Zero downtime migration
AI and Data Innovation
OIP Insurtech combines in-depth insurance expertise with advanced AI and data engineering to deliver tangible outcomes: faster quoting, sharper decisions, and more efficient operations.
in-appetite rate
0
%
faster triage
0
%
ETL runtime reduction
0
%
The future of insurance runs on AI and clean data. But first, your systems need to speak the same language. We help you get there by combining:
Automation that mimics your underwriter's workflow
Custom AI models built for your lines of business
Data platforms that make insights easy to access and act on
What we build
What we build
- AI Agents and Intelligent Document Processing (Bound AI)
- Custom ML and Gen AI Models
- Data Engineering and Advanced Analytics
Built for insurance. Trained by underwriters.
Most AI tools fail because they're built in a vacuum. We embedded insurance logic directly into Bound AI so it doesn't just scan PDFs, it understands what matters and what's missing.
It Can
Extract and structure data from submissions, loss runs, policies, and inspection reports
Validate the information against rules and guidelines
Flag inconsistencies and incomplete files
Feed clean data directly into your policy admin or CRM systems
Results That Speak in
Multiples
Client Large E&S Wholesaler
Problem Submission backlog, slower market reach, inconsistent clearance and unpredictable turnaround
Solution BoundAI loop deployed for real-time intake, extraction, and submission clearance decisions
Impact
- Cleared 69% of new submissions with zero human touch
- Cut average submission processing time from 3.5 hours to 4 minutes
- Saved ~1,200+ underwriter hours per month
When should you
use Bound AI?
You're buried in PDFs and emails
Your team spends hours rekeying submission data
You need to scale fast without growing headcount
You want audit-ready results and full traceability
What if your team didn’t have to handle the busywork?
LLMs are easy to drop into a workflow. Real results aren't.
We design, train, and deploy insurance-specific ML and GenAI models around your actual workflows, data, and risk appetite.
We build and train models for
Submission Clearance Automate triage, validate submission docs, and pre-check risk appetite
Underwriting Helpers
Use GenAI to summarize large packets (e.g., SOVs, policies, loss runs) and validate binder vs quote
Use GenAI to summarize large packets (e.g., SOVs, policies, loss runs) and validate binder vs quote
LOB Classification / Routing Models
Train on submission metadata + past placements to improve quote routing accuracy
Train on submission metadata + past placements to improve quote routing accuracy
Claims FNOL Assistants
Extract, classify, and route FNOL documents using GenAI
Extract, classify, and route FNOL documents using GenAI
GenAI Q&A + Summarizers
Assist underwriters/claims analysts with context-aware agents trained on their own docs
Assist underwriters/claims analysts with context-aware agents trained on their own docs
Real-world
results
Client
Specialty MGA
Specialty MGA
Problem
Underwriters were manually classifying business lines, which slowed quote speed and created inconsistencies.
Underwriters were manually classifying business lines, which slowed quote speed and created inconsistencies.
Solution
We built a custom LOB classification model trained on historical submissions.
We built a custom LOB classification model trained on historical submissions.
Impact
- 96% model accuracy
- 52% reduction in misclassified submissions
- 3x faster quote generation and less rework
Why custom
models win
Tailored to your underwriting appetite and risk criteria
Continuous learning from your own portfolio
Competitive edge through differentiated analytics
Complete control over inputs, outputs, and thresholds
AI shouldn't be a black box
Insurance runs on data, but only if you can trust it, structure it, and surface it in time.
From ingestion to insight, we design, maintain, and evolve your data pipelines for performance, scalability, and insurance-grade reliability.
Our services
include
Data lakes and warehousing
Centralize structured and unstructured data
Centralize structured and unstructured data
ETL and ELT pipelines
Clean, transform, and load data across systems
Clean, transform, and load data across systems
Data quality and governance
Build trust with rule-based validation
Build trust with rule-based validation
Always-On Reporting
Ensure your dashboards are always up to date, driven by continuous data flows and short refresh periods.
Ensure your dashboards are always up to date, driven by continuous data flows and short refresh periods.
Data cataloging and lineage
Understand where your data comes from and where it goes
Understand where your data comes from and where it goes
Analytics that drive
decisions
Executive dashboards
Track bind rates, loss ratios, and producer performance
Underwriting insights
Analyze hit ratios, submissions, and premium or TIV trends
Claims intelligence
Identify frequency/severity trends, reserve adequacy, and closure rates
Operations monitoring
Surface cycle time blockers and productivity gaps
Real-world
results
Client
Mid-sized MGA
Problem
Submission and quote data was spread across carrier portals, internal Excel sheets, and AMS exports, making reporting a difficult, long, and manual process.
Solution
We built a centralized data platform with an automated ingestion workflow.
Impact
Consolidated multiple data sources into a single source of truth
Automated dashboards for underwriting and sales that lead to a significant improvement in the turnaround time
Make data your competitive edge
Proof in Practice
Custom ML and GenAI Models
A mid-size MGA writing specialty business was overwhelmed with broker submissions, forcing underwriters to review too many risks that rarely bound. Nearly 55% were out-of-appetite, and static PDF guidelines made consistency impossible. By unifying three years of submission, quote, and bind data, our team built custom ML models that scored each submission against actual binding patterns. The scores, embedded directly into the underwriting workbench, gave underwriters real-time triage guidance and leadership new distribution intelligence.
The results? In-appetite rates jumped to 69%, average review time per submission was cut in half, and hit ratios climbed from 18% to 32%. Brokers even began improving submission quality thanks to new efficiency rankings.
“We went from chasing everything to focusing on what actually fits. The model saves time and gives us confidence we’re spending it where it matters.” – Head of Underwriting, Specialty MGA
Data Engineering and Advanced Analytics
A specialty market intermediary modernized its data foundation by migrating from a legacy Oracle database to SQL Server. OIP Insurtech designed custom ETL pipelines, optimized schemas for analytics, and ensured seamless continuity across APIs and utilities. The cutover was completed with zero downtime, reducing ETL runtimes by 35% and eliminating long-standing data inconsistencies.
“Now our data environment is faster, cleaner, and analytics-ready. We can run advanced queries and integrate BI tools directly without the bottlenecks we used to face.” – Head of Operations, Specialty Intermediary
Real clients, real results
83% faster submission-to-quote cycle by pre-clearing ACORDs, loss runs and SOVs before a human sees them
2.3 fewer FTEs per 10K submissions - without reducing service levels or speed
Zero-touch approvals for up to 69% of standard submissions, with embedded business logic checks
Why it works: Insurance-first AI
We don’t chase buzzwords. We build AI solutions grounded in underwriting logic and carrier guidelines.
That’s why our document agents know what to look for and why our models outperform off-the-shelf options.
That’s why our document agents know what to look for and why our models outperform off-the-shelf options.
