Structuring Unstructured Data for Insurance Gen AI Excellence

Harness Your Data: Structure Unstructured Data with Knowledge Graphs to Power Insurance Gen AI for Smarter Insights, Efficient Claims, and Innovative Customer Solutions

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Benefits of using structured data in Insurance Industry

Seqtra improves the accuracy of AI agents to 95%+

Structure On-Demand

Convert unstructured claims, policies, and customer data into AI-ready formats for accurate processing.

Fits your tech stack

Integrates smoothly with insurance CRMs and underwriting systems, enhancing workflow compatibility.

Smart Risk Assessment

Assess risks for the users by assessing their relationship with the policy and risk profile

Simplified AI Compliance

Ensure your data practices align with evolving insurance industry regulations like NAIC, GDPR, and AI governance guidelines.

High Accuracy

Increase the precision of claims adjudication, fraud detection, and underwriting models with highly accurate data.

Seamless Integration

Effortlessly embed into policy management, claims processing, and underwriting platforms without disrupting operations.

Created by the team who have built products for

Created by team who have built products for

Knowledge Graph

Extract Context not Data Point

Visualize the relationships between policies, claims, regulations, and customer data through a dynamic, auto-updating knowledge graph.

Extensive Knowledge Base

Transform unstructured data into structured data instantly

Extensive Knowledge Base

Transform unstructured data into structured data instantly

Extensive Knowledge Base

Transform unstructured data into structured data instantly

All document formats

We have build multiple formats. Happy to add additional formats at request

All document formats

We have build multiple formats. Happy to add additional formats at request

All document formats

We have build multiple formats. Happy to add additional formats at request

Insurance Specific AI Model

Unlock Higher Accuracy

Unlock higher accuracy with SeqtraInsurance — a specialized model trained to extract insights from insurance policies, underwriting forms, claims reports, regulatory filings, and telematics data.

1-Click Graph Database

Bring your own agents to search any unstructured data

1-Click Graph Database

Bring your own agents to search any unstructured data

1-Click Graph Database

Bring your own agents to search any unstructured data

Document paths

Always find the right document for given context

Document paths

Always find the right document for given context

Document paths

Always find the right document for given context

Answer Generator

Your LLM Provider

User Graph Profile

Seqtra

Compliance

Tools for Regulated Insurance Flow

Every transaction automatically generates a traceability artifact, offering a complete audit trail for regulatory reviews and internal governance.

Tools and Apps

Integrate with any tool to power your workflow

Tools and Apps

Integrate with any tool to power your workflow

Tools and Apps

Integrate with any tool to power your workflow

Infractructure Agnostic

Run Seqtra API from any cloud service

Infractructure Agnostic

Run Seqtra API from any cloud service

Infractructure Agnostic

Run Seqtra API from any cloud service

Extract, classify, and structure text from diverse insurance documents with up to 95% precision

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Easy Implementation

Get started with less than 10 lines of code

Copy the API implementation code and start your first call.

import datetime
import os
import json
from omegaconf import OmegaConf

from src.seqtra_client import SeqtraClient
    
req_cfg = OmegaConf.load("./config/request.yaml")
os.makedirs("results", exist_ok=True)
seqtra = SeqtraClient(
    api_token=req_cfg.api_token, project_name=req_cfg.project_name, url=req_cfg.url,
    llm=req_cfg.llm, llm_key=req_cfg.llm_key
)
seqtra.ingest(req_cfg.file_dir)

Copy code snippet

import datetime
import os
import json
from omegaconf import OmegaConf

from src.seqtra_client import SeqtraClient
    
req_cfg = OmegaConf.load("./config/request.yaml")
os.makedirs("results", exist_ok=True)
seqtra = SeqtraClient(
    api_token=req_cfg.api_token, project_name=req_cfg.project_name, url=req_cfg.url,
    llm=req_cfg.llm, llm_key=req_cfg.llm_key
)
seqtra.ingest(req_cfg.file_dir)

Copy code snippet

SeqtraInsurance: Built for Accuracy

Structure, Load, and Automate for better risk assessment, fraud detection, customer onboarding, and regulatory improvement

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FAQs

Questions? We got answers

Check out the answers to most frequently asked questions.

How does your AI-driven API ensure over 95% accuracy

What types of unstructured data can your API handle?

Can this API integrate with existing graph databases or does it require a specific one?

How does structuring data into a graph benefit LLMs?

What is the process for implementing this API in an existing data workflow?

How does your API handle data privacy ?

How does your AI-driven API ensure over 95% accuracy

What types of unstructured data can your API handle?

Can this API integrate with existing graph databases or does it require a specific one?

How does structuring data into a graph benefit LLMs?

What is the process for implementing this API in an existing data workflow?

How does your API handle data privacy ?