"Agile Infoways team delivered exceptional iOS and Android apps with responsive support and outstanding problem-solving expertise."
- Rob Machado
AI EngineeringAI-Powered Process Automation
AI Automation services with intelligent AI tools
Drive smarter operations with our AI automation services, designed to help businesses reduce manual work, improve workflows, and make faster decisions. As an AI automation company, we build intelligent solutions using AI agents, machine learning, RAG, APIs, and cloud platforms to automate complex processes. Hire AI engineers to create secure, scalable automation systems that connect your data, apps, and teams while improving efficiency and business outcomes. Our AI capabilities help leaders solve everyday operational challenges.
Automation Capabilities
AI Process Automation
From intelligent document processing to end-to-end workflow orchestration, we deliver business process automation and AI automation services that reduce manual work without sacrificing control.
Intelligent Document Processing
Extract, classify, and validate data from invoices, contracts, forms, and emails with 99%+ accuracy using vision AI and NLP.
End-to-End Workflow Orchestration
Design AI-native workflows that route, approve, transform, and escalate — integrating with your existing ERP, CRM, and ITSM systems.
Decision Automation
Replace manual decision points with AI models trained on your historical data — credit approvals, risk scoring, content moderation at scale.
Process Mining & Discovery
Analyze event logs to discover actual process flows, identify bottlenecks, and pinpoint the highest-ROI automation opportunities.
RPA + AI Hybrid
Supercharge existing RPA bots (UiPath, Automation Anywhere) with AI perception and judgment for handling unstructured inputs.
Exception Handling & Escalation
Smart exception detection that routes edge cases to humans with full context, learns from decisions, and improves handling over time.
Why Agile Infoways for AI Automation
We've automated processes for Fortune 500 companies delivering 60–90% cost reduction with measurable SLA guarantees.
Our Capability
Automation Tech Stack
Production-proven tools for intelligent process automation, agentic automation, orchestration, and decisioning at enterprise scale.
Azure Document Intelligence
Enterprise-grade IDP for invoices, forms, contracts, and custom document types.
Apache Airflow / Prefect
Orchestration engines for complex multi-step AI workflows with retry and monitoring.
UiPath + AI Center
RPA platform enhanced with AI models for unstructured data handling.
Celonis Process Mining
Data-driven process discovery to identify automation opportunities with quantified impact.
Temporal.io
Durable workflow engine ensuring automation steps complete reliably despite failures.
MLflow / BentoML
ML model management and serving infrastructure for decision automation at scale.
Our ApproachHow We Build
How We Build
AI Automation
From process discovery through live deployment with measurable ROI at each stage.
01
Process Discovery & ROI Analysis
02
AI Model Development
03
Workflow Integration & Testing
04
Go Live & Continuous Improvement
Step 01
Process Discovery & ROI Analysis
Map current-state processes using event logs and stakeholder interviews. Quantify time, cost, and error rates to rank automation opportunities by ROI.
Process mapsROI ranking matrixQuick-win identificationBusiness case
Step 02
AI Model Development
Train or fine-tune models specific to your documents and decision patterns. Validate accuracy against your historical data before any automation goes live.
Custom AI modelsAccuracy benchmarksEdge case catalogValidation reports
Step 03
Workflow Integration & Testing
Wire AI models into your existing systems, configure exception routing, and run parallel testing alongside manual processes to prove accuracy.
System integrationsParallel testingException playbooksUAT sign-off
Step 04
Go Live & Continuous Improvement
Staged rollout with real-time monitoring dashboards. Monthly model retraining on new data to keep accuracy high as your processes evolve.
Staged rolloutReal-time dashboardsMonthly retrainingSLA reporting
Our ApproachHow We Build
How We Build
AI Automation
From process discovery through live deployment with measurable ROI at each stage.
Step 01
Process Discovery & ROI Analysis
Map current-state processes using event logs and stakeholder interviews. Quantify time, cost, and error rates to rank automation opportunities by ROI.
Process mapsROI ranking matrixQuick-win identificationBusiness case
Step 02
AI Model Development
Train or fine-tune models specific to your documents and decision patterns. Validate accuracy against your historical data before any automation goes live.
Custom AI modelsAccuracy benchmarksEdge case catalogValidation reports
Step 03
Workflow Integration & Testing
Wire AI models into your existing systems, configure exception routing, and run parallel testing alongside manual processes to prove accuracy.
System integrationsParallel testingException playbooksUAT sign-off
Step 04
Go Live & Continuous Improvement
Staged rollout with real-time monitoring dashboards. Monthly model retraining on new data to keep accuracy high as your processes evolve.
Staged rolloutReal-time dashboardsMonthly retrainingSLA reporting
Use Cases
Automation in Action
Real AI automation deployments with documented cost savings and accuracy gains.
