AI Development & Integration for Indian Enterprises
Indian enterprises are flooded with AI pilots and starved of production systems. The blockers are real: data residency, DPDP and RBI obligations, and prototypes that never touch the ERP. Opsio's engineers build LLM apps, RAG, agents, and ML models, then deploy them in AWS Mumbai or self-hosted on open weights, integrated and operated under a 99.9% SLA.
Trusted by 100+ organisations across 6 countries
Mumbai / Hyderabad region
India data-residency deployment
DPDP-ready
Built for the 2023 Act, RBI & CERT-In
50+
Engineers across India & Sweden
99.9%
Uptime SLA, 24/7 NOC
What's Included
Shipping AI in India means getting every layer right and respecting constraints that lighter markets ignore. A model that cannot run inside the country, a pipeline that leaks personal data, or an integration that bypasses your audit trail will not survive a compliance review, however good the demo looked. Opsio delivers the complete stack with engineers who have built and operated these systems under real regulatory weight. The six capabilities below, generative-AI and LLM applications, retrieval-augmented generation over your data, action-taking agents, custom and fine-tuned models, MLOps and deployment, and integration into your enterprise systems, each arrive with evaluation, guardrails, and observability included, and each can be delivered on managed India-region cloud or fully self-hosted on open weights where your data is not permitted to leave your own environment.
Generative-AI & LLM Applications
Assistants, copilots, extraction, and content generation on frontier or open-weight models. We manage prompts, context, streaming, and cost, with evaluation built in, and can run the whole application inside your India-region tenancy where residency requires it.
Retrieval-Augmented Generation (RAG)
Grounded answers over your policies, contracts, and records. We engineer chunking, embeddings, vector search, and reranking so the model cites your real content, with the option to keep the entire pipeline self-hosted on Indian infrastructure.
AI Agents & Workflow Automation
Agents that plan, call internal APIs, and complete multi-step processes across your stack. We add guardrails, human approval gates, and complete action logging, aligned to the auditability your regulators and CERT-In obligations expect.
Custom ML & Model Fine-Tuning
Fraud, credit-risk, forecasting, and classification models, plus fine-tuning and adapters to specialise foundation models on Indian-language and domain data. We own data preparation, training, evaluation, and the fine-tune-versus-retrieve decision.
MLOps, CI/CD & Deployment
Versioning with MLflow, automated evaluation gates, CI/CD, and containerised serving on Kubernetes, deployable to AWS Mumbai, Azure or GCP India regions, or your own data centre. Every model ships reproducible, observable, and safe to update.
Enterprise & Core-System Integration
Connecting AI to core banking, ERP, CRM, and data platforms through their existing authentication, audit, and access controls. Outputs are validated, every action logged, and personal data kept within your DPDP-compliant boundary throughout.
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- Certified cloud engineers
Opsio's focus on security in the architecture setup is crucial for us. By blending innovation, agility, and a stable managed cloud service, they provided us with the foundation we needed to further develop our business. We are grateful for our IT partner, Opsio.
Jenny Boman
CIO · Opus Bilprovning
Production AI, built for Indian compliance
Across India's BFSI institutions, global capability centres, ITeS firms, and product startups, the same pattern repeats: an enthusiastic AI experiment that demos well and then stops at the door of production. The reasons are specific to this market. Data cannot always leave the country. A regulator, the RBI, SEBI, or IRDAI, has views on where models run and how decisions are logged. The CERT-In directives impose logging and incident timelines. And the prototype, charming on a laptop, was never built to touch the core banking system or the ERP that the business actually runs on. Opsio is an engineering partner built to clear exactly these hurdles and put working AI into production. We deliver across the entire AI stack rather than one slice of it. Our engineers build generative-AI and large-language-model applications, design retrieval-augmented generation over your own documents and records, and construct agentic workflows that call internal tools and APIs to finish multi-step tasks. We develop and fine-tune machine-learning models for forecasting, fraud, classification, and ranking, and we build the data pipelines, embeddings, and vector stores that supply them. Critically for India, we are comfortable deploying not only on managed cloud endpoints but also on self-hosted open-weight models running inside your own VPC, for cases where source data is not permitted to leave your environment at all.
Compliance is treated as an engineering requirement, not a legal afterthought bolted on before go-live. The Digital Personal Data Protection Act, 2023 shapes how we handle personal data, consent, and the rights of data principals. For regulated financial workloads we design to RBI expectations around localisation, auditability, and outsourcing governance, and we build the logging and retention that CERT-In directions require. Where residency demands it, we keep data and inference inside Indian boundaries, whether in AWS Mumbai and Hyderabad regions or fully self-hosted. The aim is a system your risk, compliance, and audit functions can sign off without a fight, because the controls were part of the architecture from the first sprint.
