Automate What Humans Think, Not Just What They Click.
Traditional RPA breaks on unstructured inputs, edge cases, and anything that requires judgment. SourceMash's AI-Powered Process Automation practice goes further — combining large language models, intelligent document processing, ML decision engines, and agentic orchestration to automate complex, judgment-intensive business processes end-to-end, achieving 80–90% straight-through processing where rule-based automation reaches a ceiling of 40–60%.
Beyond RPA: Automation That Understands, Decides & Acts.
Traditional robotic process automation works by recording and replaying deterministic UI interactions. It is effective for processes that are entirely rule-based, use fixed data formats, and never encounter genuine exceptions. But most high-value enterprise processes are none of those things — they involve unstructured documents, variable formats, judgment calls, and edge cases that break rules-based automation at exactly the moments it matters most.
AI-powered process automation operates at a fundamentally different level: it reads and interprets unstructured inputs, applies contextual judgment learned from historical examples, handles exceptions with reasoning rather than error codes, and improves continuously as it processes more real-world cases. The result is straight-through processing rates of 80–90% on complex, document-heavy processes that traditional automation cannot touch.
Intelligent Document Processing (IDP)
Every enterprise drowns in documents — invoices, purchase orders, contracts, insurance claims, customs declarations, bank statements, medical records, regulatory filings. Processing them manually is expensive, error-prone, and slow. Basic OCR automates the keystrokes but not the understanding. SourceMash IDP pipelines combine optical character recognition, layout analysis, named entity recognition, and large language model extraction to classify incoming documents, extract structured data, validate against business rules, and route to downstream systems — with accuracy that matches or exceeds trained human reviewers, at any volume, 24 hours a day.
Our IDP systems handle multi-page, multi-language, and variable-format documents — scanned PDFs, native PDFs, images, emails with attachments, and handwritten forms — with exception workflows that flag genuinely ambiguous cases for human review rather than silently producing incorrect output. Clients typically achieve 80–90% straight-through processing within three months of go-live.
The IDP Pipeline — Five Automated Stages
Every document entering your process passes through five automated stages before any human touches it
Ingestion & Classification
Documents ingested from email, portal, SFTP, or scanning — automatically classified by type using a fine-tuned model with 99%+ type accuracy.
OCR & Layout Analysis
Multi-engine OCR with layout-aware processing identifies tables, headers, fields, stamps, signatures, and handwritten annotations.
Entity Extraction
NER and LLM-based extraction plus structured fields — amounts, dates, parties, line items, references — with field-level confidence scores.
Validation & Rules
Extracted data validated against business rules — arithmetic checks, master data matching, duplicate detection — exceptions flagged with failure reason.
Routing & Integration
Validated data pushed to SAP, Oracle, Coupa, or your system of record via API — with full audit trail and exception queue management.
Document Types We Automate
Pre-trained models for common enterprise document categories — plus custom training for your proprietary formats
Invoices & Purchase Orders
Vendor invoices and POs across formats — header data, line items, tax, shipping, payment terms, and GL coding suggestions. Three-way match validation against PO and GRN data.
Contracts & Agreements
Key clause extraction — parties, effective dates, termination rights, payment terms, liability caps, non-standard clauses — with risk flag classification against your legal standards library.
Medical Records & Clinical Forms
Patient demographics, diagnoses, medications, lab results, procedure codes (ICD-10, CPT), and clinical notes — with HIPAA-compliant handling and EHR system integration.
Identity & KYC Documents
Passports, driving licences, PAN/Aadhaar cards, utility bills, and bank statements — with liveness detection, tamper analysis, and cross-field consistency validation for KYC workflows.
Trade & Customs Documents
Bills of lading, certificates of origin, packing lists, commercial invoices, and customs declarations — enabling automated customs filing, trade finance processing, and compliance screening.
Insurance Claims & Reports
FNOL forms, adjuster reports, medical bills, repair estimates, and policy documents — with coverage validation, fraud signal detection, and reserve calculation integration for claims workflows.
IDP Technology Stack
We select the right combination of OCR engine, layout model, and extraction approach for each document type and accuracy requirement — custom fine-tuning on your document corpus is standard practice, not an add-on.
