stores, vector infrastructure and training pipelines for classical models
How the platform is built
Everything is infrastructure as code in your cloud account, because an AI platform your team cannot rebuild is a liability. Serving sits behind an internal interface so providers and models can be swapped without touching product code. Evaluation runs on every pull request against a fixed set, and a release that drops below threshold does not ship. Tracing captures the full request tree, so an incident starts from a trace rather than a guess. Spend has hard limits at request, user and tenant level, with alerts before the ceiling rather than after. Deployments are progressive, with canary traffic and automatic rollback on quality or error regressions. Secrets, tenancy boundaries and data retention are set once, in code, and reviewed as part of the platform rather than per feature.
Where this fits
This is the platform layer under our AI First Development practice. It carries AI Agent Development, RAG and Knowledge Systems, Generative AI Development and AI Chatbot Development in production, and it is where model choices from LLM Integration and Fine Tuning are served and measured. Scheduled processing from AI Workflow Automation runs on the same infrastructure. Platform investment is prioritised through AI Consulting and Strategy, engineering practice through Building with AI Coding Tools, and surrounding services through Software Development and API Integration.
Observability and evaluation in production
A trace shows the prompt, the retrieved context, every tool call and its arguments, the model version, token counts and latency at each hop. Failures are clustered so a recurring problem is visible rather than buried in volume. Online evaluation samples live traffic and scores it against the same rubric used offline, which is how quality drift is caught before users complain. User feedback, thumbs down responses and escalations feed the same pipeline, so the evaluation set grows from real usage.
Cost, capacity and reliability
Token spend is attributed per feature, per tenant and per user, so an unexpected bill has an owner rather than a mystery. Caching covers repeated context and identical requests. Provider outages are handled with fallback models and degraded modes that keep the product usable. For self hosted serving we size GPU capacity against measured throughput, mix reserved and spot instances, and set queueing behaviour so a spike delays requests rather than dropping them.
What you get at handover
Infrastructure as code in your cloud account, serving and gateway configuration, the evaluation pipeline wired into your continuous integration, tracing dashboards and alert routing, cost attribution reporting, guardrail configuration, capacity and scaling documentation, an incident runbook, 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 engineering 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 and retention rules decide which regions and providers are usable, and they differ by jurisdiction, so we confirm what applies to businesses in Mumbai before the platform is designed. That answer shapes the architecture rather than following it.
You get one point of contact, senior engineers on delivery, and reporting tied to running infrastructure and measured quality rather than activity. Businesses in Mumbai and the wider region are supported on the same terms.
Ready to make your AI features operable?
Tell us what is already running and we will send a platform assessment, a plan and a quote.
Serving Mumbai and the wider region.
Queries raised before signature
Everything worth knowing about MLOps and AI Infrastructure in Mumbai.
01What is MLOps in the context of language models?
The practice that keeps prompts, datasets and models under version control, scores every change against a fixed set, serves requests reliably, traces them end to end and holds cost to a budget. It is what lets an AI feature be operated like any other production service.
02Do we need a platform or can we call an API directly?
One feature can call an API directly. By the third feature you need shared serving, evaluation, tracing and spend controls, or every team rebuilds them differently and none of them well.
03Should we self host models?
Self hosting wins when data cannot leave your tenancy, when volume is high and steady, or when a tuned small model matches a hosted large one. Below that, managed endpoints are usually cheaper overall.
04How do you control AI spend?
Spend is attributed per feature, tenant and user. Hard ceilings apply at request and account level. Repeated context is cached, requests are batched, cheaper models take the work they can handle, and alerts fire before the limit rather than after it.
05What does tracing show?
The full request tree: prompt, retrieved context, tool calls and arguments, model version, token counts, latency per hop and the final output, which is what makes a bad answer debuggable.
06How do you catch quality regressions?
Offline evaluation on every pull request with release gates, plus online sampling of live traffic scored against the same rubric, so drift is visible without waiting for complaints.
07How long does a platform build take?
A working platform with serving, evaluation, tracing and cost controls usually takes eight to fourteen weeks. Self hosted GPU serving and multi region deployment add to that.
08What happens when a provider has an outage?
Traffic fails over to a fallback model, and where quality would suffer the product degrades to a documented reduced mode rather than returning errors.
09Which tools do you use?
vLLM and Ollama for serving, Langfuse and OpenTelemetry for tracing, Ragas and custom rubrics for evaluation, Terraform and Kubernetes for infrastructure, and Bedrock, Vertex AI or Modal where managed serving fits.
10Can you work with our existing cloud setup?
Yes. Everything is deployed into your accounts using your networking, identity and logging conventions, so the platform is reviewed and operated like the rest of your estate.
11How is GPU capacity planned?
From measured throughput and latency targets, with a mix of reserved and spot capacity, autoscaling on queue depth and a documented behaviour for spikes that favours queueing over dropping.
12Who operates the platform afterwards?
Your team, with the runbooks and dashboards to do it, or us under a support agreement covering upgrades, incident response, evaluation runs and cost review.
13Do you offer MLOps and AI Infrastructure in Mumbai?
Yes. e10 Infotech delivers MLOps and AI Infrastructure 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 MLOps 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 running infrastructure and measured quality 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