On the cloud question we stay genuinely neutral and capable. Opsio deploys AI on Amazon Bedrock, Azure OpenAI Service, and Google Vertex AI, and selects among them on the models you need, the India regions available, and the platform your estate already favours, or runs open-weight models on Kubernetes inside your own tenancy when nothing may leave it. We are an AWS Advanced Tier Services Partner with the AWS Migration & Modernization Competency, a Microsoft Partner, and a Google Cloud Partner, so the recommendation you get reflects your situation rather than a single vendor relationship we are trying to protect.
India also lets us bring scale and economics that change what is feasible. Our ISO 27001-certified Bangalore delivery centre fields large, senior engineering teams at INR-denominated rates, which means a serious AI build, models, pipelines, integration, and MLOps, lands at a cost that makes ambitious programmes viable rather than theoretical. Combined with a 24/7 network operations centre and a 99.9% uptime SLA, this gives Indian enterprises and the GCCs operating here a delivery partner that is local in presence, compliant by design, and able to staff a programme deeply without the unit economics of an offshore vendor or a Western consultancy working against you. Featured reading from our knowledge base: Product Engineering: Streamline Development Processes – Opsio, AI Agent Consulting Services: A Practical Guide for Indian Enterprises in 2026, and Discover the Best Indian Software Development Companies. Related Opsio services: AI Solutions & Strategy for Indian Enterprises: Roadmap to ROI, Managed AI & Support for Indian Enterprises: Run AI in Production, IoT Solution Development India, and AI Chatbot Development for India.
How Opsio Compares
| Capability | Opsio AI Engineering | Generic AI Vendor | In-House From Scratch |
|---|---|---|---|
| Output | Production system, integrated & under SLA | Demo + strategy deck | Depends on hiring & ramp-up |
| Data residency | AWS Mumbai/Hyderabad or self-hosted in India | Often offshore or unclear | Possible but slow to build |
| DPDP / RBI / CERT-In | Engineered into the architecture from day one | Rarely addressed | Depends on internal maturity |
| MLOps & operations | CI/CD, monitoring, 24/7 NOC, 99.9% SLA | Seldom included | Must be built and staffed |
| Core-system integration | Core banking / ERP / CRM, with audit | Frequently out of scope | Slow and resource-heavy |
| Delivery economics | Senior India teams at INR rates | Premium consultancy pricing | High fixed cost of hiring |
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What You Get
Pricing & Investment Tiers
Transparent pricing. No hidden fees. Scope-based quotes.
Proof of Concept
Fixed fee, from approx. ₹12,00,000
2-4 week scoped PoC on your real data, India-region or self-hosted; varies by scope.
Production Project
Project-based, typically ₹35,00,000-₹1,30,00,000+
Build, integrate, harden, and deploy a compliant production system; varies by scope.
Dedicated AI Pod
Monthly retainer, from approx. ₹15,00,000/month
A standing team of senior AI engineers for ongoing delivery; varies by scope and seniority.
Transparent pricing. No hidden fees. Scope-based quotes.
Questions about pricing? Let's discuss your specific requirements.
Why Choose Opsio for Cloud Services
Indian enterprises evaluating AI partners face a crowded field of demo-builders and a thin layer of teams that can actually operate a regulated production system. Opsio belongs to the second group. We arrived at AI from years of running mission-critical cloud workloads under SLA, and we carry that operational and security discipline into every AI build. For BFSI, GCCs, ITeS providers, and product companies, the deciding factors are usually residency, compliance, depth of delivery, and whether the partner sticks around to operate what it builds. The points below set out, plainly, why those organisations choose Opsio over a generic AI vendor or a from-scratch internal effort.
Data Stays In India
Deploy in AWS Mumbai or Hyderabad, or self-hosted on open weights inside your VPC, so source data never has to leave Indian boundaries when rules forbid it.
Compliant By Design
DPDP Act 2023, RBI, and CERT-In obligations engineered into the architecture from sprint one, ready for your risk and audit functions to sign off.
Self-Hosting Or Managed
We run managed Bedrock, Azure OpenAI, and Vertex AI, or open-weight models on Kubernetes in your tenancy, whichever your data and budget demand.