AI-Driven Decision Automation
High-volume, rule-governed business decisions — loan approvals, insurance underwriting, trade finance exception handling, credit limit adjustments, warranty claim validation, compliance screening — consume enormous analyst capacity on routine cases where a well-trained ML model can make the same decision more consistently, faster, and with full explainability. SourceMash builds ML-based decision engines that automate these decisioning workflows at machine speed while maintaining complete audit trails, human override mechanisms, and the regulatory explainability that makes automated decisions defensible.
The critical design principle: every automated decision is accompanied by a ranked list of the factors that drove it — SHAP-based explanations in plain language — so compliance officers, auditors, and affected customers can understand and challenge any specific outcome. This explainability is engineered in from the start, not retrofitted.
Decision Automation Use Cases
Across industries where high-volume, rule-governed decisions consume analyst capacity
Loan & Credit Decisioning
Automated origination decisions for personal loans, SME credit, credit cards, and BNPL — combining bureau data, alternative signals, and application behaviour in ML models that outperform scorecard baselines, particularly for thin-file applicants.
Insurance Underwriting
Automated underwriting decisions for personal lines, commercial lines, and specialty insurance — risk scoring, coverage eligibility, premium calculation, and referral routing, dramatically reducing quote turnaround time.
Returns & Warranty Adjudication
Automated claims adjudication for product returns, warranty claims, and refund requests — validating eligibility against purchase history, policy terms, and product condition data to approve, deny, or route complex cases in seconds.
Trade Finance Exception Handling
Document discrepancy decisioning in trade finance — classifying LC document exceptions as acceptable, refusals, or requiring waiver, recommending resolution actions based on UCP 600 rules and historical bank decision precedents.
Admissions & Eligibility Screening
Automated screening for university admissions, scholarship eligibility, government benefit eligibility, and programme enrolment — applying complex, multi-criteria rules consistently at scale while routing borderline cases for human review.
Logistics & Freight Routing
Automated carrier selection, route optimisation decisions, and exception handling for delivery failures — applying dynamic routing logic incorporating real-time capacity, pricing, SLA risk, and sustainability constraints.
How We Ensure Regulatory Defensibility
Every automated decision system we build satisfies six non-negotiable governance requirements
Hyperautomation & Intelligent RPA
Hyperautomation is an architectural strategy: the combination of RPA, AI cognition, process mining, and orchestration to automate the maximum possible proportion of end-to-end business processes. SourceMash's hyperautomation practice works in two directions simultaneously — extending your existing RPA investments by adding AI cognition where they break (unstructured inputs, edge cases, judgment-heavy steps), and designing AI-native automation architectures from scratch for processes where RPA was never the right tool. The goal in both cases is the same: maximum straight-through processing with minimum human intervention, in a way that is observable, maintainable, and compliant.
For organisations with existing UiPath, Blue Prism, or Automation Anywhere deployments, we assess bot exception rates, failure patterns, and escalation volume to identify exactly where AI augmentation will deliver the highest return — adding IDP, LLM interpretation, or ML decisioning at the specific workflow steps that currently break most frequently.
RPA vs. AI-Augmented vs. AI-Native — Where Each Wins
Understanding the right architecture for each type of process step
| Process Characteristic | Traditional RPA | AI-Augmented RPA | AI-Native Automation |
|---|---|---|---|
| Structured, fixed-format data | ✓ | ✓ | ✓ |
| Unstructured / variable documents | ✗ | ✓ | ✓ |
| Judgment-based exception handling | ✗ | Limited | ✓ |
| Natural language processing | ✗ | Partial | ✓ |
| Learning from new patterns | ✗ | Partial | ✓ |
| Legacy system UI interaction | ✓ | ✓ | Via Computer Use |
| Resilience to UI changes | Low | Medium | High (API-first) |
| Maintenance overhead | High | Medium | Low |
How It Works
Our hyperautomation engagement — from bot audit to enhanced production deployment
Process & Bot Audit
We audit your existing automation landscape — exception rates, escalation patterns, failure logs, and manual intervention volume — to identify the workflow steps where AI augmentation delivers the highest ROI.
