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Intelligent Process Automation

AI Workflow Automation for Enterprise Operations

We combine RPA platforms with large language models to automate the document-intensive, judgment-dependent workflows that traditional automation cannot reach. Our engagements deliver measurable throughput gains, error reduction, and auditable compliance across accounts payable, legal, onboarding, and regulatory reporting functions.

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500+
Enterprise Clients
10,000+
Engineers Deployed
50+
Countries Served
99.4%
CSAT Score
48h
Team Assembly

The Challenge

Manual Workflows Are Constraining Enterprise Scalability

Enterprise operations teams spend billions of hours annually on knowledge work that is repetitive but requires reading unstructured documents - invoices, contracts, correspondence, and reports. Traditional RPA solves only the structured fraction of this problem, leaving the majority of document processing volume dependent on skilled labor that is expensive, error-prone, and difficult to scale during peak periods.

68%
of enterprise process costs tied to manual document handling
4.2x
higher error rate in manual vs automated data entry
$14M
average annual cost of manual processing for mid-market AP teams
9x
faster cycle time achievable with AI-augmented automation

Why QuickHire

Why Enterprises Choose QuickHire

01

RPA Plus LLM Architecture

We extend your existing UiPath or Automation Anywhere deployment with LLM-powered extraction and decision components rather than replacing your bot library. This protects prior automation investment while dramatically expanding the scope of automatable processes.

02

Process Mining - Led Discovery

We use Celonis and UiPath Process Mining to analyze your actual transaction event logs before writing a single line of automation code. This data-driven approach ensures we target processes with the highest genuine ROI potential.

03

Intelligent Document Processing

Our IDP pipelines handle invoices, contracts, onboarding documents, and compliance forms across 50-plus languages and all major file formats with extraction accuracy consistently above 95 percent. Edge cases route to structured human review queues with AI-generated summaries.

04

Embedded ROI Tracking

Every automation we deploy includes instrumentation that captures throughput, exception rates, straight-through processing, and cost per transaction against your pre-automation baseline. ROI dashboards update weekly and feed into your existing BI tools.

05

Regulated - Industry Governance

Our delivery framework aligns with SR 11-7, GDPR, HIPAA, and SOC 2 requirements. We provide model risk documentation, immutable audit logs, and change control procedures that satisfy internal audit and regulatory examination requirements.

06

Continuous Improvement Service

Post-deployment, our managed service team ships model updates and workflow enhancements every 4 to 6 weeks as your document volumes and variants evolve. Exception rates typically decline 30 to 50 percent in the first 90 days of production operation.

Challenges

Common Enterprise Pain Points

01

Unstructured Document Variability

Invoices arrive from thousands of vendors in hundreds of layouts, and contracts vary enormously in structure and language. Traditional RPA templates break when layouts change, generating exception queues that eliminate the expected labor savings. AI-augmented extraction adapts to layout variability without requiring template maintenance for each new vendor or document type.

02

Legacy System Integration Complexity

Enterprise automation programs routinely underestimate the effort required to integrate with SAP, Oracle, and mainframe systems that expose limited or poorly documented APIs. Integration complexity can consume 40 to 60 percent of total project budget when not scoped rigorously. Our discovery phase produces a detailed integration architecture and API inventory before development begins, preventing cost and schedule overruns.

03

Data Quality in Source Systems

AI models inherit the quality of the data they process. Duplicate vendor records, inconsistent cost center coding, and missing master data in ERP systems cause automation failures that are misattributed to the AI. We conduct a structured data quality assessment in discovery and remediate critical issues before automation deployment.

04

Compliance and Auditability Requirements

Regulated industries require that every automated decision be explainable, traceable, and reversible. Generic automation platforms often lack the audit log granularity and access controls that compliance and legal teams require. We build audit trails and exception management interfaces that satisfy internal audit requirements and regulatory examinations from day one.