Scale At INR Economics
Large, senior teams from our ISO 27001-certified Bangalore centre make serious AI programmes financially viable, not just technically possible.
Built To Be Operated
MLOps, evaluation, and a 24/7 NOC keep models monitored and maintained under a 99.9% uptime SLA, rather than degrading after launch.
Full Stack, One Team
Data, models, applications, integration, and operations from a single accountable partner, with no hand-offs where ownership quietly disappears.
Not sure yet? Start with a pilot.
Begin with a focused 2-week assessment. See real results before committing to a full engagement. If you proceed, the pilot cost is credited toward your project.
Our 4-Phase Delivery Process
We run engagements in short, gated phases so the investment de-risks before it scales, which matters all the more when a compliance review can halt a programme late. You get a working artefact early, decide on measured evidence whether to proceed, and avoid a single large deliverable arriving past deadline. The residency and compliance choices, India-region cloud or self-hosted, are settled at the start so the architecture you prove is the one you ship. The four steps below carry a use case from scoped proof of concept to a deployed, integrated, and handed-over production system.
Discovery & Proof of Concept
We scope the use case, fix success metrics, assess data and residency rules, and build a PoC on real inputs in a compliant India-region or self-hosted setup. Timeline: 2-4 weeks.
Build
We engineer the production system, models, RAG or agents, pipelines, evaluation, and guardrails, with cloud or self-hosted target and DPDP controls locked in. Timeline: 4-10 weeks, scope-dependent.
Integrate & Harden
We connect AI to core banking, ERP, CRM, and APIs, add auth, CERT-In-aligned logging, monitoring, and load testing, and tune cost and reliability. Timeline: 2-6 weeks.
Deploy & Handover
We deploy to AWS Mumbai, an India-region cloud, or your data centre, stand up MLOps and on-call, and train your team or operate it for you. Timeline: 1-3 weeks, then ongoing.
Key Takeaways
- Generative-AI & LLM Applications
- Retrieval-Augmented Generation (RAG)
- AI Agents & Workflow Automation
- Custom ML & Model Fine-Tuning
- MLOps, CI/CD & Deployment
Industries Served by Opsio
AI delivery in India varies sharply by sector, driven less by technology than by which regulator is watching and where data is allowed to sit. Opsio has delivered cloud and AI work across the regulated and data-heavy industries that dominate the Indian market, and we bring that context, residency, auditability, and required human checkpoints, straight into the build. The four sectors below are where our compliance and integration experience pays off most directly for enterprises and the global capability centres operating here.
BFSI
RAG over policy and product documents, fraud and credit-risk models, and document automation, built to RBI localisation, auditability, and outsourcing expectations with data kept inside India.
Global Capability Centres
Production AI platforms and embedded copilots for the captive's parent enterprise, delivered at India scale with the governance and residency controls a global group requires.
ITeS & Technology Services
AI accelerators, RAG knowledge systems, and agentic automation that ITeS firms can deploy for their own delivery or embed into client solutions under clear data boundaries.
Product Startups & E-commerce
In-app copilots, search and recommendations, and support automation embedded directly into the product, shipped with multi-tenant isolation and DPDP-aligned consent handling.
AI Development & Integration for Indian Enterprises — FAQ
What is AI integration?
AI integration is the engineering work of connecting an AI model or application to the systems your enterprise already operates, so its outputs flow into real processes instead of sitting in a disconnected pilot. For an Indian business that means wiring a model into your core banking platform, ERP, CRM, data warehouse, or internal APIs, while honouring the authentication, audit logging, and access controls those systems enforce, and while keeping personal data within DPDP-compliant boundaries. It also covers validating every output before it reaches a downstream system, handling failures gracefully, and ensuring data residency rules are respected throughout. Integration is usually the hardest and most valuable part of an AI programme, because it is what converts a clever model into something your teams genuinely use and your auditors are willing to approve.
RAG or fine-tuning, which suits our use case?
They address different needs and frequently work together. Retrieval-augmented generation (RAG) supplies the model with your current proprietary content at query time, so answers stay grounded in your documents and can cite sources, ideal when knowledge changes often or when traceability matters for audit. Fine-tuning alters the model's weights to learn a particular style, output format, or specialised task, and is valuable for consistent tone, structured responses, or Indian-language and domain vocabulary that prompting cannot reliably reach. In short, RAG governs knowledge while fine-tuning governs behaviour. Most systems we deliver use RAG for factual grounding and add fine-tuning only where evaluation proves it justifies the extra cost. With open-weight models, fine-tuning also enables fully self-hosted deployment when data cannot leave your environment.