AI Layer Design
We design the AI augmentation architecture — which steps get IDP, which get LLM reasoning, which get ML decisioning — and specify integration points between AI components and existing RPA bots or system APIs.
Build & Integrate
AI components built and integrated with your RPA platform (UiPath AI Centre, Blue Prism Decipher, Automation Anywhere IQ Bot) or as standalone intelligent orchestration layer via LangGraph.
Monitor & Improve
Exception rate, STP rate, and accuracy monitored continuously — with periodic model updates and workflow refinements that progressively reduce residual human intervention volume over time.
Email & Communication Triage Automation
Enterprise inboxes are operational bottlenecks. Customer service teams, procurement desks, trade finance operations, compliance functions, and financial institutions process thousands of inbound emails daily — each requiring a human to read, interpret, classify, route, and either respond or trigger an action in a downstream system. This is high-volume, cognitively demanding triage work that is too unstructured for traditional automation, but too repetitive and rule-governed to justify skilled analyst time at scale.
SourceMash's email triage automation systems apply NLP classification, named entity extraction, priority scoring, and LLM-based response drafting to process inbound communications at volume — automatically routing the right emails to the right teams, triggering the right system actions, and drafting suggested responses for human approval or sending autonomously for routine cases. Clients typically reduce email triage effort by 60–75% within two months of deployment.
The Triage Pipeline — Five Automated Stages
From raw inbound email to routed, actioned, and responded-to communication
Ingestion & Dedup
Emails ingested from shared mailboxes, portals, and messaging channels — deduplicated, threaded, and pre-processed (attachment extraction, language detection, encoding normalisation).
Intent Classification
Multi-label intent classifier trained on your historical email types — identifying primary intent (complaint, query, instruction, escalation) and secondary attributes with 95%+ accuracy.
Entity Extraction
NER extracts structured data from free-text email bodies — account numbers, dates, amounts, counterparty names, reference numbers — making emails machine-readable for downstream system actions.
Priority & Routing
ML priority score assigned based on SLA sensitivity, customer tier, intent urgency, and business rules — automatically routed to the correct team queue or automated resolution workflow.
Action & Response
System actions triggered and response drafted by LLM — either sent autonomously for routine cases or queued for human approval on complex ones.
Where Email Triage Automation Delivers Mos
Highest-impact industry and function applications
KYC / AML & Compliance Automation
Financial crime compliance is one of the most resource-intensive operational functions in banking, insurance, and fintech — and one where the gap between manual effort and actual risk detection is most acute. Most banks employ hundreds of KYC analysts performing largely manual review of documentation, adverse media, sanctions databases, and transaction patterns — producing consistent false positive rates of 90–95% on AML alerts that consume analyst time on non-suspicious cases while potentially missing genuinely suspicious patterns that require cross-entity analysis to detect.
SourceMash builds AI-powered compliance automation systems that perform the investigative and decisioning steps currently done by analysts: identity document verification with tamper detection, adverse media screening using NLP across hundreds of sources, PEP and sanctions list matching with fuzzy logic, transaction behaviour anomaly detection, network graph analysis for connected-party risk, and structured SAR drafting — achieving analyst-level accuracy on routine cases while surfacing complex cases with full supporting evidence for senior review.
The Compliance Automation Stack
Layered AI capabilities covering end-to-end compliance — from onboarding through ongoing monitoring
Identity Document Verification
AI-powered verification of passports, driving licences, PAN/Aadhaar, and utility bills — with tamper detection, MRZ validation, liveness check integration, and cross-field consistency checks that flag forgeries and alterations.
Adverse Media & NLP Screening
Continuous adverse media screening across 10,000+ global news sources, court records, and regulatory databases using NLP entity linking, sentiment analysis, and risk event classification — in 20+ languages.
PEP & Sanctions Matching
Fuzzy matching against PEP lists, UN, OFAC, EU, HMT, and MAS sanctions databases with phonetic and transliteration-aware algorithms — dramatically reducing both false positives and missed matches vs. exact-string approaches.
Transaction Behaviour Analytics
ML-based transaction monitoring that learns normal behaviour patterns per customer segment and flags anomalies — cash structuring, velocity spikes, counterparty network changes, and behavioural inconsistencies that rule-based systems miss.
