A missing company description. An unclear business model. No context on operations. Each gap forces underwriters into manual web searches, tab switching, and copy-paste work just to make a submission usable.
This is exactly the kind of upstream friction Indico is designed to remove.
Indico Data’s web Research Agent helps underwriters find, populate, and validate missing submission information in seconds without leaving the triage workspace.
Manual research breaks underwriting flow and wastes capacity
Consider a common scenario. A commercial submission arrives with no company description. Before an underwriter can assess appetite or risk, someone has to track down basic details about the applicant.
Traditionally, that means opening new tabs, searching the company website, scanning for relevant information, summarizing it manually, and pasting it back into the underwriting system. It’s slow. It’s inconsistent. And it pulls experienced underwriters away from actual risk evaluation.
Multiply this across hundreds or thousands of submissions, and the impact on clearance time and underwriting capacity adds up quickly.
Research should happen directly inside submission triage
With Indico’s Research Agent, that manual work disappears.
Instead of leaving the submission, the underwriter simply tells the Agent what is missing. For example, “Find information about the company description from their website.”
With one click, the Agent navigates to the applicant’s website, scans for relevant content, and returns a concise, structured summary directly inside the triage workspace. The output includes what the company does, where it operates, and relevant lines of business. This is the context underwriters need to move forward with confidence.
There is no tab switching. No copy-pasting. No guessing.
Underwriters stay in control throughout the process. The returned description is easy to review and verify. If it looks accurate, the underwriter confirms it with a single click.
From there, the research Agent automatically updates the submission, populating the missing field in the underwriting record. The submission is now complete, consistent, and ready for evaluation without adding manual steps or downstream cleanup.
This keeps work moving while maintaining clear human oversight.
Automating research improves data quality and accelerates clearance
This type of embedded research may seem simple, but operationally, it delivers meaningful impact.
By automating repetitive web research, Indico improves data completeness at intake. Submissions clear faster. Records stay cleaner. And underwriters spend more time evaluating risk instead of reconstructing submissions.
The result is faster clearance, more consistent and audit-ready data, and increased underwriting capacity without adding headcount.
Indico keeps underwriting work in motion by ensuring submissions arrive complete, accurate, and ready for confident risk selection from the start.
FAQs
How does Indico ingest and normalize mixed broker submission packets including scanned PDFs, SOVs, loss runs, ACORDs, images, and handwritten fields?
Indico performs multi-channel ingestion of emails, attachments, zipped bundles, scanned and native PDFs, TIFFs, images, Excel files, ACORD forms, SOVs, loss runs, and handwritten fields through its Extraction Agents.
These agents unbundle submission packets, apply OCR with selectable presets (simple, standard, detailed) and produce structured JSON with preserved table and cell structure, token bounding boxes and per field confidence scores, enabling direct mapping into underwriting workflows. The platform exposes configuration controls to tune OCR latency versus accuracy per document class, allowing operations to set faster presets for high volume simple documents and more detailed presets for complex loss runs or schedules, this is supported by Agent Studio which lets teams compose extraction, enrichment and routing logic visually. Field provenance is retained at token level, including source page references and bounding boxes, which enables traceability of every extracted value back to the original image or text.
In production deployments Indico has published customer outcomes such as SOV and loss run processing under 30 seconds for configured flows, demonstrating the throughput and latency achievable when extraction agents and OCR presets are tuned for the LOB. The platform’s Agent Gallery contains insurance tuned agents for rapid bootstrap of common document types, reducing pilot configuration time, while versioning and integrated testing ensure repeatable results as extraction rules evolve. Extracted outputs are delivered as structured JSON with normalized value mappings, which supports deterministic downstream decision logic and simplifies field mapping into policy or claims systems.
What measurable accuracy and latency metrics does Indico publish for underwriting triage workflows?