FI
FinanceAccounts Payable Automation
The Challenge
Finance team manually processing 5,000 invoices monthly across 12 formats with 8% error rate and 5-day cycle time.
The Outcome
AI automation processes 94% of invoices touchlessly, reducing cycle time to 4 hours and errors to under 0.3%.
Document AISAP integrationException routingAudit trail
IN
InsuranceClaims Triage Automation
The Challenge
Claims adjusters spending 40% of time on data entry and routing instead of actual claims assessment.
The Outcome
AI triage system auto-classifies and routes 80% of claims, cutting average handle time from 45 to 12 minutes.
NLP classificationImageAISalesforcePriority scoring
HR
HRResume Screening & Scheduling
The Challenge
Recruiters manually screening 500+ applications per role, taking 3 weeks to reach first interviews.
The Outcome
AI screening shortlists top candidates and auto-schedules interviews, compressing time-to-interview from 21 to 4 days.
NLP matchingCalendar APIATS integrationBias detection
MA
ManufacturingQuality Control Automation
The Challenge
Manual visual inspection catching only 78% of defects, with 3 inspectors per production line.
The Outcome
Computer vision system detects 99.2% of defects in real-time, reducing QC cost by 60% and recalls by 85%.
Computer visionEdge inferencePLC integrationDefect classification
Industries
AI Built for Every Industry
Every industry re-imagined with our enterprise AI services. We are reinventing every industry and creating a unique solution that moves our clients forward and disrupts the market.
Before You Build
Evaluating AI Automation
What teams ask before automating workflows — what AI automation is, what to automate, ROI, integration, and control.
8 questions answered
01
What is AI automation, and how is it different from RPA?
AI automation uses machine learning to understand context, read unstructured data, and make decisions — not just follow fixed rules like RPA. RPA clicks through predictable steps and breaks on exceptions; AI automation adapts, handles ambiguity, and improves over time. The two work best together, often coordinated by agentic AI that decides what to do.
02
Which processes should we automate first?
Automate the processes that are high-volume, repetitive, rule-heavy but exception-prone, and costly when done manually — invoice processing, claims triage, data entry, and document review are common starting points. We use process mining to quantify time and error costs, then prioritize by ROI. Higher-judgment steps get decision intelligence rather than blind automation.
03
How does AI automation handle documents and unstructured data?
AI automation reads documents and unstructured data with intelligent document processing: OCR, layout understanding, and language models extract fields from invoices, contracts, emails, and forms — even messy, non-standard ones rules can't parse. Extracted data flows straight into your workflows. The same retrieval techniques behind RAG document intelligence power accurate extraction at scale.
04
What ROI can we expect, and how much does AI automation cost?
AI automation typically cuts processing time and manual cost by 40–80% on the right workflows, with payback often inside a year. Cost is scoped per process — driven by volume, document complexity, and systems to integrate — not a flat license. We start with an ROI analysis, then build. Many teams add dedicated AI/ML engineers to scale.
05
Will AI automation work with our existing systems and RPA tools?
Yes — AI automation connects to your existing ERPs, CRMs, databases, and current RPA bots through APIs and connectors, so you enhance what you have rather than rip it out. AI handles the judgment and exceptions; existing RPA keeps doing the deterministic clicks. We design this through AI integration so automation fits your stack.
06
How do you handle exceptions and keep humans in control?
AI automation routes low-confidence cases and high-stakes decisions to people through human-in-the-loop checkpoints, while handling routine volume autonomously. Confidence thresholds, approval gates, and full audit logs keep humans in control and every action traceable for compliance. This observability and governance is part of the AI infrastructure we build around production automation.
07
What is intelligent process automation (IPA)?
Intelligent process automation is automation that combines workflow logic with AI models that can read documents, classify inputs, make recommendations, and route exceptions with context. It goes beyond macros and scripts by handling variation in real business data. Many teams adopt IPA as part of broader AI solutions for operations, service, and back-office work.
08
What is agentic automation, and how does it go beyond RPA?
Agentic automation is workflow automation where AI systems can plan steps, use tools, react to changing inputs, and decide when to escalate instead of following a fixed script. That makes it more adaptive than classic RPA, which works best on predictable tasks. It is usually most effective when paired with clear process design from AI strategy consulting.
Client Stories
From Vision to Verified Results
Behind every number - 40% faster deliveries, 60% less admin workload, 50% quicker data processing - is a client who trusted us with a real business challenge. These aren't just demos. They're live products, running at scale sustainably, delivering results clients can measure.
"Great company with great management quality developers were really dedicated to get the job done in a timely cost-effective manner."
- Alexandar Salahsour
"They consistently delivers reliable, high-quality development solutions with exceptional communication, value, and trusted partnership."
- Joe Pellegrino, Jordan Pellegrino
Get In Touch
Let's Build Something Remarkable Together
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