05

Change Management and Adoption Risk

Automation programs that neglect the human dimension consistently underperform. Employees who fear job displacement resist adoption, work around automated systems, and introduce manual overrides that degrade ROI. Our engagements include structured change management with role redesign workshops and retraining pathways that convert potential resistance into program advocacy.

Our Approach

A Full - Spectrum AI Automation Platform Tailored to Your Process Portfolio

We deliver end-to-end AI workflow automation through a structured methodology that begins with process mining discovery, progresses through iterative bot development and LLM integration, and transitions into a managed service with continuous model improvement. Our platform-agnostic approach works with your existing RPA investment and connects to your ERP, CRM, and document management systems through a secure integration layer.

01
Discovery and Process Intelligence
Process mining analysis, stakeholder workshops, data quality assessment, and ROI business case development establish the automation roadmap before any code is written.
02
Intelligent Document Processing
LLM-powered extraction pipelines handle invoices, contracts, onboarding documents, and compliance forms with adaptive learning that improves accuracy as transaction volumes grow.
03
RPA Plus AI Integration
We embed generative AI decision components and document understanding models into your UiPath or Automation Anywhere bot architecture, extending automation scope without replacing existing bots.
04
Managed Automation Operations
Post-deployment managed services cover 24/7 bot health monitoring, model retraining, exception management, and quarterly business reviews with ROI reporting against your pre-automation baseline.

Delivery Models

How We Deliver

Focused Process Sprint

End-to-end automation of a single high-value process such as invoice processing or contract analysis, from discovery to production deployment with full governance documentation.

Timeline
8-12 weeks
Team Size
3-5 engineers
Program Delivery

Multi-process automation roadmap covering 5 to 10 workflows across business units, delivered in phased sprints with a shared integration layer and enterprise governance model.

Timeline
16-36 weeks
Team Size
6-12 engineers
Platform Build and Operate

Design and deployment of a reusable enterprise automation platform with IDP capabilities, process mining integration, and an internal center of excellence operating model for self-serve automation.

Timeline
20-40 weeks
Team Size
8-15 engineers

Capabilities

Technical Capability Matrix

Intelligent Document Processing
Invoice Data ExtractionContract Clause AnalysisKYC Document VerificationPurchase Order MatchingRemittance Advice Processing
RPA Platforms
UiPath DevelopmentAutomation Anywhere Bot DevelopmentBlue Prism IntegrationPower Automate FlowsBot Orchestrator Configuration
AI and LLM Integration
GPT-4 API IntegrationClaude API IntegrationCustom Fine-Tuned ModelsPrompt EngineeringRetrieval-Augmented Generation
Process and Analytics
Celonis Process MiningUiPath Process MiningAutomation ROI DashboardsException AnalyticsCompliance Audit Reporting

Engagement Models

How We Engage

Choose the model that fits your programme governance, budget cycle, and team structure.

01

Staff Augmentation

Engineers embed directly under your management.

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02

Dedicated Developers

Full-time team aligned to your product roadmap.

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03

Managed Teams

End-to-end delivery with SLA-backed outcomes.

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04

Engineering Pods

Autonomous cross-functional pods per domain.

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05

Offshore Dev Centre

Permanent engineering base in India. Full IP ownership.

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06

Build-Operate-Transfer

We build and run it. You take ownership on schedule.

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Our Process

From Discovery to Delivery

1

Process Mining Discovery

Weeks 1-2

We ingest your ERP and system event logs into a process mining tool to map actual workflow execution paths, identify automation candidates, and build a prioritized ROI-ranked backlog.

2

Architecture and Integration Design

Weeks 2-3

Solution architects produce an integration design, data flow diagram, security architecture, and governance framework tailored to your RPA platform and source systems.

3

Bot and Model Development

Weeks 4-8

RPA developers build orchestrated bot workflows while AI engineers develop and validate LLM extraction components using representative document samples from your production environment.

