Managed AI & Support for Indian Enterprises: Run AI in Production
A pilot that dazzled the board can quietly fail in production. Models drift as data shifts, token and GPU bills climb, and unchecked outputs invite compliance risk. Opsio operates your AI from our ISO 27001-certified Bangalore centre, with 24/7 follow-the-sun support, drift and quality evaluation, INR cost optimization, retraining, and analytics, all under firm SLAs.
Trusted by 100+ organisations across 6 countries
24/7
Bangalore + Sweden NOC
<15min
Critical incident response target
99.9%
Operational uptime SLA
50+
Cloud & AI engineers
What's Included
Managed AI & Support spans the entire operational life of production AI, delivered as one accountable service rather than a toolkit you assemble yourself. We bring together LLMOps and MLOps engineering, round-the-clock monitoring, structured evaluation, rupee-focused cost control, and analytics, all under SLAs. Your stack may run on Amazon Bedrock, Azure OpenAI, Google Vertex AI, open-weight models on dedicated GPUs, or a blend, and the operating logic stays constant: make behaviour visible, make regressions catchable, make spend predictable, and make improvement continuous. The six capabilities below are the foundations of the service. Each is delivered by named engineers working a follow-the-sun rhythm from our Bangalore centre and Sweden, instrumented with proven tooling, and reported back to you openly, so you always know how your AI performs and what we are doing to keep it sharp.
24/7 AI Monitoring & Support
Our Bangalore and Sweden NOC monitors your AI services every hour, alerting on latency, errors, quality drops, and cost spikes. Critical incidents draw a sub-15-minute response with clear escalation and on-call engineers familiar with your environment.
Model Drift & Quality Evaluation
We score live outputs continuously for accuracy, groundedness, and distribution shift using automated evaluation suites and LLM-as-judge techniques. Drift surfaces as a measurable signal, prompting review or retraining before customers or regulators notice.
LLMOps & MLOps Engineering
We run the operational pipeline end to end: versioned prompts and models, AI CI/CD, registries, automated regression gates, and reproducible deployments. Every change is tested and traceable, keeping audit trails intact for compliance review.
Cost Optimization (Token & GPU)
We trace token and GPU spend to the request level and cut it via right-sizing, caching, prompt compression, batching, and tiered routing, favouring India-region endpoints where sensible. Rupee unit economics become a metric we actively manage.
Retraining & Lifecycle Management
We run scheduled and trigger-based retraining, manage data versioning and labelling, and validate each new model against your evaluation suite before promotion. Models stay current and compliant without manual scrambles or unplanned downtime.
AI Analytics & Reporting
We convert operational telemetry into dashboards and reports covering model performance, usage, rupee cost trends, and business outcomes. Leadership sees AI's real contribution while engineers get the signals to prioritise the next improvement.
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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 for Indian enterprises, operated and governed
Indian enterprises have moved fast from AI curiosity to AI deployment. BFSI players, global capability centres, and ITeS firms now have models making real decisions and answering real customers. The challenge has shifted accordingly: the hard part is no longer building a model, it is keeping it dependable once it carries production load. Opsio's Managed AI & Support is built for exactly this stage. We take operational ownership of your deployed AI, instrument it for visibility, and run it against measurable service levels from our ISO 27001-certified delivery centre in Bangalore. That turns a promising pilot into a capability your organisation can stake business decisions on, without your own team firefighting at odd hours. Operating AI is unlike operating ordinary software, and that difference trips up many in-house teams. The code can sit untouched while the model's accuracy slips, because the data it learned from no longer matches the data it now sees. A credit model meets a new customer segment; a vernacular support assistant meets an unfamiliar phrase; a document system meets a corpus that has doubled. None of these raise an error. Opsio handles this by treating the model's own behaviour as something to be watched, sitting alongside latency and uptime. We monitor accuracy, groundedness, and output patterns as first-class signals, so quality loss shows up on a dashboard, not in a regulator's query or a customer escalation.
Cost discipline matters acutely in the Indian market, where AI economics are scrutinised closely against business value. Token consumption and GPU hours grow with adoption, and waste hides easily: bloated prompts, oversized context, needless retries, and premium models doing work a cheaper tier could handle. Left alone, the rupee cost of AI can balloon without any gain for users. Opsio profiles where your AI budget genuinely goes and engineers it down through model right-sizing, caching, prompt compression, batching, and intelligent routing. We manage unit economics as an operational metric, choosing India-region endpoints where they help with both latency and data residency, so your AI stays commercially justifiable as it scales across the organisation.
