Solving Today's Business Challenges with MLOps
Choose the Right Model Architecture for the Task
Choose the Right Model Architecture for the Task
Eliminate uncertainty in selecting the optimal model architecture for your AI initiatives. We help evaluate and benchmark LLMs, open-weight models, and proprietary APIs to ensure performance, cost-efficiency, and alignment with domain-specific use cases.
Control Inference Costs with Smart Engineering
Navigate the LLMOps and AgentOps Ecosystem
Select Tools That Are Built to Last
Align AI with Business Value
Build MLOps Systems That Stand Up in Production
Optimize Performance Without Blowing Your Budget
Ensure Governance from Day One
Choose the Right Model Architecture for the Task
Eliminate uncertainty in selecting the optimal model architecture for your AI initiatives. We help evaluate and benchmark LLMs, open-weight models, and proprietary APIs to ensure performance, cost-efficiency, and alignment with domain-specific use cases.
Control Inference Costs with Smart Engineering
Keep your token budgets predictable and sustainable. Our strategies for prompt design, hybrid architectures, and model optimization reduce API overuse and stabilize operational costs – especially for high-volume GenAI workloads.
Navigate the LLMOps and AgentOps Ecosystem
Ensure your business remains updated with the latest GenAI tooling landscape. We guide you through stable frameworks and orchestration tools like LangChain or n8n, helping you avoid lock-in and keep your GenAI stack modular and future-ready.
Select Tools That Are Built to Last
With hands-on experience across industries, we help you choose MLOps tools and platforms with proven reliability, mature ecosystems, and sustainable roadmaps that support long-term success.
Align AI with Business Value
Not every challenge needs a model. We work with stakeholders to translate business goals into AI use cases, ensuring data readiness and solution fit – or identifying where AI creates real impact and advising alternatives when traditional solutions are more effective.
Build MLOps Systems That Stand Up in Production
Move beyond experiments and design end-to-end MLOps pipelines featuring version control, automated validation, CI/CD for ML workflows, and resilient rollback mechanisms. This enables your models to stay reliable, monitored, and production-grade over time.
Optimize Performance Without Blowing Your Budget
Deploy smarter with infrastructure-aware design. We help simulate and compare cloud-native, edge, GPU, or hybrid strategies to find the best performance-to-cost balance for your AI workloads.
Ensure Governance from Day One
Stay compliant from the start. We build privacy-aware training pipelines, enforce data policies, and support regulatory frameworks like GDPR and HIPAA—ensuring trust, transparency, and auditability across your ML lifecycle.
Our clients
Our Approach
Discovery & Problem Framing
We start by grounding ML strategy into real business needs. From clarifying what success looks like to identifying impactful use cases, we ensure your MLOps journey begins with a focused strategy, eliminating technical uncertainty.
Data Assessment & Architecture Planning
Next, we assess your existing data streams, integrations, and metadata structure. This step lays the groundwork for scalable, ML-ready pipelines by mapping requirements and planning your ontology and data architecture.
Defining Model Strategy & Prototyping
We help select the right AI/ML approach based on performance, cost, and domain fit. Then we design a proof of concept that tests feasibility fast – minimizing risk and validating the model’s potential in real-world conditions.
Laying MLOps Foundations
We turn experimentation into repeatable engineering. This includes setting up version-controlled pipelines, metadata frameworks, and CI/CD for ML – all built for auditability, reliability, and long-term maintainability.
Training, Tuning & Validation
We build, tune, and validate your models in context. By running controlled evaluations of your taxonomy, ontology, and model setup, we ensure accuracy, fairness, and fitness for production deployment.
Deploying into Live Environments
Once proven, we help transition the solution into your ecosystem. From refining data streams to integrating ontology updates, we ensure a smooth handoff from pilot to scalable production architecture.
Creating Feedback Loops & Scaling with Confidence
Post-deployment, we embed monitoring, observability, and iteration loops. This enables proactive improvement, supports new feature releases, and helps scale your ML systems without compromising performance, cost, or compliance.
Discovery & Problem Framing
We start by grounding ML strategy into real business needs. From clarifying what success looks like to identifying impactful use cases, we ensure your MLOps journey begins with a focused strategy, eliminating technical uncertainty.
