For most insurance teams, table-heavy documents are where productivity breaks down. Schedules of values stretch across hundreds of rows. Loss runs arrive in inconsistent formats. Key data is buried in scanned PDFs or fragmented spreadsheets. Before any underwriting or claims decision can happen, someone has to reconstruct that data manually.
That work is slow, error-prone, and expensive. And more importantly, it sits at the very front of the workflow, delaying everything that comes after.
This is exactly the kind of upstream bottleneck that has held back insurance operations for years. Even with modern systems in place, teams still spend a significant portion of their time preparing data instead of acting on it.
From reconstruction to ready-to-use data
Indico’s Table Builder agent changes how this work gets done.
Instead of asking teams to manually build and validate tables, the platform automatically converts unstructured, like-kind data into structured, editable outputs. It reads documents as they are, no templates, no pre-formatting, and organizes the data into clean tables that are immediately usable.
This isn’t just extraction. It’s preparation.
Tables are delivered in a format underwriting and claims teams already work in, with row-by-row and column-by-column alignment that reflects the original document. Whether the input is a spreadsheet with 100+ columns or a nested table inside a scanned PDF, the output is consistent and structured.
Take a closer look at Indico’s document and table ingestion capabilities.
Built for real insurance data, not ideal inputs
Most tools struggle with variability. Insurance data doesn’t arrive in neat, predictable formats, and that’s where generic solutions break down.
Table Builder is designed for that reality. It handles complex schedules, large datasets, and inconsistent layouts without requiring custom training or workarounds. The system identifies the right fields, groups related data intelligently, and preserves the structure that matters for downstream decisions.
This is what it means to be purpose-built for insurance operations: handling the messy inputs that actually exist, not the ones systems wish they had.
See how this fits into a broader operational strategy: learn how Indico keeps insurance work in motion.
Transparent, editable, and ready to move
Clean data alone isn’t enough. Teams also need confidence in where it came from and control over how it’s used.
Every data point in a Table Builder output is fully traceable back to its source. Users can click into any cell and immediately view the original document context. That level of transparency makes it easier to validate outputs, support audits, and trust automation in production workflows.
From there, teams can edit tables directly in the platform or export them to Excel in seconds. No rework. No reformatting. Just data that moves forward with the workflow.
What this unlocks for underwriting and claims
When table-building is automated, the impact shows up immediately.
Underwriters spend less time reconstructing submissions and more time evaluating risk. Claims handlers move faster through intake and validation without getting stuck in document prep. Workflows that used to stall at the front door start moving.
This is the role of agentic AI in insurance operations. Not replacing decisions, but removing the manual work that delays them. By transforming unstructured inputs into structured, system-ready data, Indico keeps underwriting and claims work in motion, faster, cleaner, and with far less friction.
Ready to eliminate manual table-building from your workflows? See Indico in action.
FAQs
How does Indico Data extract structured data from complex insurance documents like SOVs and loss runs?
Indico Data uses a proprietary hybrid of discriminative and generative AI technology to extract structured data from complex insurance documents such as Schedules of Values (SOVs), loss runs, broker slips, and ACORD forms. The platform ingests emails, attachments, and PDF packets, then unbundles, classifies, and extracts field-level data into structured outputs such as JSON or CSV for downstream business intelligence and ETL systems.
A base model trained on more than 500 million labeled data points powers this extraction, enabling transfer learning that reduces the labeled data needed for custom tasks by 100 to 1,000 times compared to traditional approaches. The platform applies field-level validation and confidence scoring to each extracted value, allowing operations teams to trust outputs or route exceptions for human review. Outputs are designed to auto-populate policy administration systems, including direct integrations with Guidewire PolicyCenter and ClaimCenter.
This approach converts messy tables, handwritten notes, and inconsistent layouts into clean, system-ready records that underwriters and claims handlers can act on immediately. The extraction pipeline supports enterprise security requirements, including SOC 2 compliance, end-to-end encryption, role-based access control, and audit logs.
Does Indico Data integrate with Guidewire PolicyCenter and ClaimCenter for insurance workflows?
Indico Data provides a purpose-built Guidewire integration accelerator that connects its Intelligent Intake platform to Guidewire PolicyCenter and ClaimCenter via APIs, enabling automatic population of system fields from unstructured submission and claims documents. This accelerator is certified as Ready for Guidewire, indicating compatibility with Guidewire Cloud environments used by more than 540 insurers in 40 countries.
The integration extracts data from emails, PDFs, loss runs, and ACORD forms, then routes structured outputs directly into PolicyCenter for underwriting or ClaimCenter for First Notice of Loss (FNOL) processing. Markel’s deployment for claims intake resulted in 48% faster claim setup time and 33% lower ingestion costs. The platform applies validation and confidence scoring at the field level before populating Guidewire, reducing downstream data quality issues. For P&C insurers on the Guidewire stack, this integration eliminates manual re-keying of submission data and accelerates time-to-quote. Guidewire is a strategic investor in Indico Data, reinforcing the depth of the integration partnership. The accelerator supports both policy servicing and claims workflows within a unified intake architecture.
How does Indico Data’s Table Builder convert messy tables into clean, structured data?
Indico Data addresses the challenge of extracting data from messy, inconsistent tables found in insurance documents such as SOVs, loss runs, and broker slips by combining proprietary discriminative and generative AI models. The platform’s base model, trained on more than 500 million labeled data points, recognizes table structures regardless of formatting inconsistencies, merged cells, or non-standard layouts. Once ingested, documents are unbundled, classified, and processed to extract row and column data into structured JSON or CSV outputs suitable for downstream analytics, ETL pipelines, or direct system integration.
Indico’s transfer learning approach reduces the amount of labeled data required to train custom extraction models by 100 to 1,000 times compared to traditional machine learning methods. This design allows operations teams to deploy table extraction for new document types without months of manual labeling. The Convex case study demonstrates the platform’s effectiveness: median processing time for SOV and loss run documents dropped from approximately two hours to roughly 40 seconds, and raw prediction accuracy reached 91% without any human intervention. Confidence scores accompany each extracted field, enabling exception routing when values fall below defined thresholds. The platform’s outputs feed directly into underwriting workbenches, policy administration systems, and business intelligence tools. This capability transforms previously manual, error-prone table transcription into an automated, auditable process.
What security and compliance features does Indico Data provide for enterprise insurance deployments?
Indico Data’s Intelligent Intake platform is built with enterprise security and regulatory compliance as foundational requirements for insurance deployments. The platform claims SOC 2 compliance, end-to-end encryption, role-based access control (RBAC), and audit logs to support regulated insurance environments. These controls enable insurance carriers to meet internal governance standards and external regulatory requirements when processing sensitive policyholder and claims data. Deployment options include Indico-hosted clusters, private-tenant deployments with customer-controlled data encryption, and customer-deployed software installations within the customer’s own computing environment.
Developer documentation describes containerized deployment using Docker and Helm charts, allowing enterprise IT teams to integrate Indico into existing infrastructure and private registries. The platform provides data lineage and confidence scoring for every extracted field, creating an auditable trail from source document to structured output. This traceability supports compliance reviews and helps underwriting and claims teams explain automated decisions to regulators or auditors. The Agentic Decisioning Platform extends these governance features to multi-agent workflows, where each agent’s actions are logged and explainable. Indico Data’s Trust Center and Knowledge Base provide public access to security posture documentation, user guides, and release notes for ongoing compliance verification.
