Service
Traditional automation is rigid and brittle, failing when processes change or exceptions arise.
Rules-based systems like RPA require constant maintenance and cannot handle ambiguity. They break with minor process changes, creating technical debt and operational fragility.
Agentic Process Automation replaces static scripts with adaptive AI workflows that understand intent, reason through exceptions, and complete multi-step tasks autonomously.
This shift delivers:
Move beyond rigid, rules-based RPA. Our development of adaptive, AI-driven workflows delivers concrete operational and financial results.
Deploy autonomous workflows that execute complex, multi-step tasks like invoice processing or customer onboarding end-to-end. This eliminates manual handoffs, reduces cycle times by up to 80%, and reallocates FTEs to higher-value strategic work.
Replace brittle, rules-based automation with systems that dynamically reason, handle exceptions, and learn from new data. Our agents coordinate models, APIs, and databases to navigate unstructured inputs and evolving business conditions without breaking.
Leverage our proven frameworks and expertise in agentic workflow orchestration to go from concept to production in weeks, not months. We deliver fully integrated, secure solutions tailored to your specific operational domain.
Build with confidence. Every workflow incorporates audit trails, policy-as-code enforcement, and human-in-the-loop controls. Our designs comply with frameworks like the EU AI Act, ensuring your automation is both powerful and responsible.
Unlock automation for core business processes trapped in legacy ERP, CRM, or mainframe systems. We engineer custom API bridges and data connectors, enabling agentic intelligence to act on your existing data without costly platform replacements.
Achieve sustained ROI with ongoing performance monitoring and iterative refinement. We provide services for AI agent performance tuning, cost optimization, and workflow adaptation to ensure your automation evolves with your business needs.
A transparent breakdown of our phased development approach for building adaptive, AI-driven workflows that replace rigid RPA.
| Phase & Key Deliverables | Weeks 1-4: Discovery & Design | Weeks 5-12: Core Development | Weeks 13-16: Pilot & Deployment |
|---|---|---|---|
Process Mapping & Feasibility Analysis | |||
Agentic Architecture & Tool Integration Plan | |||
Core Autonomous Workflow Engine | |||
Integration with Legacy Systems (APIs, DBs) | |||
Human-in-the-Loop & Exception Handling | |||
Staging Environment Pilot & Validation | |||
Performance & Security Audit Report | |||
Production Deployment & Knowledge Transfer | |||
Ongoing Support & Optimization SLA | Optional | Optional |
Our agentic process automation solutions deliver measurable business outcomes by replacing brittle, rules-based RPA with adaptive intelligence. See how we engineer autonomous workflows for specific operational challenges.
Enabling Efficiency, Speed & Accuracy
We build AI systems for teams that need search across company data, workflow automation across tools, or AI features inside products and internal software.
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Give teams answers from docs, tickets, runbooks, and product data with sources and permissions.
Useful when people spend too long searching or get different answers from different systems.
Use AI to route work, draft outputs, trigger actions, and keep approvals and logs in place.
Useful when repetitive work moves across multiple tools and teams.
Build assistants, guided actions, or decision support into the software your team or customers already use.
Useful when AI needs to be part of the product, not a separate tool.
Get specific answers on timelines, costs, and technical approach for deploying adaptive AI workflows that replace traditional RPA.
Standard deployments take 2-4 weeks for a defined, single-process workflow (e.g., invoice processing). Complex, multi-departmental automations (e.g., customer onboarding) typically require 6-8 weeks for full integration, testing, and deployment. We use a phased sprint methodology, delivering a functional prototype within the first two weeks.
About the author
CEO & MD, Inference Systems
Prasad Kumkar is the CEO & MD of Inference Systems and writes about AI systems architecture, LLM infrastructure, model serving, evaluation, and production deployment. Over 5+ years, he has worked across computer vision models, L5 autonomous vehicle systems, and LLM research, with a focus on taking complex AI ideas into real-world engineering systems.
His work and writing cover AI systems, large language models, AI agents, multimodal systems, autonomous systems, inference optimization, RAG, evaluation, and production AI engineering.
How We Work
One-fit-all AI don't work for modern businesses. At Inferensys, we aim to understand your business & custom requirements; which we use to define most efficient agentic workflows, the data, and the tools for your business.
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We understand the task, the users, and where AI can actually help.
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We define what needs search, automation, or product integration.
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We implement the part that proves the value first.
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We add the checks and visibility needed to keep it useful.
Read moreThe first call is a practical review of your use case and the right next step.
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