Getting insurance teams live faster requires more than generic AI

A recommended pilot scope includes ingestion of representative submission bundles (emails with attachments, SOVs, loss runs, ACORDs, scanned multi‑page PDFs and handwritten fields), daily processing of a statistically significant sample size, and end‑to‑end orchestration into the underwriting workflow so that time‑to‑first‑action and STP at both document and submission level can be measured; Indico recommends such pilot configurations as part of its FNOL and triage guidance. Duration should be 4 to 8 weeks to capture variability across submission types and to allow iterative model tuning and human review feedback loops, with an initial ingest cadence scaled to typical daily volumes for the book of business under test. Core KPIs should include field precision and recall by document type, median and 95th percentile per‑document latency, submission level STP%, underwriter time‑to‑first‑action, and FTE‑savings projection, where acceptance criteria map to business outcomes such as reduced cycle time and improved quote throughput; Indico publishes production benchmarks such as sub‑30 second processing for SOVs and loss runs that serve as pilot targets. 

Additional operational KPIs should capture provenance completeness and model version adoption for governance, and integration KPIs should validate callbacks, webhooks and back‑sync to policy systems as part of the acceptance tests. The pilot should include a predefined acceptance matrix, for example: median SOV processing under 60 seconds, per‑field precision above the targeted threshold derived from historical error rates, and submission STP improvement percentage tied to business case uplift, using Indico’s published case outcomes and implementation success metrics as comparative baselines. 

Finally, the pilot SOW should require exportable artifacts including sample provenance JSON, audit logs, and model cards to validate regulatory and governance requirements during evaluation, artifacts that Indico surfaces as part of its operational offering and review tooling.

Indico supplies a Ready for Guidewire accelerator that is available in the Guidewire Marketplace to automatically populate extracted fields into PolicyCenter, enabling rapid integration into underwriting policy workflows and reducing field mapping effort during pilots and production rollouts. 

For environments that use different middleware or policy engines, Indico exposes a comprehensive set of connectivity options including REST APIs, GraphQL sandbox endpoints, SDKs for Python Java and C#, and webhooks or SNS style push patterns to support synchronous STP flows and asynchronous case creation for human‑in‑the‑loop review. Deployment options include cloud hosted, private cloud and on‑premises or bare metal isolated deployments to satisfy data residency and enterprise controls, with role based access control, granular audit logs and end‑to‑end encryption to align with insurer security requirements. The integration approach supports both accelerated out‑of‑the‑box Guidewire population for fast time to value, and API first patterns for carriers that require custom middleware transformation and enterprise service bus orchestration, enabling deterministic callbacks and back‑sync behavior for human edits and provenance capture. 

Indico documents developer tooling and sandbox capabilities to enable solutions architects to validate integrations in a test PolicyCenter instance or equivalent staging environment prior to production cutover.

Indico returns extractions with structured JSON that includes component_results and model_results metadata, enabling traceability to token and bounding box level, and the platform records the source page, model version and timestamp for each extracted field to provide a complete provenance trail for audits. Human corrections made in the Indico Review UI are timestamped and linked to the originating model version, with revisions captured for staged retraining and governance, and these edits are exportable to support legal and regulatory review workflows. 

The platform captures granular audit logs and role based access events, enabling extraction of an audit package that demonstrates the lineage of a decision from ingestion through enrichment, agentic decision recommendations and final underwriter action, aligning with standard underwriting governance expectations. Provenance is delivered in machine readable formats suitable for ingestion into compliance systems or for attachment to policy records in PolicyCenter, and the system provides model version tracking to show which model produced a prediction at any point in time, enabling reproducible outcomes for regulatory inquiries. 

Token level confidence scores accompany extracted fields to permit thresholding rules in triage logic and to quantify human review effort, with confidence metadata available for analytics and SLA reporting. Indico documents these provenance capabilities as part of its orchestration and review tooling, enabling underwriting operations to establish audit ready processes for automated triage.

Indico’s customer stories and product materials cite SOV and loss run processing times around 30 seconds, which serve as median per‑document latency targets for pilots. Case study disclosures report implementation outcomes such as 97% implementation success rate and reductions in submission processing time in the range of 70% to 85%, offering operational improvement anchors to construct FTE‑savings and throughput models. 

Business outcome examples in published Indico materials include quantified premium growth metrics, for example a $30 million increase in quarterly premiums and reported 50% to 150% increases in net premiums associated with faster triage and quote turnaround in featured customer narratives, providing potential revenue upside assumptions for ROI scenarios. 

These benchmarks are complemented by Indico’s LLM and model benchmarking resources, which present accuracy, cost and latency comparisons designed to inform model selection for extraction and classification tasks within the triage pipeline. For operational KPIs, Indico’s platform exposes per‑field confidence scores and throughput telemetry that support construction of pilot acceptance criteria and SLAs, enabling finance and actuarial teams to model time to value and net benefit using documented case metrics as upper bound scenarios.

Indico’s platform includes the Indico Review UI that highlights source text and bounding boxes, records corrections with timestamps and associates edits with the producing model version, which feeds labeled corrections into the model lifecycle for staged retraining and continuous improvement. 

The Agentic Workflow Canvas provides visual orchestration to sequence extraction, enrichment and decisioning agents, configure branching and conditional logic, and run parallel pipelines enabling business users and solutions architects to design triage flows without low level coding. Developer tooling includes REST APIs GraphQL sandboxes and SDKs in Python Java and C#, alongside webhook patterns for asynchronous notifications, enabling integration with existing middleware and enabling deterministic automation of STP flows and case creation for human review. Model governance capabilities include model versioning, exportable model cards and provenance metadata for each extraction, which supports promotion of agents between dev test and prod environments and provides artifacts required for audit trails. The platform supports staged training workflows where labeled corrections captured during human review are incorporated into retraining pipelines, enabling measurable accuracy gains over time and documented model lineage for governance. Operational telemetry and confidence scoring enable dynamic routing policies that escalate low confidence items to human underwriters while allowing high confidence submissions to flow via STP, enabling targeted human review capacity planning and predictable scaling of underwriter workload.