Building an AI model is 20% of the work. Getting it into production reliably, securely, and at scale is the other 80% — and where most enterprise AI projects stall. We handle that 80% with proven MLOps infrastructure, automated pipelines, and 24/7 monitoring.
Faster than traditional IT deployment timelines
Uptime SLA on all production AI systems
Average reduction in inference cost
Every component of a robust production AI stack — from CI/CD pipelines to cost optimisation — built and maintained by our certified MLOps engineers.
Automated testing, versioning, and deployment pipelines that treat AI models and prompts as first-class software artefacts — with rollback, canary releases, and zero-downtime deploys.
Infrastructure-as-code provisioning on AWS, Azure, or GCP — including compute, networking, storage, and security — configured specifically for AI workloads and your compliance requirements.
Real-time dashboards and statistical monitors that detect performance degradation, data drift, and model drift before they impact business outcomes — with automated alerting to your team.
High-availability AI endpoints built to handle millions of requests with consistent sub-200ms latency — load balanced, rate-limited, and protected against abuse and misuse.
Systematic reduction of AI inference costs through caching strategies, model routing, prompt compression, and batching — without compromising accuracy or response quality.
Zero-downtime deployments with automatic fallback to the last stable version if a new deployment degrades performance — and active-passive failover for critical systems requiring maximum availability.
The difference between a 6-week and a 9-month deployment isn't effort — it's methodology. Here's how our approach compares.
💡 Every extra month in pre-production costs you: inference cost savings you could already be capturing, competitive advantage, and the opportunity cost of 4–6 engineers blocked on infrastructure rather than building features.
Speed in deployment doesn't mean skipping steps — it means having the right infrastructure templates, tooling, and expertise ready before Day 1. We've deployed 150+ AI systems and extracted every reusable component into battle-tested templates that compress weeks of work into days.
Of infrastructure provisioned from IaC templates — not from scratch
CI/CD pipeline operational from the first day of engagement
Infra, security, and integration workstreams run simultaneously
Ticket queues — your dedicated engineer answers directly
Click any deployment type to see the reference architecture we use and the specific technology decisions at each layer.
Web app · Mobile · Internal tool · Third-party system
AWS API GW · Kong · Auth · Rate limiting · Logging
LangChain / LlamaIndex · Query routing · Caching
Pinecone / pgvector · Semantic search · Reranking
Claude / GPT-4o · Fallback routing · Cost optimisation
Grafana · Prometheus · MLflow · PagerDuty alerts
Multi-AZ deployment with automatic failover. If any component fails, traffic routes to healthy instances within seconds. Tested monthly with chaos engineering scenarios.
Semantic caching serves repeated query types without hitting the LLM API. Model routing sends simple queries to cheaper models. Average 30–60% cost reduction vs naive deployment.
All data in transit encrypted with TLS 1.3. Data at rest encrypted with AES-256. Prompt injection detection layer. PII scrubbing before any data leaves your VPC.
Horizontal auto-scaling on the orchestration and API layers. Vector database sharding for large corpora. Handles from 100 to 10M+ daily requests with the same architecture.
AI inference costs can spiral quickly at scale. We apply four proven optimisation techniques that reduce your monthly API and compute spend without compromising quality.
Similar queries return cached responses without hitting the LLM API. Typical enterprise systems have 30–50% query similarity — each cached response saves the full API cost.
Route simple queries to smaller, cheaper models (GPT-3.5, Claude Haiku, Mistral 7B) and reserve expensive models (GPT-4o, Claude Sonnet) only for complex tasks that need them.
Automatically compress lengthy system prompts and conversation history using LLMLingua and context trimming — reducing token consumption by 20–40% with minimal quality impact.
✓ We guarantee a minimum 30% inference cost reduction within 90 days of deployment — or we continue optimising at no extra charge.
Production AI fails silently if you're not watching the right signals. Our monitoring stack catches degradation within minutes — not days.
Grafana dashboards tracking latency, throughput, error rate, accuracy, and cost in real time. Custom KPIs specific to your use case — not just infrastructure metrics.
Multi-level alerting: P1 incidents wake your on-call team immediately, P2 sends Slack/Teams messages, P3 generates a ticket. Alert fatigue prevented with smart grouping and suppression rules.
Statistical tests run continuously on input distributions and output quality. Drift triggers automated alerts and, optionally, automated retraining pipelines before users see degraded results.
Automated monthly reports covering uptime vs SLA, incident timeline, performance trends, cost vs budget, and recommendations for the next 30 days. Delivered to your inbox on the 1st of each month.
● All systems nominal · Latency P99 at 187ms · Within SLA
2m ago● Auto-scaling triggered · 3 → 5 instances · Incoming spike from Marketing campaign
24m ago● Input drift detected on doc-processor · KL-divergence 0.12 → monitoring · No action yet
47m ago● Cache hit rate 68% → saving ₹14,200 in API costs today vs uncached baseline
1h ago● Deployment v2.4.1 promoted to production · 100% canary success · Rollback window active
2h agoWhether you have a working model and need production infrastructure, or you need us to build and deploy end-to-end, the same four-phase process applies.
