Software Development/Warehousing
Order Fulfillment Warehouse Management System
Order management system for warehouse and fulfillment operations
View projectIntelligent Document Processing
Client Challenges
Document Processing Challenges Affecting Operational Capacity And Data Quality
Solbeg is typically engaged for Intelligent Document Processing when document volume, format variability, or audit requirements make manual handling difficult to scale and control. The focus is on improving how documents are captured, validated, reviewed, and passed into the business systems that depend on their data.
Every new supplier, client, or case adds data entry and checking work that has to be absorbed by additional staff or overtime. Peak periods become a staffing challenge rather than a system capacity issue, while the effort required to process each document remains largely unchanged as volumes grow.
A template- or rules-based setup can work reliably until a supplier redesigns an invoice, a form gains a field, or a new document type appears. Each variation requires additional configuration, and maintenance effort increases as coverage expands. Over time, the setup can become dependent on a small number of people who understand how the rules fit together.
Incorrect amounts, dates, identifiers, or party names can reach the ERP, billing, or case management system before anyone notices them. Correcting these records may involve several departments and additional reconciliation work, while reports built on the affected data become less reliable.
When intake, correction, and approval happen across mailboxes, shared drives, and spreadsheets, it becomes difficult to establish which document version produced a particular value or who changed it. Regulated and audit-sensitive processes then depend on manual evidence gathering instead of a consistent processing history.
Service Overview
Document Capture, Extraction, Review, And Integration Built As Production Software
Solbeg develops Intelligent Document Processing solutions as engineering systems that can include ingestion, classification, extraction, validation, review interfaces, integrations, and the infrastructure required to operate them. Work can begin with an entirely manual process or with an existing OCR or rules-based setup that no longer covers the document flow effectively.
Ingestion from email, scanners, file storage, portals, or APIs, followed by document type recognition, page splitting for mixed batches, and routing to the appropriate processing path. The workflow can also account for duplicate documents, amended versions, and inputs that do not match any known document class.
Extraction of fields, tables, and line items, followed by validation against business rules and reference data such as vendor, customer, or product records. Confidence information and validation results help determine which data can proceed automatically and which cases require additional review.
Interfaces where operators can review or correct uncertain results alongside the source document, with queue management, role-based actions, and an audit trail of changes. Corrections can also contribute to later evaluation and improvement of the extraction process.
Delivery of verified data into ERP, accounting, CRM, DMS, or case management systems through APIs, queues, file exchange, or supported import formats, including retry and failure-handling logic. This is the layer that turns Intelligent Document Automation into part of the operational workflow rather than a separate tool used alongside it.
Monitoring of extraction quality, document mix, exception rates, processing latency, and operating cost after release, together with versioned models or configurations and defined update procedures. This makes it possible to identify changes in document quality or model behavior before they affect downstream processes.
Problem & solution
The problem
Results from a curated sample do not necessarily carry over to the real document flow
Pilots and vendor demonstrations are often evaluated on a relatively clean sample: recent documents, readable scans, familiar layouts, and a limited number of document types. Production streams can also contain low-resolution scans, phone photographs, multi-page bundles, stamps, handwritten notes, credit notes, amendments, and formats that change without notice. A setup that performs well on the initial sample can therefore behave differently once the full range of real inputs appears.
A second challenge is that quality cannot always be represented by one overall accuracy figure. The business impact of an extraction error depends on the field. An error in a descriptive field may require a small correction, while an incorrect total, tax identifier, account number, or effective date can affect financial, legal, or operational processes. Acceptance criteria therefore need to reflect the importance of individual fields and document types.
The third challenge is operational. Even accurate extraction creates limited value if exceptions do not have a defined route, if verified data still has to be entered manually into the target system, or if employees keep a parallel process for unusual cases. In that situation, automation adds another processing step instead of reducing work across the overall flow.
Solbeg's approach
Solbeg defines the scope around exceptions, field-level accuracy, and the receiving system
At Solbeg, scoping begins with representative documents, including difficult cases where available: poor-quality scans, unusual layouts, rejected inputs, rare document types, and formats that have caused problems before. This helps determine which document classes the pipeline needs to support, what preprocessing may be required, and which parts of the process are realistic to automate. It can also reveal situations where an Intelligent Document Processing solution should be combined with changes to the intake process itself.
Acceptance criteria are defined at the level required by the business process. Different fields can have different thresholds depending on the consequences of an incorrect value. Those requirements then inform validation rules, confidence thresholds, review logic, and the evaluation dataset used to compare changes to the system.
Integration and exception routing are treated as part of the same engineering scope. Delivery into the system of record, duplicate handling, failure behavior, and the review queue are designed together. As a result, Intelligent Document Processing automation can be evaluated by both extraction metrics and the share of the document workflow that can be completed reliably through the target business system.