Network Graph & Entity Risk
Graph neural network analysis that detects connected-party risk, beneficial ownership chains, fraud rings, and related-party networks — surfacing risks that point-in-time individual screening cannot identify.
Automated SAR Drafting
LLM-powered first-draft SAR generation — compiling investigation findings, transaction evidence, risk narrative, and regulatory disclosure details into a structured draft that analysts review and file, reducing production time by 70%.
IDP Technology Stack
We select the right combination of OCR engine, layout model, and extraction approach for each document type and accuracy requirement — custom fine-tuning on your document corpus is standard practice, not an add-on.
Supply Chain & Operations Automation
Supply chain operations generate enormous volumes of structured and unstructured data — demand signals, supplier communications, logistics events, quality reports, customs documents, and invoice disputes — most of which is processed by operations teams performing high-volume, repetitive coordination work. SourceMash's supply chain automation practice connects demand signals, supplier data, inventory positions, logistics constraints, and quality data into a single intelligent automation layer that reduces procurement cycle times, improves supplier compliance, eliminates coordination overhead, and surfaces operational exceptions before they cause business disruption.
We go beyond automating individual tasks to orchestrating end-to-end supply chain workflows — from demand signal through purchase order creation, goods receipt, invoice matching, and supplier payment — with AI handling the interpretation, decision, and exception steps at each stage. The result is a supply chain that responds to demand changes and supply disruptions in hours rather than days.
End-to-End Supply Chain Automation Coverage
From demand sensing through supplier payment — the complete procure-to-pay automation stack
AI Demand Sensing & Auto-Replenishment
Real-time demand signal aggregation from POS, e-commerce, and order management systems — feeding ML demand sensing models that generate automated replenishment proposals directly in your ERP without manual planning intervention.
Autonomous PO Generation & Routing
AI-driven purchase order creation from approved requisitions — applying supplier selection logic, preferred contract terms, delivery window optimisation, and multi-level approval routing based on value thresholds and category policies.
Supplier Performance Scoring
Continuous ML-based supplier performance scoring across on-time delivery, quality reject rate, invoice accuracy, responsiveness, and ESG compliance — automatically updating supplier risk tiers and triggering supplier development workflows for underperformers.
Intelligent Three-Way Match
AI-powered invoice matching that handles variability in vendor invoice formats, partial deliveries, currency conversions, and tolerance windows — automating 88–96% of invoice matching with structured exception queues for genuine mismatches.
Supply Chain Risk & Disruption Intelligence
Continuous monitoring of supplier news, geopolitical events, logistics disruption signals, and financial health indicators — proactively surfacing supply risk events with recommended mitigation actions before they translate into stock-outs or production stoppages.
Logistics & Dispatch Optimisation
AI-driven carrier selection, route optimisation, load consolidation, and delivery scheduling — processing real-time capacity, rate, SLA, and sustainability constraints to automatically assign shipments to the optimal carrier and route.
50+ Enterprise System Connectors
Pre-built and custom connectors for the systems your processes already run on — reducing integration development timelines from months to weeks
AI Process Automation Technology Stack
We select the right combination of LLM, orchestration framework, document AI platform, and RPA tool for each process — optimising for accuracy, latency, security, and total cost of ownership.
What Our Clients Say
Our loan processing was a bottleneck — 3-day turnarounds in a market where competitors promised 24 hours. The IDP and AI decisioning system SourceMash built now processes 87% of applications straight-through in under 40 minutes with better accuracy than our human review team. Customers are delighted. Our cost per loan has dropped 60%. This is what applied AI looks like in production.
Our procurement team was drowning in manual PO processing — 5-day cycle times, constant supplier disputes, invoice errors. The AI automation SourceMash built now handles 91% of POs and invoice matches straight-through in hours. Our team focuses on strategic vendor development, not chasing paperwork. The ₹2.2 crore annual saving was a bonus we hadn't even modelled.
We processed 4,000 trade finance emails daily — each requiring a human to read, classify, extract key fields, and update our systems. SourceMash's triage automation now handles 68% of that volume automatically with 97% classification accuracy. Our operations team moved from drowning in inbox management to focusing entirely on complex exceptions and client relationships. Transformational for our ops cost structure.
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