The statement of values sits at the center of every commercial property placement. Carriers use it to price risk, model exposures, and make underwriting decisions. When the data is incomplete or inconsistent, deals slow down and clients end up paying more than they should.
Most broker teams still handle SOV cleanup manually. Fixing addresses, filling in missing construction types, reformatting columns to match carrier requirements. It is tedious work that takes hours away from the advisory and relationship work that actually moves the needle.
SOV insurance broker tools are built to solve this. They automate cleanup, enrich records, flag inconsistencies, and get submissions to market faster. But they are not all built the same way. Some are end-to-end platforms. Others focus on a single step in the process. Picking the wrong one can create as much friction as it removes.
This guide covers five tools worth evaluating. For each, we break down what it does, where it works well, where it falls short, and what type of team it fits best.
What Makes SOV Data So Hard to Manage
SOV processing sounds simple in theory. Collect property data from the client, organize it, pass it along for modeling and placement. In practice, the data shows up messy. Clients send outdated spreadsheets with missing fields. Construction codes do not match up. Occupancy descriptions vary from location to location. Every carrier has its own formatting preferences. What should take an hour ends up taking most of the day.
The downstream cost goes beyond lost time. Inaccurate SOVs feed into unreliable catastrophe modeling, which means the pricing a client receives may not reflect their actual exposure. Errors that surface during a renewal, or after a loss event, damage credibility that takes a long time to rebuild.
That is why more broker teams are turning to purpose-built tools. Not to replace judgment, but to take the grunt work off the plate so the focus can go back to client strategy.
What to Look for When Evaluating SOV Tools
Before comparing specific products, it helps to have a framework. A few criteria consistently separate tools that deliver lasting value from those that look good in a demo and then sit unused.
Ease of use matters more than feature count. If onboarding takes weeks, or team members need technical support to run a basic submission, the tool will not stick. The best options feel intuitive from the first login and require minimal hand-holding.
Data quality and enrichment separate the good from the average. A tool that only reformats columns is solving a surface problem. One that validates geocodes, fills in missing construction details, and flags outliers is improving the underlying data quality that underwriters and cat modelers actually rely on.
Collaboration and workflow fit matter for teams where multiple people touch the same account. Version control, shared access, and progress tracking prevent duplicated effort and keep everyone working from the same source of truth.
5 SOV Tools for Insurance Brokers
1. Archipelago
Archipelago is a productivity suite designed specifically for insurance brokers. The platform is built to handle the full SOV workflow, from initial ingestion and data cleanup through enrichment, quality checks, and submission readiness. It is designed to serve both individual brokers working on a single account and team leaders managing portfolios across multiple organizations.
The platform uses automated enrichment to pull in data from industry sources, flags insured-to-value outliers, and produces data improvement recommendations in a presentation-ready format. Accounts are typically processed in under 24 hours. Archipelago is SOC 2 certified and integrates with leading risk modeling platforms including Moody's Analytics and Verisk. Pricing starts at $400 per month on a per-seat basis.
Pros: End-to-end workflow coverage, automated enrichment, SOC 2 certified, integrations with major RMIS and risk modeling systems.
Cons: Per-user annual subscription may not suit teams that only process occasional submissions. Not all casualty lines are supported.
Best fit: Broker teams that want a single platform covering the full SOV lifecycle and need data quality at scale.
2. Ping Intel
Ping Intel is a modular SOV tool built around three standalone products: SOV Fixer, Ping Data, and Ping Geocoding. Each can be used independently, which gives teams the flexibility to plug in only the piece they need rather than adopting an entire platform.
The workflow is email-based. You send an SOV file, Ping processes and cleanses it, then returns the cleaned version. An API is also available for teams that prefer programmatic access. The platform offers dozens of data enrichment integrations and stores processed data in a Snowflake-based warehouse for downstream analytics use.
Pros: Easy to get started, modular structure, API access, broad enrichment integrations, flexible for teams that only need one processing stage.
