Intelligence
you can trace.

PwC's DocIntelligence project involved developing an enterprise AI-powered platform designed to automate the extraction, analysis, and review of complex financial and business documents.

The solution combines Large Language Models (LLMs), Retrieval-Augmented Generation (RAG), agentic workflows, and cloud-native application services to support document processing at enterprise scale.

A central engineering challenge was moving beyond simple LLM interactions to create a reliable system capable of producing structured, evidence-grounded outputs that users could verify against original documents.

The platform brings together Python-based AI services, .NET backend APIs, an Angular frontend, and Microsoft Azure infrastructure.

Core value proposition: Transform complex, document-intensive workflows into AI-assisted processes with structured extraction, source traceability, automated validation, and human oversight.

Financial document analysis presents several challenges for enterprise organizations.

Complex and inconsistent document structures

Business and financial documents often contain tables, numerical data, narrative descriptions, and information distributed across multiple pages. Relevant information can appear in different locations and formats.

Time-consuming manual review

Identifying and extracting relevant information requires substantial manual effort, particularly when documents are lengthy or contain numerous financial details.

Accuracy and traceability

Conventional LLM responses can contain inaccurate values or conclusions that are not supported by the source material. Financial document processing requires mechanisms for verifying extracted information.

Enterprise processing requirements

Large-scale workflows need reliable asynchronous execution, fault tolerance, monitoring, and integration with existing applications.

Human validation

AI-generated outputs must remain accessible for human review, correction, and verification before being used in business processes.

The platform follows a distributed architecture separating the user interface, application services, AI processing, and cloud infrastructure.

Frontend – Enterprise Document Review

Angular 20 · Nx · NgRx Signals · TypeScript

The frontend provides a unified interface for document upload, processing, extraction review, and evidence verification.

Key capabilities include:

Backend – Enterprise Application Services

C# · .NET · REST APIs · Azure Service Bus

The .NET backend provides the application integration layer between the frontend, data persistence, and AI services.

Responsibilities include coordinating document operations, handling application requests, integrating AI processing workflows, and communicating with asynchronous processing components.

AI Processing Layer

Python · FastAPI · LangGraph · LLMs

The AI processing layer implements document extraction, information retrieval, workflow orchestration, and result validation.

Its responsibilities include:

Cloud Infrastructure

Microsoft Azure · Azure AI Search · Microsoft Foundry · Azure Functions

Azure services support retrieval, model integration, messaging, and background processing.

The architecture provides a foundation for separating computationally intensive AI workloads from user-facing application services.

One of the platform's core technical capabilities is its Hybrid RAG implementation.

Rather than providing complete documents directly to an LLM for every operation, the system retrieves relevant source evidence and supplies it to the model during extraction and review workflows.

Dense Retrieval

Semantic vector search identifies passages based on conceptual similarity between a query and document content.

This is valuable when the terminology used in a query differs from the wording found in the original document.

Sparse Retrieval

Keyword-oriented retrieval identifies passages based on lexical matching.

It is particularly useful for exact financial terminology, identifiers, and specific references.

Reciprocal Rank Fusion (RRF)

Results from dense and sparse retrieval are combined through Reciprocal Rank Fusion.

This produces a consolidated ranking that benefits from both semantic and lexical search.

Evidence-Grounded Extraction

Relevant source passages are supplied to the LLM as supporting context for extraction.

Generated results are associated with references to source evidence, supporting subsequent validation and review.

Page-Level Traceability

The platform provides page-level source references, enabling users to navigate from extracted information to the corresponding document evidence.

Technical outcome: A retrieval architecture designed to improve evidence discovery while supporting verifiable AI-generated results.

The platform uses LangGraph-based workflows to coordinate multi-step AI operations.

Instead of relying on isolated LLM requests, the processing layer executes structured sequences of retrieval, extraction, and validation steps.

Workflow Capabilities

Document processing

Preparation and orchestration of document extraction tasks.

Evidence retrieval

Identification of relevant passages through hybrid search.

LLM extraction

Generation of structured information using retrieved document context.

Validation

Verification of output structure, citations, and consistency with source material.

Resilience

Retry and failover mechanisms for recoverable processing failures.

Multi-Model Routing

The architecture supports selecting different configured AI models depending on processing requirements.

This allows model capabilities to be evaluated and compared without tightly coupling the application to a single model.

