From $15 to Under $0.75 Per Run With Agentic AI
An automotive enterprise re-engineered its AI workflows with n8n and specialized agents, reducing operational costs by over 95% and cutting time to insight.
At a Glance
The client needed to reduce AI workflow costs, improve processing speed, and build a more flexible system for frequent and high-volume data analysis.
Industry
Automotive
Function
Data Analytics & Reporting
Solution
Agentic AI Workflow
Engagement
Workflow Re-engineering
The Client
The client is an automotive enterprise that analyzes data consumption patterns, user activity, and other usage metrics to support operational decision-making, optimize resource allocation, and improve platform performance.
The Challenges
Reducing Cost and Processing Time
The existing AI workflow produced accurate outputs but relied on a monolithic execution model, making frequent, high-volume analysis costly and slow.
High Cost Per Run
Each query ran as a single end-to-end operation, with average costs around $15 per run.
Long Processing Times
Individual queries could take up to 35 minutes to complete, delaying access to important insights.
Token Limit Concerns
Processing large volumes of data created token limit constraints, making the existing approach difficult to scale.
Limited Flexibility
The monolithic workflow made it difficult to handle repeated or evolving queries efficiently.
Rising Operational Costs
As usage increased, the cost of processing frequent queries became increasingly difficult to sustain.
Processing Bottlenecks
The existing approach needed restructuring into smaller, focused steps for more efficient execution.
Reduce the Cost and Complexity of Your AI Workflows
Re-engineer monolithic AI workflows into modular systems that improve processing speed, flexibility, and scalability.
The Solution
Re-engineering the Workflow With n8n
Maruti Techlabs re-engineered the existing AI workflow using n8n as the orchestration layer and specialized agents to divide complex processing into focused steps.
Introduced n8n to coordinate the workflow and break end-to-end processing into smaller, manageable steps.
Introduced n8n to coordinate the workflow and break end-to-end processing into smaller, manageable steps.
The Journey
Monolithic to Modular Workflows
Business Outcomes
Smarter Data Processing, Lower Costs, Faster Decisions
95%+ Cost Reduction
Cost per operation dropped from around $15 to under $0.75.
Faster Time to Insight
Processing time decreased from up to 35 minutes to 4–5 minutes.
Improved Scalability
Modular execution reduced bottlenecks and supported frequent, high-volume requests more efficiently.
Greater Flexibility
Smaller workflow steps made it easier to adapt queries and evolve the process as requirements changed.
Faster Data Analysis
Specialized agents handled individual tasks, reducing the need to process every request as one large operation.
Improved Decision-Making
Faster and more accessible insights supported timely, data-driven operational decisions.
Conclusion
The redesigned workflow reduced operational costs and processing time while creating a more flexible approach to high-volume data analysis.
Tech Stack
Build Faster Data Workflows With Specialized AI Agents
Create modular agentic AI workflows that process data efficiently, deliver insights faster, and support high-volume analysis.
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Frequently Asked Questions
The existing platform handled each query as a single end-to-end operation, regardless of complexity or data volume. This resulted in an average cost of around $15 per run and made frequent analysis expensive.
The workflow processed the entire request as a single operation, increasing processing time and raising token-limit concerns when handling larger datasets. It also made individual workflow steps harder to modify or optimize.
n8n served as the orchestration layer, dividing the workflow into smaller, focused steps. This let specialized agents handle tasks such as script refinement, data analysis, formatting, visualization, and output delivery.
Each agent had a defined responsibility within the workflow. The Engineer Agent handled scripts, the Data Analysis Agent processed data, the Formatter Agent structured outputs, the Visualization Agent prepared visual representations, and the Destination Agent delivered the final results.
The cost per operation dropped from around $15 to under $0.75, reducing operational costs by more than 95%. This made the workflow more practical for frequent and high-volume data analysis.