Governance is not optional for Indian enterprises, and it cannot be retrofitted. The DPDP Act 2023, RBI expectations for regulated entities, and CERT-In directives all shape how AI must be operated, logged, and secured. Opsio bakes these concerns into the operating model: guardrails and output filtering in the request path, continuous sampling and scoring of live responses, automated evaluation gates before any prompt or model change ships, and audit-ready logging from our ISO 27001-certified environment. Data residency and access control are handled deliberately, not assumed. This gives risk, compliance, and security leaders the evidence they need to defend AI use to boards and regulators alike, and lets the business adopt AI with confidence rather than caution.
Operating AI well also generates insight that too many teams discard. Every request, evaluation, and cost data point describes how your AI and your business are performing. Opsio converts that telemetry into AI analytics: dashboards and reports on model performance, usage, rupee cost trends, and business outcomes, giving leadership a clear line of sight into AI's contribution and where to invest next. Managed AI & Support is therefore far more than keeping systems alive. It is a continuous-improvement engine in which operation feeds measurement, measurement drives optimization, and your AI grows steadily more reliable, more compliant, and more valuable to the enterprise over time. Featured reading from our knowledge base: Managed DevOps Services: Outsourcing DevOps Done Right for Indian Enterprises, Machine Learning Cloud: Build, Deploy & Scale ML in Production for Indian Enterprises, and Azure Managed Services: Features, Benefits & Real-World Use Cases for Indian Enterprises. Related Opsio services: AI Development & Integration for Indian Enterprises, AI Solutions & Strategy for Indian Enterprises: Roadmap to ROI, MLOps Consulting & Implementation India, and AI Visual Inspection for Indian Manufacturing.
How Opsio Compares
| Capability | In-House Ad Hoc | Generic Cloud Support | Opsio Managed AI |
|---|---|---|---|
| AI quality & drift monitoring | Manual, sporadic | Infrastructure only | Continuous, automated |
| 24/7 coverage (India time) | Business hours | Offshore ticket queue | Bangalore + Sweden, real engineers |
| DPDP / RBI / CERT-In awareness | Inconsistent | Not addressed | Built into operating model |
| Token & GPU cost (INR) optimization | Reactive | Not included | Actively managed metric |
| Retraining & lifecycle | When someone remembers | Out of scope | Scheduled & trigger-based |
| AI analytics & reporting | Ad hoc spreadsheets | Generic dashboards | Model, cost & business insight |
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What You Get
Pricing & Investment Tiers
Transparent pricing. No hidden fees. Scope-based quotes.
Essential
from approx. INR 2.5 lakh/month
24/7 monitoring, incident response, and support for a small number of production models. Illustrative; varies by scope.
Professional
approx. INR 7-13 lakh/month
Adds drift and quality evaluation, active INR cost optimization, guardrails, and monthly AI analytics across multiple models. Illustrative; varies by scope.
Enterprise
custom retainer
Full LLMOps/MLOps operation, strict SLAs, dedicated on-call engineers, retraining pipelines, and compliance-grade reporting at scale. Illustrative; varies by scope.
Transparent pricing. No hidden fees. Scope-based quotes.
Questions about pricing? Let's discuss your specific requirements.
Why Choose Opsio for Cloud Services
Many vendors will build a model and hand it over. Few will operate it in production, every hour, with their name on the SLA, and fewer still understand the Indian regulatory context while doing so. Opsio is a cloud and AI operations partner at heart, having run mission-critical workloads on AWS, Azure, and Google Cloud for years, and Managed AI & Support brings that same rigour to machine learning and generative AI. Our follow-the-sun model, anchored by our ISO 27001-certified Bangalore delivery centre and headquarters in Karlstad, Sweden, delivers true 24/7 coverage by engineers rather than an answering service. As an AWS Advanced Tier Services Partner with the AWS Migration & Modernization Competency, a Microsoft Partner, and a Google Cloud Partner, multi-cloud AI is our natural ground. Here is what distinguishes the service.
India-Based 24/7 Operations
Our Bangalore centre, paired with Sweden, gives genuine round-the-clock engineering coverage in your time zone and beyond, not a queue that wakes someone overseas.
Compliance-Aware by Design
We operate with DPDP Act, RBI, and CERT-In expectations in mind: audit-ready logging, access control, and data residency handled deliberately from the start.
Multi-Cloud AI Expertise
We run AI natively on Amazon Bedrock, Azure OpenAI, and Google Vertex AI, fitting your existing stack rather than pushing a single-vendor agenda.