Audit your existing infrastructure, compliance requirements, and integration points. Define the target architecture, cloud platform, and security controls. All IaC templates provisioned.
CI/CD pipeline operational from Day 3. Cloud infrastructure provisioned via IaC. Staging environment configured. Security hardening applied. Integration tests written and passing.
Full load testing, security penetration testing, failover testing, and performance benchmarking in staging. Cost optimisation implemented. Monitoring dashboards configured and alerts verified.
Canary deployment to 5% of traffic, then full promotion. 90-day monitoring SLA begins. Knowledge transfer to your team. Runbooks and incident response playbooks delivered.
Real outcomes from enterprise AI deployment and MLOps engagements across every major industry.
Clinical documentation RAG system deployed across 12 hospital sites. Multi-AZ architecture with on-premise data residency. 99.99% uptime in first 6 months. Zero PHI data egress incidents.
Fraud detection ML pipeline serving 10M+ daily transactions. Semantic caching + model routing reduced monthly inference cost from ₹8.2L to ₹3.1L while maintaining 99.2% detection accuracy.
Predictive maintenance ML system across 50 factories. Automated retraining pipeline updates models weekly with new sensor data. Deploy cycle reduced from 3 weeks to 4 hours with full CI/CD.
Questions from CTOs, DevOps leads, and engineering managers evaluating MLOps and rapid deployment services.
MLOps (Machine Learning Operations) is the set of practices, tools, and infrastructure that enables organisations to deploy, monitor, maintain, and continuously improve AI models in production. Without MLOps, AI models that perform well in development frequently degrade in production due to data drift, infrastructure failures, or poorly managed updates. Enterprise AI needs MLOps for the same reason software engineering needs DevOps: to make the path from development to production reliable, fast, and repeatable at scale.
With our rapid deployment service, most enterprise AI systems move from a tested model to a live production environment within 4–6 weeks. This includes infrastructure provisioning, CI/CD pipeline setup, integration testing, security hardening, monitoring configuration, and canary go-live. Compared to traditional IT timelines of 4–9 months, we achieve 3× speed through pre-built IaC templates, parallel workstreams (infrastructure and security run simultaneously rather than sequentially), and dedicated engineers with no ticket queue overhead.
We deploy on AWS (SageMaker, ECS, Lambda, Bedrock), Microsoft Azure (Azure ML, AKS, Azure OpenAI), and Google Cloud Platform (Vertex AI, Cloud Run, GKE). We are cloud-agnostic and select the platform based on your existing infrastructure, compliance requirements, data residency needs, and cost profile. We also support private cloud and on-premise deployments for regulated industries requiring full data sovereignty — using open-source models served locally with Ollama, vLLM, or TGI.
We provide a 99.9% uptime SLA for all production AI systems deployed through our service. This is achieved through multi-availability-zone infrastructure, automatic failover configurations, load balancing, health checks with auto-restart, and 24/7 monitoring with PagerDuty-integrated alerting. For critical systems requiring 99.99% uptime, we offer enhanced architecture with active-active multi-region deployment. We have maintained a 99.97% average uptime across all client deployments over the past 12 months.
Model drift occurs when an AI model's performance degrades because real-world data diverges from training data. Data drift happens when input distributions change (e.g., customers start phrasing requests differently). Concept drift happens when the correct output for a given input changes over time. We detect both using statistical monitoring of input distributions, continuous tracking of output quality metrics against ground truth samples, and automated alerting when performance drops below defined thresholds. Once detected, we can trigger automated retraining pipelines or escalate to your team, depending on your configured response policy.
Yes — deployment-only engagements are one of our most common use cases. Many clients have a working model built in-house or by a third party and need production infrastructure, CI/CD pipelines, monitoring, and cost optimisation. We assess your existing model, design the production architecture, and deploy to your preferred cloud environment. We'll also evaluate and recommend any performance or cost improvements to the model itself if we identify opportunities during the infrastructure review.
We apply four systematic optimisation techniques to every deployment: semantic response caching (eliminates 30–50% of API calls for similar queries), intelligent model routing (routes simple queries to cheaper models), prompt compression (reduces token consumption by 20–40%), and request batching (reduces per-call overhead). The exact savings depend on your query distribution, but we have achieved a minimum of 30% reduction on every engagement to date. If we don't achieve 30% within 90 days of deployment, we continue optimising at no additional charge until we do.
Every production deployment includes a written incident response runbook covering P1, P2, and P3 scenarios. P1 incidents (full outage or critical accuracy failure) trigger immediate PagerDuty alerts to both our on-call engineer and your designated contact, with a 15-minute response SLA. Most P1 incidents are resolved via automatic failover or one-click rollback within minutes. P2 incidents (degraded performance) trigger Slack/Teams notifications with a 1-hour response SLA. All incidents are documented in a post-mortem report within 48 hours.
Enterprises that start today have a 12–18 month head start on competitors still evaluating options. Here's exactly what happens when you reach out:
Speak directly with a senior AI architect — not a salesperson. No commitment required.
Within 48 hours we'll send your top 3 AI opportunities with rough ROI estimates — in writing.
If you proceed, engineers can be active on your project within 2 weeks of contract signing.
We respond within 4 business hours.
Your information is protected and never shared.