4

UAT, Governance, and Cutover

Weeks 9-12

Business stakeholders validate automation output against a sample of historical transactions, governance documentation is finalized, and the automation is promoted to production with a parallel-run monitoring period.

5

Managed Operations and Improvement

Ongoing

Our operations team monitors bot health, processes exception queues, retrains models on correction data, and delivers quarterly ROI reviews with recommendations for process expansion.

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Security & Compliance

Enterprise-Grade Security by Default

✓ISO 27001 Certified✓SOC 2 Type II Ready✓GDPR Compliant✓DPDP Act Ready✓NDA on Day 1✓MSA Available✓IP Assignment Clauses✓Escrow Options

Governance

Programme Governance

✓

Model Risk Management

All AI components are documented under SR 11-7 aligned model risk frameworks covering conceptual soundness, validation results, ongoing monitoring thresholds, and escalation procedures.

✓

Immutable Audit Trails

Every automated decision is logged with timestamp, input data hash, model version, confidence score, and outcome in an append-only audit store that satisfies regulatory examination and internal audit requirements.

✓

Change Control and Version Management

All bot and model changes pass through a formal change control process with UAT sign-off, rollback procedures, and versioned deployment records maintained in your existing ITSM system.

✓

Data Residency and Privacy Controls

PII and sensitive financial data are processed within your designated data residency boundary using on-premise or VPC-deployed AI components, with field-level masking for data elements not required by the automation task.

Team Structure

Your Enterprise Team

Our automation delivery teams combine RPA platform specialists, AI and ML engineers, integration architects, and process analysts who have collectively delivered over 200 enterprise automation programs. We embed a dedicated engagement manager who coordinates across your IT, operations, compliance, and business stakeholder groups to maintain program momentum and alignment.

✓Automation Architect
✓UiPath Developer
✓Automation Anywhere Developer
✓AI/ML Engineer
✓Integration Engineer
✓Process Analyst
✓Change Management Lead
✓Engagement Manager

Project Lifecycle

From Kickoff to Production

01
2 weeks

Discovery

Process mining analysis, automation candidate backlog, ROI business case, data quality assessment, integration inventory.

02
1-2 weeks

Design

Solution architecture, integration design, security architecture, governance framework, delivery roadmap.

03
4-8 weeks

Build

RPA bot workflows, LLM extraction components, exception management interface, human-in-the-loop review queues, ROI instrumentation.

04
2-3 weeks

Validate and Deploy

UAT results, governance documentation, parallel-run monitoring report, production deployment, operations runbook.

05
Ongoing

Operate and Improve

Bot health monitoring, model retraining, exception analytics, quarterly ROI reviews, automation expansion recommendations.

Case Studies

Enterprise Outcomes

Financial Services

A regional bank processed 40,000 invoices monthly with a 12-person AP team and a 4.5-day average cycle time.

We deployed an AI-augmented UiPath pipeline with GPT-4 extraction and SAP integration that achieved 94 percent straight-through processing on first deployment.

78%reduction in AP processing cost
Insurance

A specialty insurer needed to review 1,200 commercial contracts per month for non-standard liability clauses before binding.

Our LLM contract analysis pipeline reduced average review time from 3.5 days to under 6 hours per contract with a flagging accuracy rate of 97 percent.

$3.2Mannual legal review cost avoided
Healthcare

A hospital network onboarded 800 new vendors annually, with manual credentialing and compliance checks taking an average of 22 days.

An end-to-end onboarding automation using Automation Anywhere and Azure Document Intelligence reduced credentialing cycle time to 3.5 days.