Token-Aware Batching

Large inputs are divided into manageable processing batches that respect model context constraints.

This supports efficient processing of lengthy documents.

Asynchronous Execution

Background workers execute document processing operations independently of immediate frontend requests.

Azure messaging and serverless services support coordination across application components.

A key aspect of the engineering work was implementing mechanisms to evaluate and validate AI-generated outputs.

Ground-Truth Evaluation

Reference datasets containing expected extraction results are used to evaluate model performance.

Predictions can be compared against known values to identify errors and assess extraction quality.

Model Comparison

Different models and processing strategies can be evaluated against consistent reference datasets.

This supports evidence-based model selection and ongoing improvements.

Structured Output Validation

LLM responses are validated against expected data structures.

The objective is to prevent malformed or incomplete outputs from passing through downstream workflows.

Citation Accuracy

Source-reference validation mechanisms check whether extracted information is supported by the cited document evidence.

Consistency Checks

AI-generated information is assessed for inconsistencies against the original source documents.

Observability

Internally developed monitoring and evaluation mechanisms support investigation of model behavior, processing failures, and quality issues.

Engineering principle: LLM output is treated as information requiring validation, not as an inherently reliable source of truth.

The platform combines automated extraction with a user-facing review workflow.

Users can:

The system is designed to support human judgment rather than replace it.

This is particularly important for financial information, where traceability and correctness are essential.

The platform integrates multiple application and AI technologies into a coordinated processing system.

Python/FastAPI

Dedicated services for AI operations, document extraction, retrieval, and workflow execution.

C#/.NET

Enterprise APIs and application integration services.

Azure Service Bus

Asynchronous messaging between processing and application components.

Azure Functions

Background and event-driven processing integrations.

Azure AI Search

Search infrastructure supporting hybrid retrieval.

Microsoft Foundry

Integration with AI model capabilities and associated platform services.

Angular

Enterprise user experience for document submission, evidence inspection, and AI-assisted review.

This separation allows AI processing capabilities to evolve independently from the application interface and conventional business services.

The engagement included hands-on contributions across several technical areas.

The engineering work combined AI-specific development with full-stack enterprise application integration.

The platform delivers capabilities intended to improve document-intensive enterprise workflows.

Faster Information Extraction

Automated document processing reduces reliance on manual identification and transcription of relevant information.

Evidence-Based AI Results

Source citations and document highlighting allow users to verify outputs against original materials.

Improved Review Experience

A unified review interface combines extracted information with relevant source evidence.

More Reliable AI Processing

Evaluation, validation, and resilient processing mechanisms address common limitations of conventional LLM-based applications.

Scalable Processing Architecture

Asynchronous workloads, batching, and Azure integrations support the processing demands of enterprise applications.

Long-Term Model Flexibility

The orchestration layer supports evaluating alternative models and adapting the AI processing strategy over time.

These describe implemented capabilities and intended business value. Quantified productivity improvements, accuracy rates, processing volumes, and financial returns are not claimed.

The DocIntelligence engagement demonstrates the engineering capabilities required to integrate generative AI into enterprise software.

Enterprise AI Architecture

Combining LLM capabilities, backend integrations, cloud infrastructure, and user-facing applications into a coordinated system.

Reliable Agentic Workflows

Using LangGraph, retrieval, validation, and controlled processing to build AI systems that go beyond isolated prompt interactions.

Evidence-Grounded Document Intelligence

Linking extracted information to original source material through hybrid retrieval and citation mechanisms.

End-to-End Engineering

Working across AI services, .NET APIs, Angular applications, asynchronous processing, and Microsoft Azure.

PwC's DocIntelligence project demonstrates how generative AI can support complex enterprise document workflows through structured extraction, hybrid retrieval, agentic orchestration, and human-assisted validation.

The platform combines the flexibility of modern LLMs with engineering practices focused on reliability, traceability, integration, and operational resilience.

The key achievement is moving beyond standalone AI demonstrations to production-deployed enterprise document intelligence, where AI-generated information is supported by evidence, validated through controlled workflows, and accessible for human review.

Portfolio Attribution

This case study describes selected professional engineering contributions made during an engagement with PwC. It is presented as part of the technical experience of Noian Software's founder and does not imply that Noian Software independently delivered or contracted the entire platform.

Specific internal implementation details, confidential business information, and unverified performance metrics have been excluded.