Cons: Pricing is metered by data volume, which may add up for high-volume portfolios. No built-in user interface for workflow management or analytics. SOC 2 compliance is not currently advertised.
Best fit: Teams that need a focused, low-friction data cleaning or geocoding service without committing to a full platform. Also a practical option for shops with lighter submission volumes.
3. Convr
Convr is an AI-driven underwriting platform built for commercial property and casualty insurance. SOV processing is one of its capabilities, though the platform is designed more broadly for underwriting workflow. It uses machine learning to extract and validate submission data, supports customizable workflows, and includes real-time data validation before submissions are processed.
Pros: Strong AI-driven extraction, real-time validation, customizable workflows, designed to scale for larger organizations.
Cons: Not exclusively focused on SOV processing, steeper learning curve than broker-specific tools, higher pricing that may not suit smaller teams.
Best fit: Larger insurance organizations or carriers that need SOV handling as part of a broader underwriting automation platform.
4. Scrub AI
Scrub AI is a specialist tool focused narrowly on cleaning and standardizing insurance SOV data. It is designed to integrate with existing systems and focuses on delivering clean, consistent data before it reaches the underwriting stage. The platform offers automated cleansing workflows, real-time data quality feedback, and flexible deployment options including on-premises and cloud.
Pros: Purpose-built for SOV data cleansing, integrates smoothly with existing systems, real-time feedback on data quality, flexible deployment.
Cons: Limited analytics capabilities, does not offer an end-to-end SOV management workflow, so it typically needs to be paired with other tools to cover the full submission process.
Best fit: Teams that already have a submission workflow in place and need a reliable tool to handle the data cleaning step specifically.
5. UiPath
UiPath is a robotic process automation platform that can be configured to handle SOV ingestion and processing tasks. It is not an insurance-specific tool, but its automation capabilities are flexible enough to be adapted for SOV workflows. The platform supports a low-code and no-code interface and can scale from small implementations to large enterprise deployments.
Pros: Highly customizable, extensive integration options, scales well, accessible to users with limited technical backgrounds through its low-code interface.
Cons: Requires dedicated setup and configuration for insurance-specific workflows. Ongoing maintenance can be resource-intensive. Teams without in-house technical support may find the implementation cost and complexity difficult to absorb.
Best fit: Technology-forward organizations with dedicated IT resources that want a fully customized automation solution and are comfortable with a longer implementation timeline.
Quick Comparison
| Tool | Best For | SOV-Specific | End-to-End Workflow | SOC 2 Certified |
|---|---|---|---|---|
| Archipelago | Full SOV lifecycle management | Yes | Yes | Yes |
| Ping Data Intelligence | Modular cleanup and geocoding | Yes | No | Not stated |
| Convr | Underwriting automation at scale | Partial | Partial | Not stated |
| Scrub AI | Standalone data cleansing | Yes | No | Not stated |
| UiPath | Custom RPA for tech-enabled teams | No | No | Yes |
How to Choose the Right Tool for Your Team
The comparison above gives you a starting point, but the right choice depends on how your team actually operates day to day.
If your biggest bottleneck is one specific step, such as geocoding or cleaning up messy client files, a focused tool may be all you need. Ping Data Intelligence and Scrub AI are both built for that kind of targeted use. You send the data, get cleaned data back, and move on. There is no platform to learn, no annual commitment, and setup is straightforward.
If your team juggles multiple accounts, has several people touching the same submission, and wants to track data improvements with clients throughout the process, a more complete workflow platform will save time that standalone tools cannot. In that scenario, a tool that covers ingestion through submission in one place starts to make more sense.
Also worth factoring in: how much technical support your team realistically has. General-purpose automation platforms like UiPath offer a lot of flexibility, but that flexibility comes with setup and maintenance requirements that can quickly outpace the bandwidth of a lean broker team. Purpose-built options tend to have shorter onboarding timelines and require less ongoing technical involvement.
The most reliable test is running real account data through a trial. Not a demo dataset, but an actual messy SOV from a recent renewal. Compare the output and the time it took against your current process. That single exercise will tell you more than any feature comparison chart.
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