6.3xfaster vendor onboarding

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Hiring Models

One platform, two ways to hire

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Both models use the same vetted talent network · PM always included · Multi-country billing

Frequently Asked Questions

AI workflow automation combines traditional robotic process automation (RPA) with large language models (LLMs) and machine learning to handle both structured and unstructured data. Traditional RPA executes rule-based tasks on structured inputs with rigid, brittle workflows that break when layouts change. AI-augmented automation can read, interpret, and act on unstructured content such as emails, PDFs, contracts, and handwritten forms. The result is a far more resilient and capable automation layer that handles exceptions intelligently rather than escalating them to human queues.
The highest-ROI candidates are document-intensive, repetitive, and rule-governed processes that currently consume significant skilled labor. Accounts payable invoice processing, vendor contract review, customer onboarding KYC checks, regulatory compliance reporting, HR offer letter generation, and financial period-close reconciliation are all strong targets. These processes share a common trait: they involve extracting structured meaning from unstructured documents and then routing or transforming that data across multiple systems. AI automation delivers the greatest value where human judgment was previously required only to compensate for a lack of machine comprehension.
We extend your existing UiPath or Automation Anywhere deployments by embedding LLM-powered components at decision and extraction points within your existing bots. For UiPath, this involves deploying Document Understanding pipelines enhanced with GPT-4 or Claude-based extractors and connecting them to Action Center for human-in-the-loop validation. For Automation Anywhere, we integrate the IQ Bot platform with custom LLM microservices via REST APIs. In both cases, we preserve your existing bot library and governance model while adding generative AI capabilities that handle the long tail of document variations your current automation cannot process.
Process mining uses event log data from your ERP, CRM, and ITSM systems to reconstruct the actual execution paths of your business processes - not the idealized version in your SOPs. Tools like Celonis, UiPath Process Mining, and SAP Signavio analyze millions of historical transactions to identify bottlenecks, rework loops, and compliance deviations. This data-driven discovery ensures we automate the processes that have the greatest actual impact rather than the ones that stakeholders believe are most important. Process mining also establishes a pre-automation baseline for ROI measurement after deployment.
We establish a measurement framework before development begins, capturing baseline cycle times, error rates, labor hours per transaction, and cost per process instance from your existing systems. Post-deployment, we instrument each automated workflow with telemetry that tracks throughput, exception rates, straight-through processing percentages, and bot utilization. ROI dashboards surface cost savings, headcount redeployment value, and error reduction metrics on a weekly basis. Clients typically see full payback within 9 to 18 months and ongoing savings of 40 to 70 percent of pre-automation labor costs for high-volume processes.
We architect AI automation pipelines with data residency and classification controls from the outset. Documents containing PII, financial data, or trade secrets are processed through on-premise or VPC-deployed LLM instances rather than shared cloud APIs when data sovereignty requirements demand it. All data in transit is encrypted using TLS 1.3, and documents are never retained by third-party AI providers beyond the processing window. We implement role-based access controls, audit logs, and data masking for fields not required for the automation task, ensuring compliance with GDPR, HIPAA, SOC 2, and other applicable frameworks.
Our contract analysis pipeline ingests documents from your CLM system, email, or shared drives and passes them through an LLM extraction layer that identifies parties, effective dates, payment terms, liability caps, termination clauses, and non-standard provisions. Extracted fields are validated against your contract playbook rules, and deviations are flagged for legal team review with the relevant contract language highlighted. Compliant contracts route automatically to signature workflows while flagged items enter a structured review queue with AI-generated summaries. This reduces average contract review time from several days to under four hours for standard agreement types.
Yes. Modern LLMs process documents in over 50 languages without requiring separate translation infrastructure, making them well suited to multinational enterprises that receive invoices, contracts, and customer correspondence in diverse languages. Our pipelines handle PDF, DOCX, XLSX, HTML, TIFF, JPEG, and EDI formats through a unified ingestion layer that normalizes content before LLM processing. We have deployed multilingual invoice processing for clients operating across EMEA that processes documents in 12 languages with extraction accuracy above 95 percent across all supported formats.