How does AI embed into our existing systems?
Through the interfaces your other integrations already rely on, APIs, event streams, webhooks, and database connections, governed by your existing authentication and access controls. We usually deploy the AI service inside your own cloud tenancy or data centre, grant it scoped and audited access to the data it needs, and connect it to platforms such as your core banking system, ERP, or CRM so it can read context and, where appropriate, write back. Every output passes through validation and guardrails before reaching a downstream system, and every action is logged in line with CERT-In expectations. For higher-stakes agentic flows we insert human approval gates. The result is AI that behaves like a well-governed internal service, observable and controllable, rather than an opaque external dependency.
How do you handle DPDP compliance, security, and our IP?
Compliance and security are engineered into the architecture from the first sprint. Under the Digital Personal Data Protection Act, 2023 we design for lawful processing, consent, and data-principal rights, and for regulated finance we align to RBI localisation, auditability, and outsourcing governance, with CERT-In-compliant logging. Where residency requires it, data and inference stay within India, in AWS Mumbai or Hyderabad regions or fully self-hosted on open weights inside your VPC, so source data need never leave your environment. Managed endpoints we use, Bedrock, Azure OpenAI, Vertex AI, do not train on your inputs. Your code, models, fine-tuned weights, and data remain your intellectual property, delivered with full source and no lock-in. Our Bangalore delivery centre is ISO 27001-certified.
How long until we reach production?
A focused proof of concept on your real data, in a compliant India-region or self-hosted setup, usually runs two to four weeks, enough to prove value and surface risk before a larger commitment. A production build then typically takes four to ten weeks by scope, followed by two to six weeks to integrate with your core systems and harden for security, compliance, reliability, and cost, and one to three weeks to deploy and hand over. A straightforward use case can therefore reach production in roughly two to three months, while a complex, multi-system integration in a regulated environment takes longer. We work in gated phases specifically so you can judge progress on measured evidence at each stage, rather than waiting on one large deliverable that risks slipping.
Which cloud, or should we self-host inside India?
It depends on your residency, regulatory, and commercial constraints, and Opsio supports every option. We deploy on Amazon Bedrock, Azure OpenAI Service, and Google Vertex AI, using India regions such as AWS Mumbai and Hyderabad where available, and we choose based on the models you need, the cloud your estate already favours, and your spend. When data is not permitted to leave your environment at all, we run open-weight models self-hosted on Kubernetes inside your own VPC or data centre, giving you full residency and control. As an AWS Advanced Tier, Microsoft, and Google Cloud partner with no single-vendor allegiance, we give a recommendation grounded in your compliance position, and tell you plainly which approach fits and why.
Can you embed AI into our existing product instead of building separately?
Yes, and for product companies and ITeS firms it is frequently the strongest route. Rather than building a parallel tool, we add AI directly inside the product your customers or teams already use: an in-app copilot, RAG over their own data, intent-aware search, or agentic automation of a manual workflow. We work within your codebase, deployment pipeline, and design system, and we build for the multi-tenant isolation and per-customer data boundaries a real product, and DPDP consent handling, require. Evaluation and guardrails ship with the feature so quality is measured rather than assumed. The outcome is AI that feels native to your product and is maintained through your existing release process, not a bolt-on that complicates your roadmap or your compliance story.
What do we actually receive at the end of an engagement?
A running, integrated, compliant production system, not a report. Specifically: the deployed application or model on your chosen India-region cloud or self-hosted environment, the source code and infrastructure-as-code, the data and retrieval pipelines, an evaluation harness scoring quality on an ongoing basis, the MLOps pipeline for versioning and safe updates, integration with your named systems, CERT-In-aligned logging, monitoring and alerting, and documentation plus handover so your team can operate or extend it. Where you prefer, we continue to run and improve the system under SLA through our 24/7 NOC. Everything we build is yours, including models and fine-tuned weights, with no dependency on a closed Opsio runtime, so you retain full control and residency over the system long after delivery.
How much does it cost to build an AI solution?
Cost depends on scope, integrations, data readiness and security needs, with pricing quoted in INR. Opsio usually starts with a fixed-fee proof of concept to validate value quickly. A full build then runs as a fixed-scope project or a dedicated AI team billed monthly, ideal for BFSI and GCC programmes that evolve over time. The main drivers are integrations, data quality, compliance and ongoing model operations. Exact pricing is provided via a quote once we have scoped your solution together.
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AI Development & Integration for Indian Enterprises
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