When this service fits
Document Workflows Where Automated Processing Can Make A Practical Difference
IDP solutions are most relevant when document volume, format variability, validation requirements, or downstream integration make manual processing difficult to scale. Solbeg adapts the processing flow to the accuracy, review, and integration requirements of each document type.
Invoices arrive by email, portal, or paper and vary by supplier, currency, tax structure, and line-item format. Processing may require header and line-level extraction, validation against supplier data, and matching against purchase orders or receipts before verified information can enter the accounting or ERP system.
Identity documents, proof of address, registration certificates, insurance documents, or other records may be submitted as scans or photographs of varying quality. Relevant fields need to be extracted, checked against application data, and routed for review when information is missing or inconsistent.
Agreements may be stored in a repository while important information such as renewal dates, payment terms, counterparty details, or other clauses remains available only inside the document text. Extraction can turn selected terms into structured fields for reporting, tracking, or downstream workflows, with review applied according to the required level of confidence.
A single submission can contain forms, correspondence, invoices, images, and supporting reports in one file or package. Before the case can be processed, the bundle may need to be split into documents, classified, routed, and processed according to the rules for each document type.
Bills of lading, packing lists, customs declarations, and commercial invoices may describe the same shipment in different formats and sometimes different languages. Extraction supports comparison between these documents so mismatches can be identified before they create downstream delays.
Orders, service requests, change requests, and attachments arrive in one inbox where employees manually read, categorize, and forward them. Classification, extraction of reference information, and routing into a ticketing, order, or workflow system can reduce repetitive triage work.
Years of scanned records may exist primarily as images with limited metadata. Batch processing can produce searchable text, structured fields, and indexes that make historical information easier to retrieve and use in analytics or other applications.
Scope of Work
Engineering Responsibilities Across An Intelligent Document Processing Engagement
Solbeg can combine process analysis, data engineering, extraction, application development, integration, QA, and security controls within an Intelligent Document Processing engagement. Team composition depends on document complexity, processing volume, integration requirements, data constraints, and the client’s existing capabilities.
Process and document analysis
Collection of document types, volumes, required fields, approval paths, exception scenarios, and available baseline information such as handling time or error rates. This establishes the process requirements and acceptance criteria that later implementation can be measured against.
Document ingestion and dataset preparation
Connectors for email, storage systems, scanners, portals, or APIs, together with preprocessing such as rotation correction, noise reduction, page splitting, and format normalization where required. The scope can also cover annotation workflows, dataset versioning, and retention rules for source and derived data.
Classification and extraction
OCR, document classification, and extraction of fields, tables, and line items using the techniques appropriate to the document set. Where traceability is required, extracted values can be linked back to the corresponding source page or region. Results are evaluated against the agreed document and field-level criteria.
Processing services and integrations
Pipeline orchestration, APIs, queues, retries, idempotency, duplicate detection, and delivery into ERP, DMS, accounting, or case management systems. This layer also defines what happens when extraction is unavailable, validation fails, or a result falls below the required threshold, helping keep the IDP solution usable within a transactional workflow.
Review and correction interfaces
Interfaces that present the source document together with extracted values, confidence information, validation issues, and the actions available to reviewers. Depending on the process, this can include queue assignment, keyboard-oriented correction, approvals, and a record of who changed individual values.
Deployment and monitoring
Development, training, and inference environments where applicable, CI/CD for application components, versioning of models and datasets, and monitoring of throughput, latency, quality, exceptions, and relevant operating costs. Update and rollback procedures are defined according to the deployment model.
Testing of software and extraction behavior
Regression datasets for comparing extraction behavior between versions, test cases covering degraded document quality, validation logic, integration failures, and other relevant scenarios. QA covers both the surrounding software workflow and the accuracy and consistency of extracted information.
Data protection and security controls
Definition of where documents are stored and processed, whether external services can be used, which information needs masking or restricted access, and what logging and retention requirements apply. These constraints are considered before the deployment architecture is finalized because they can affect the available technical options.
Why Solbeg
How Solbeg Combines Document Processing With Data And Application Engineering
Solbeg combines data engineering and analytics services, application engineering, integration, QA, and production operations within the same document processing engagement. This keeps extraction connected to the systems, workflows, and operational controls that use the resulting data.
Models and surrounding software can be delivered within one engagement
Extraction components, processing services, review interfaces, integrations, and infrastructure can be developed as parts of the same solution instead of being divided among separate suppliers. When companies compare Intelligent Document Processing companies, the surrounding engineering scope is important because extraction has to work inside an operational business process rather than as an isolated model.
Data engineering supports the extraction layer
Reliable document processing depends on ingestion, preprocessing, dataset management, traceable transformations, and controlled movement of information through the pipeline. Solbeg can include these data engineering responsibilities within the same engagement, helping teams reproduce results and investigate quality changes when they occur.