Every AI automation pipeline we build includes a structured exception management layer. When confidence scores fall below configurable thresholds or business rules cannot be satisfied, the transaction is routed to a human-in-the-loop queue with the AI-extracted data, the source document, and a plain-language explanation of why the exception occurred. Human reviewers correct or approve the transaction through a purpose-built review interface, and their decisions are fed back as training signal to improve future model performance. Exception rates typically drop 30 to 50 percent within the first 90 days of production operation as the model adapts to your specific document variations.
Financial services onboarding automation integrates identity document verification, KYC data extraction, sanctions screening, and risk scoring into a single orchestrated workflow that replaces manual handoffs between compliance, operations, and technology teams. We use LLMs to extract and validate identity fields from passports, driving licences, and utility bills against authoritative sources, and connect to screening APIs such as Refinitiv World-Check or Dow Jones for AML checks. The entire process from document submission to account provisioning is tracked in a compliance audit trail that satisfies regulatory examination requirements. Clients have reduced onboarding cycle times from 5 business days to under 4 hours for straightforward applicant profiles.
Compliance reporting automation aggregates data from multiple source systems - trading platforms, risk engines, HR systems, and GL - and applies LLM-powered interpretation to regulatory text to generate draft report sections, identify data gaps, and flag potential violations before submission deadlines. We have built CCAR, MiFID II transaction reporting, and ESG disclosure automation pipelines that reduce the labor burden on compliance teams by 60 percent or more. Each pipeline includes a validation layer that cross-checks generated content against the underlying data and regulatory schema, ensuring accuracy before human review and sign-off.
Governance for regulated-industry AI automation encompasses model risk management documentation, change control procedures, segregation of duties in workflow design, and immutable audit logs for every automated decision. We align with SR 11-7 model risk guidance and equivalent international standards by documenting model assumptions, validation results, and performance monitoring thresholds. All automation changes pass through a formal UAT and approval process before promotion to production, and we implement circuit breakers that pause automation and alert operations teams when anomaly detection thresholds are breached. Quarterly model performance reviews and annual validation cycles are built into our managed service offering.
A focused single-process automation - such as invoice processing or a specific onboarding workflow - typically moves from discovery to production in 8 to 14 weeks. A broader program covering 5 to 10 processes across multiple business units operates on a 6 to 12 month roadmap with phased deployments every 6 to 8 weeks. The timeline is driven primarily by the complexity of source system integrations, the availability of historical data for model training, and the change management requirements of the affected business teams. We use a parallel delivery model where integration work, model development, and change management proceed concurrently to compress overall delivery timelines.
Our managed automation service includes 24/7 bot health monitoring, monthly performance reviews, and a continuous improvement cycle that ships model updates and workflow enhancements every 4 to 6 weeks. As your document volumes grow or new document variants emerge, our team retrains extraction models and adjusts business rules without requiring a new project engagement. We also provide a dedicated customer success manager who reviews ROI metrics with your operations leadership quarterly and identifies new automation opportunities as your program matures. SLAs cover bot availability, exception resolution times, and model accuracy floors.
Successful automation programs require as much attention to people as to technology. We embed a change management workstream in every engagement that includes stakeholder impact assessments, role redesign workshops, and communication planning to help affected employees understand how their work will change rather than disappear. Our experience shows that organizations that invest in retraining displaced staff for higher-value analytical and exception-handling roles achieve significantly better automation adoption rates and sustain those gains over time. We partner with your HR and L&D teams to design transition pathways and measure employee sentiment throughout the program.
The most frequent failure mode is automating a broken process rather than redesigning it first, which locks inefficiency in place and limits the achievable ROI. A close second is underestimating the integration complexity of legacy ERP and mainframe systems, which can consume 40 to 60 percent of total project budget if not scoped carefully in discovery. Poor data quality in source systems - duplicate vendor records, inconsistent cost center codes, missing master data - is the third major risk, as AI models inherit the quality of the data they process. We address all three through our structured discovery phase, which produces a data quality assessment, a process redesign recommendation, and a detailed integration architecture before any development begins.
Industries
Financial ServicesInsuranceHealthcareManufacturingLogistics and Supply Chain