Operations-First Heritage
We come from running production infrastructure under SLAs, so reliability, incident response, and observability are ingrained practices, not later additions.
Rupee Cost Accountability
We own token and GPU spend as a metric, engineering it down with India-region options rather than passing the invoice straight through to you.
Transparent SLAs & Reporting
Defined response targets, uptime commitments, and regular reports keep the engagement measurable, so you always know exactly how your AI is performing.
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
Our onboarding is designed to assume ownership without disturbing what already works. We begin by understanding your AI systems, their business role, and their current health, then instrument them for visibility before altering anything. Only once the system is clearly observable do we start to operate and optimise it. The engagement then settles into a steady rhythm of running, measuring, and improving. The four stages below show how a typical Indian enterprise engagement moves from handover to a continuously improving, compliance-aware operation, with indicative timelines.
Onboarding & Baseline
We map your models, data flows, dependencies, and compliance constraints, capture a performance and cost baseline, and agree SLAs and runbooks. This sets the ground truth for everything that follows. Timeline: weeks 1-3.
Instrument & Observe
We deploy monitoring, tracing, and evaluation tooling across your stack, wiring quality, drift, and rupee cost signals alongside infrastructure metrics with audit-ready logging. Timeline: weeks 2-5, overlapping onboarding.
Operate & Optimize
We run the system under SLA: 24/7 monitoring, incident response, cost optimization, guardrail tuning, and scheduled retraining. Reliability rises and rupee cost falls steadily. Timeline: ongoing from week 4.
Analytics & Improve
We deliver regular AI analytics and performance reviews, surface improvement and compliance opportunities, and feed them back into the optimization loop with your team. Timeline: monthly cadence, continuous.
Key Takeaways
- 24/7 AI Monitoring & Support
- Model Drift & Quality Evaluation
- LLMOps & MLOps Engineering
- Cost Optimization (Token & GPU)
- Retraining & Lifecycle Management
Industries Served by Opsio
Managed AI & Support fits wherever AI has become a dependency rather than an experiment, though priorities differ sharply across Indian sectors. Regulated BFSI workloads weight governance and auditability above all; high-volume consumer platforms obsess over cost-per-request and latency; GCCs and ITeS firms need consistent quality across global service lines. We tune monitoring thresholds, guardrails, evaluation criteria, and reporting to each context. These four sectors show the strongest demand for disciplined AI operations in India.
BFSI & NBFC
Banks, NBFCs, and insurers need AI that is explainable, auditable, and aligned with RBI and DPDP expectations. We operate models with strict guardrails, evaluation logging, and reporting that satisfies risk and compliance while keeping decisioning accurate.
Global Capability Centres
GCCs run AI across functions for global parents and must meet group governance standards. We provide consistent, audit-ready operations with follow-the-sun coverage, so AI services hold quality and compliance across regions and time zones.
IT Services & ITeS
ITeS firms embed AI in client-facing delivery at scale, where cost-per-request and reliability are contractual. We optimise token and GPU spend hard while keeping quality steady across high volumes and many concurrent engagements.
Retail & E-commerce
Recommendation, search, and support AI run at huge volume where rupee cost and latency rival quality in importance. We drive down spend aggressively while keeping conversion-critical models accurate through festival and seasonal demand spikes.
Managed AI & Support for Indian Enterprises: Run AI in Production — FAQ
What is Managed AI / LLMOps?
Managed AI is an ongoing service in which a partner operates your AI systems in production for you, rather than only building them. LLMOps, with MLOps covering traditional machine learning, is the operational discipline that makes it work: versioning prompts and models, monitoring quality and cost, evaluating outputs, managing retraining, and handling incidents. Just as DevOps keeps software running, LLMOps keeps model behaviour dependable, which matters because AI can degrade even when its code is untouched. Opsio's Managed AI & Support packages these practices into a single accountable service spanning monitoring, drift evaluation, guardrails, cost control, retraining, and analytics, delivered under defined SLAs by named engineers from our Bangalore centre, with Indian compliance expectations built into how we operate.
How much does Managed AI & Support cost in India?
Pricing is a monthly retainer that varies by scope, since the right figure depends on how many models you run, your traffic, the SLA tier required, and whether GPU infrastructure is involved. As illustrative guidance, lighter engagements covering monitoring and support for a few models begin in the lower lakhs of rupees per month, while comprehensive operations spanning many models, strict SLAs, and active cost and retraining work cost more. The service frequently funds part of itself: by right-sizing models, eliminating wasteful token and GPU usage, and routing efficiently, often through India-region endpoints, we typically cut AI spend meaningfully. We scope each engagement individually and confirm a clear, fixed monthly figure in INR before you commit.