Production operation is considered as part of delivery
Deployment, monitoring, versioning, and update procedures can be included before production release instead of being introduced after a quality issue appears. Document streams evolve as suppliers, forms, regulations, and business processes change, so the solution needs a defined way to detect and manage those changes.
Integration with systems that are already in production
Extraction usually needs to connect to an ERP, accounting application, DMS, CRM, or case management system that already supports business-critical processes. The integration approach can follow the interfaces and import mechanisms available in the target system while automation is introduced incrementally alongside the existing workflow.
Deployment options follow data constraints
Where confidentiality, residency, client requirements, or internal policies restrict particular external services, Intelligent Document Processing solutions can be designed around suitable private-cloud, self-hosted, or on-premises options where the selected technologies support them. The deployment model is evaluated against security, quality, performance, operational effort, and cost.
Quality assurance covers both the application and extraction output
QA can validate the application workflow as well as extraction behavior, including comparisons between versions and difficult document cases. This helps detect situations where a model or configuration change improves one document class while reducing quality for another.
Comparison
Rules-Based Extraction Vs. Model-Based Extraction With Human Review
Document processing can use rules, templates, machine learning models, foundation models, or combinations of these approaches. The right architecture depends on layout stability, document variety, accuracy requirements, review capacity, and operating constraints. In production, Intelligent Document Automation can combine more than one method within the same processing pipeline.
Engagement Process
Document Processing Delivery From Sample Assessment To Production Operation
Solbeg moves document processing work through process review, representative sample assessment, pipeline design, pilot validation, and production rollout. The depth and order of individual activities can be adapted to the existing workflow and the work already completed by the client’s team.
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Document and process review
Solbeg analysts and engineers work with the relevant process owners to understand document types, volumes, sources, required fields, approvals, exceptions, and known problem cases. Where available, current handling time, error rates, and other baseline measures are recorded. The result is a documented view of how the process operates before automation is introduced.
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Sample assessment and acceptance criteria
A representative document sample is reviewed, including difficult or unusual inputs where available. Acceptance criteria are defined according to document and field requirements, and an evaluation set can be separated so later changes are measured consistently. This stage also helps identify document types that may be unsuitable for the first automation scope.
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Pipeline design and scope definition
The architecture for capture, classification, extraction, validation, review, and integration is defined together with the deployment model. Integration targets, security constraints, roles, dependencies, and the scope of the first production release are agreed before implementation proceeds.
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Pilot on representative or production documents
The processing pipeline is evaluated against real or representative documents under an agreed rollout approach. Where appropriate, it can operate in parallel with the existing workflow so results can be compared before automated decisions affect production records. Error analysis and threshold adjustment during this phase provide a measured view of Intelligent Document Processing automation on the client’s own document mix.
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Production release and ongoing operation
Verified data begins flowing into the agreed systems of record, with monitoring, exception handling, and version control in place according to the project scope. Documentation covers processing behavior, operational responsibilities, and update procedures. The solution can then be handed over or continue under an agreed support and development model.
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Key Questions Before Starting A Document Processing Project
That can only be estimated meaningfully after testing representative examples from your own document flow. Accuracy can differ by field, document type, layout, language, and input quality, so projects often evaluate important fields separately instead of relying on one overall number. Testing a representative sample is therefore an important part of assessing Intelligent Document Processing before a larger rollout.
Not always. The amount of labeled data required depends on the extraction approach, the document type, and the level of customization involved. Some model-based approaches can begin with a relatively small verified evaluation set, while custom training may require more extensive annotation. In either case, verified examples are needed to measure output quality consistently.
Potentially, but reliability varies significantly with input quality and document type. Clear printed text is generally easier to process than blurred photographs, degraded scans, faxes, or handwriting. Preprocessing can improve some inputs, but it cannot reliably recover information that is not visible or legible in the source, so representative difficult documents should be included during assessment.
Not necessarily. Depending on the technologies selected and the project’s security requirements, deployment can use private-cloud, self-hosted, or on-premises components where appropriate. The available options affect infrastructure requirements, operational responsibility, output quality, and cost, so deployment is evaluated as part of the architecture instead of being assumed in advance.
The workflow can shift from manual entry toward exception handling, validation, and approval, but the exact effect depends on how much of the process can be automated reliably. Straightforward cases may move through automatically while uncertain or business-sensitive cases continue to require review. The target operating model should therefore be defined together with the automation scope.
The integration uses the methods supported by the target system, which can include APIs, message queues, file exchange, database interfaces, or native import formats. Retry behavior, duplicate handling, validation failures, and integration errors are addressed as part of that layer rather than being treated separately from document processing.
Start with how quality will be measured and whether the evaluation uses your own representative documents. When comparing IDP companies, it is also useful to understand how exceptions are handled, how models and configuration are versioned and monitored, what integration work is included, where data is processed, and what happens when the document mix changes after launch. Ownership of code, configuration, datasets, documentation, and operational responsibilities should also be clear before the engagement starts.
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