Enabling AI
DataOps, MLOps & AgentOps
Most enterprises face the same challenge: vast amounts of data, yet limited activation. Only a fraction of enterprise data is reliably used to inform real-time decisions or power AI.
At NowVertical, we operationalise your data end to end — supporting enabling effective dataflow, and seamless deployment of ML Models and AI Agents.
Data is Plentiful. Value is Not.
DataOps, MLOps, and AgentOps disciplines can be the missing link between raw data and enterprises being able to maximise their data and AI ROI.
Siloed systems block data visibility
Manual ETL slows delivery
Data quality is inconsistent
No pipeline performance tracking
DataOps
Unified pipelines break silos
Real-time data feeds decisioning
Automated testing improves trust
Lineage boosts traceability
Models stall before deployment
Limited version control or audits
Model drift goes unnoticed
Poor monitoring post-launch
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MLOps
CI/CD speeds up deployment
Full model traceability ensured
Live tracking and alerts
Quality checks built in
Agents lack system context
Outputs aren’t trusted by teams
Model drift goes unnoticed
Risky actions without guardrails
AgentOps
Agents access secure real-time data
Outputs are explainable
Workflows are fully integrated
Policy enforcement built in
We deliver the missing link to unlock data & AI ROI
40%
increase in model deployment frequency
30%
reduction in operating costs associated with data
Drive consistent value
Reduce variability and increase accuracy in data and AI-driven decisions.
Scale AI with confidence
Deploy and govern models at enterprise scale — securely and reliably.
Boost productivity
Automate repetitive data tasks and free up time for high-value innovation.
Maximise return on data
Ensure every data asset contributes to measurable business impact.
We Operationalise Your Data for Scale and Speed
Our DataOps methodologies transform how data moves, scales, and delivers business impact — from ingestion to orchestration.
Scale & Flexibility
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Cost Efficiency
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Cost EfficieEnhanced Data Security & Compliancency
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Improved Data Quality & Reliability
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Data Pipeline Performance
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Automated Testing
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Automated Deployment
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Monitoring
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Real-time Data Processing & Insights
We Turn Machine Learning into a Measurable Business Impact
Organisations often invest heavily in ML but struggle to translate that into production-ready systems. NowVertical’s MLOps approach operationalises ML at scale.
Collect, clean, and label data to train machine learning models effectively and ensure representativeness.
Develop and experiment with ML models that are optimised for performance, accuracy, and generalisability.
Write and refactor code for machine learning pipelines, model logic, and application interfaces.
Test models, code, and data quality through validation checks, unit testing, and reproducibility controls.
Containerise models and workflows for seamless deployment across environments using tools like Docker or MLflow.
Define deployment objectives, infrastructure requirements, and governance policies to support production ML.
Launch new ML model versions into staging or production environments with version control and rollback plans.
Track model drift, system health, and data changes to maintain model performance and trigger retraining as needed.
Track model drift, system health, and data changes to maintain model performance and trigger retraining as needed.
Deploy, manage, and optimise LLMs and AI agents
From copilots and RAG pipelines to task-specific decisioning bots—across enterprise environments.
Operationalise AI agents in real-world settings, ensuring they’re useful, safe, and aligned with business goals.
Govern agent behaviour, decision pathways, and escalation logic.
Continuously monitor, retrain, or retire agents based on performance, context shifts, or evolving policies.
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OpenAI
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Anthropic Claude
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Microsoft Copilot (365)
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Google Vertex AI Agents
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Amazon Bedrock + Agents
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LangChain / LangGraph
Deploy, fine-tune, and monitor GPT-based copilots and decision agents. Implement rate limits, content filtering, and enterprise tooling (e.g. Azure AI Studio).
Leverage Claude’s long context windows and safety alignment. Use wrappers to manage agent actions and escalation logic.
Govern usage, prompts, and access at org-level. Integrate telemetry and productivity feedback loops to enhance agent ROI.
Create multi-modal enterprise copilots. AgentOps includes tracking model usage, handling tool integrations, and data routing in Google Cloud.
Create multi-modal enterprise copilots. AgentOps includes tracking model usage, handling tool integrations, and data routing in Google Cloud.
Orchestrate multi-step agent chains with memory, tools, and control flows. Use observability modules to track and debug agent runs.
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Make Data work for you.
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