What SLAs do you offer?
We offer tiered SLAs matched to how critical your AI is to the business. Our standard operational commitment targets 99.9% uptime, with a sub-15-minute response target for critical incidents and wider windows for lower-severity issues. SLAs cover response and restoration times, monitoring coverage, and reporting cadence, backed by our 24/7 follow-the-sun NOC across Bangalore and Sweden so coverage is real at every hour, including Indian business time. Higher tiers add tighter response targets, dedicated on-call engineers, and more frequent reviews. Each SLA is documented in the engagement agreement with clear severity definitions and escalation paths, so expectations are explicit, and our performance against them is reported to you on a regular schedule.
How do you detect model drift?
We treat model behaviour as a monitored signal rather than a black box. In practice we continuously track the statistical distribution of inputs and outputs, automated quality scores on sampled live outputs (often via evaluation suites and LLM-as-judge methods), and, where ground truth becomes available, accuracy against it. When any metric crosses an agreed threshold, an alert fires. For generative systems we additionally score groundedness and hallucination rates against source data. This catches the silent decay that ordinary infrastructure monitoring misses, because a model can return fast, error-free responses while quietly losing accuracy, a real risk where regulated decisions are involved. Detection then triggers a defined response: review, guardrail adjustment, or retraining, depending on the cause and severity, with the whole chain logged for audit.
What AI analytics do I get?
You receive dashboards and regular reports across four areas. Model performance: accuracy, quality scores, drift indicators, and evaluation results over time. Usage analytics: request volumes, patterns, leading use cases, and adoption trends. Cost analytics: token and GPU spend in rupees, broken down by model, feature, and team, with trends and the impact of optimization. Business-performance insight: where we can connect AI activity to outcomes you track, such as resolution rates or conversion. The goal is to give leadership a clear view of what AI actually delivers and give engineers the signals to prioritise. Reports arrive monthly by default, live dashboards run continuously, and deeper reviews happen at agreed intervals, with formats suitable for sharing with risk and compliance stakeholders.
How often do you retrain models?
There is no universal cadence; it depends on how quickly your data and environment shift. We run two complementary approaches. Scheduled retraining follows a fixed cycle, commonly monthly or quarterly, to keep models aligned with recent data. Trigger-based retraining fires when our drift monitoring shows quality has crossed an agreed threshold, so a model is refreshed when it genuinely needs it rather than on an arbitrary date. Every retrained model is validated against your evaluation suite and compared with the incumbent before promotion, and we keep rollback paths ready. Stable models may need infrequent retraining; fast-moving ones in dynamic Indian markets warrant tighter cycles. We agree the policy with you during onboarding and keep the full lineage logged for audit.
How do you handle DPDP, RBI, and data residency?
Compliance is part of the operating model, not an afterthought. We work within the DPDP Act 2023, RBI expectations for regulated entities, and CERT-In directives, applying audit-ready logging, role-based access control, and deliberate data-residency choices, including India-region cloud endpoints where required. Our delivery centre is ISO 27001-certified, and guardrails, output filtering, and evaluation gates are built into the request path so policy is enforced continuously rather than reviewed occasionally. Every model and prompt change is traceable, giving risk, compliance, and security teams the evidence to defend AI use to boards and regulators. We design the data-handling and logging approach with your compliance function during onboarding, so AI operations meet your obligations from day one rather than being reconciled later.
Which clouds and AI platforms do you support?
We are multi-cloud by design and operate AI natively across the three major platforms: Amazon Bedrock on AWS, Azure OpenAI Service on Microsoft Azure, and Google Vertex AI on Google Cloud, including their India regions for residency and latency. We also operate open-weight and custom models on dedicated GPU infrastructure or managed endpoints. For observability and operations we use proven tooling such as Datadog for monitoring, MLflow for experiment tracking and model registry, and LangSmith for LLM tracing and evaluation, integrating with what you already run instead of forcing a replacement. As an AWS Advanced Tier Services Partner, Microsoft Partner, and Google Cloud Partner, we can operate your AI wherever it lives, including hybrid deployments spanning more than one cloud.
Still have questions? Our team is ready to help.
Keep your production AI reliable, governed, and cost-efficient
Talk to Opsio India about operating your deployed models under 24/7 managed support from Bangalore, with drift evaluation, INR cost optimization, retraining, and AI analytics under firm SLAs.
Managed AI & Support for Indian Enterprises: Run AI in Production
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