Indico’s public materials include quantified production outcomes such as SOV and loss run processing in under 30 seconds, a customer reported workflow achieving 91 percent accuracy with zero human intervention, and multiple case studies reporting reductions in submission processing time of 70 to 85 percent which provide practical benchmarks for underwriting ops. For model selection and performance planning Indico publishes an LLM benchmarking resource that reports accuracy, latency and cost tradeoffs across models for document understanding tasks, enabling quantitative comparison when architecting extraction and classification agents. The platform provides field level confidence scores and token bounding boxes with each extracted value, which support automated acceptance thresholds and allow underwriting ops to measure precision and recall for specific key fields such as total insured value, occupancy, and deductibles. Configurable OCR presets enable teams to balance latency and accuracy operationally, for example selecting a fast preset for high volume, low complexity forms while using a detailed preset for loss runs requiring table fidelity.
Agent Studio’s integrated testing and versioning capture model versions and evaluation results, which supports tracking of performance metrics over time and formal acceptance criteria for pilots. Customer reported time to value metrics, such as reduction from two hours to 40 seconds for a submission flow, provide operational reference points for setting throughput and latency SLAs in production. Combined, the public metrics and benchmarking tools enable underwriting operations to quantify expected automation rates, target accuracy thresholds, and average end to end latency for triage workflows.
How does Indico integrate with underwriting core systems and what integration patterns and accelerators are available?
Indico exposes integration surfaces including REST APIs, GraphQL endpoints, and SDKs for common languages, alongside webhook and publish subscribe push models to enable real time transfer of extracted data and routing decisions into middleware or core systems. The company publishes prebuilt connectors for Outlook, SharePoint and Salesforce which simplify ingestion and metadata preservation for broker email flows, while the Guidewire ready accelerators provide field mapping templates into PolicyCenter and ClaimCenter to expedite integration with Guidewire customers.
Agent orchestration supports conditional routing and environment promotion, which enables development, test and production pipelines for integrations and ensures deterministic behavior when promoting mapping logic into policy systems. The platform’s structured JSON outputs and normalized value mappings are designed for straightforward field mapping into policy records, and the provenance metadata accompanying each field supports reconciliation and automated back sync workflows when human review edits are applied. Integration artifacts available for procurement typically include mapping templates, webhook patterns, and examples of middleware integration, which accelerate connector build and reduce custom transformation effort.
The Agent Gallery and Agent Studio reduce integration scope by providing insurance tuned agents that output normalized policy fields, which shortens time to usable outputs for underwriters. Together these patterns support both synchronous API driven lookups and asynchronous event driven ingestion, enabling integration strategies aligned to existing underwriting system topologies.
What pilot scope and acceptance criteria should underwriting operations use to validate Indico for production triage?
A robust pilot scope should include a representative sample of broker submissions and document types mapped to the target lines of business, a defined volume over a fixed period to exercise throughput, specific acceptance thresholds such as extraction accuracy greater than or equal to 90 percent for key underwriting fields, average end to end latency under 30 seconds for SOV and loss run flows, and straight through processing rate improvement targets of 70 percent or more consistent with published customer outcomes.
The pilot should configure OCR presets and Extraction Agents via Agent Studio to demonstrate latency versus accuracy tradeoffs, capture field level confidence and token bounding boxes, and exercise the platform’s LLM benchmarking to select appropriate model stacks for extraction and classification. Integration acceptance criteria should include successful field mapping into a test instance of PolicyCenter or other core systems using published accelerators or APIs, verification of webhook or push notifications for routing decisions, and confirmation of back sync behavior for corrected fields. Governance validation should require exportable provenance logs that show source page, token bounding boxes, model version and timestamped human edits, this evidence will support auditability and model governance reviews. Operational success metrics for the pilot should measure reduction in average underwriter review time per submission, uplift in throughput measured in submissions per hour, and financial proxies such as time to quote reduction and estimated FTE effort reclaimed, these metrics align to the customer case outcomes Indico has published. The pilot timeline should allocate defined periods for configuration, iterative tuning using integrated testing and versioning, and a cutover window to validate production behavior in a controlled environment using the platform’s environment promotion capabilities.