Data Assessment & Architecture Planning
Next, we assess your existing data streams, integrations, and metadata structure. This step lays the groundwork for scalable, ML-ready pipelines by mapping requirements and planning your ontology and data architecture.
Defining Model Strategy & Prototyping
We help select the right AI/ML approach based on performance, cost, and domain fit. Then we design a proof of concept that tests feasibility fast – minimizing risk and validating the model’s potential in real-world conditions.
Laying MLOps Foundations
We turn experimentation into repeatable engineering. This includes setting up version-controlled pipelines, metadata frameworks, and CI/CD for ML – all built for auditability, reliability, and long-term maintainability.
Training, Tuning & Validation
We build, tune, and validate your models in context. By running controlled evaluations of your taxonomy, ontology, and model setup, we ensure accuracy, fairness, and fitness for production deployment.
Deploying into Live Environments
Once proven, we help transition the solution into your ecosystem. From refining data streams to integrating ontology updates, we ensure a smooth handoff from pilot to scalable production architecture.
Creating Feedback Loops & Scaling with Confidence
Post-deployment, we embed monitoring, observability, and iteration loops. This enables proactive improvement, supports new feature releases, and helps scale your ML systems without compromising performance, cost, or compliance.
Industries We Serve
Banking & Financial Services
Banking & Financial Services
Operationalize AI for fraud detection, credit scoring, and customer segmentation – while meeting strict regulatory requirements. Our MLOps in banking and insurance ensure secure, explainable, and auditable ML models, helping financial institutions deliver faster decisions with reduced risk and maximum ROI.
Healthcare & Life Sciences
Healthcare & Life Sciences
Operationalize AI for diagnostics, patient risk scoring, and treatment planning – without compromising compliance. Our MLOps services for healthcare ensure secure model deployment, performance monitoring, and regulatory alignment, helping teams deliver smarter care through reliable, explainable AI.
Hi-Tech Services
Hi-Tech Services
Speed up innovation with continuous ML integration across products and platforms. We help tech teams build, test, and deploy GenAI, LLMs, and predictive models at scale. With CI/CD pipelines, LLMOps, and governance baked in, our MLOps for tech companies accelerates time-to-market and ensures system stability.
Retail & Consumer Goods
Retail & Consumer Goods
Deliver hyper-personalized experiences, optimize inventory, and accelerate demand forecasting with production-ready machine learning. Our MLOps solutions for retail enable scalable model deployment for recommendation engines, pricing algorithms, and customer behavior prediction – while ensuring data compliance and reducing time-to-value.
Travel & Hospitality
Travel & Hospitality
Predict booking trends, personalize offers, and manage dynamic pricing with confidence. Our MLOps for travel and hospitality enables real-time model performance tracking, adaptive retraining, and scalable deployment – so your AI stays accurate across seasons, regions, and shifting customer behaviors.
Automotive & Manufacturing
Automotive & Manufacturing
From predictive maintenance to supply chain forecasting, our MLOps services for manufacturing and automotive ensure ML models run reliably at scale. We enable real-time monitoring, automated retraining, and compliance-ready deployment – minimizing downtime, maximizing throughput, and driving Industry 4.0 transformation.
Hello World
Why Choose Ciklum for MLOps Services?
Proven MLOps Expertise Across Industries
Our teams have delivered robust, production-grade ML systems across industries – not limited to prototypes or academic exercises. From versioning issues, CI/CD pipeline fragility, and model drift in real environments – we ensure your AI stays stable, compliant, and performance-ready.
Leverage Full-Stack Delivery with Cross-Domain Insight
We provide integrated MLOps teams across machine learning, DevOps, data engineering, and architecture. With delivery experience in fintech, retail, and healthtech, we design scalable systems with automated ML pipelines and deployment workflows built to last.
Drive Impact Beyond AI Models
Our MLOps experts align AI strategy to business goals from day one – avoiding over-engineered models that inflate cost without ROI. Our MLOps consulting ensures every solution is operationally efficient, measurable, and built to reduce time-to-value at scale.
Get Up-to-Date, Vendor-Agnostic Guidance
We bring delivery-tested insights to help you select the right tools, infrastructure, and model architecture – whether cloud-native, hybrid, or open-source. Our support covers LLMOps, inference cost optimization, and scalable deployment without framework lock-in.
What our customers say
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