What We Can Help With
Practical data engineering, integration and analytics focused on turning difficult data into something reliable, usable and valuable.
Data Cleanup
Normalize inconsistent values, repair missing or malformed information and turn difficult source data into something reliable and usable.
Data Enrichment
Add useful attributes from public, internal or geographic data sources to make existing customer data more complete and analytically useful.
Data Integration
Connect datasets that were not designed to work together and turn them into consistent analytical models.
Property & Location
Combine property, permits, insurance, geographic and environmental information at useful geographic levels.
Risk & Environmental Data
Integrate flood, weather, wildfire, climate, air quality and other public risk datasets.
SQL, Data Performance & Cost
Diagnose inefficient SQL, data pipelines, database architecture/data modeling, and improve database performance, processing time, cost and maintainability.
Public Data Acquisition
Build repeatable processes for acquiring, cleaning, retaining and integrating large public datasets.
Analytics
Turn integrated data into useful measures, comparisons, visualizations and analytical outputs that help expose patterns, risks and opportunities.
AI Workflow Automation
Use AI and automation to research, evaluate, classify and process information inside repeatable workflows, with results integrated into existing data and systems.
Problems We Solve
- Datasets that do not join cleanly.
- Incomplete, inconsistent or difficult location data.
- Internal data that is difficult to analyze or reuse.
- Public datasets that are difficult to acquire, clean or integrate.
- Multiple unrelated datasets that need to become one usable analytical model.
- Slow SQL, long-running transformations and expensive data pipelines.
- Property, insurance, climate and geographic data that needs to be connected.
- Manual workflows that could be researched, evaluated or processed using AI-assisted automation.
Send Us A Small Sample Dataset
If You have a difficult data problem, send us a small sample and tell us what You are trying to accomplish. We can work up a free demonstration showing how we would clean, enrich, integrate or analyze it before You commit to a larger project.
What a Small Data Project Can Look Like
You Send
- A small CSV, spreadsheet or data extract.
- A description of the problem.
- What You ultimately want to know or accomplish.
We Work On
- Cleaning inconsistent values.
- Missing or inconsistent geography.
- Joining difficult datasets.
- Useful data enrichment.
- Public-data integration.
- SQL or processing problems.
- Analysis and visualizations.
You Get Back
- A cleaned or enriched sample.
- A description of what was changed.
- Useful additional fields where appropriate.
- Example analysis or visualizations.
- A practical view of what could be done with the full dataset.
Analysis & Examples
Examples of the analytical datasets, integrations and workflows we build to turn disconnected source data into usable information.
These examples are built as repeatable data pipelines, not one-off analyses. Data and visualizations are refreshed automatically each day where updated source data is available.
Insurance
Insurance risk, claims triage, loss exposure and geographic blind-spot analysis using integrated public and insurance-related data.
Finance
Market, economic and event-driven analysis combining price, sentiment and external-event data to identify unusual behavior and recovery patterns.
Public Service
Community-level analysis combining housing, environmental, geographic and risk data to compare conditions across neighborhoods and ZIP codes.
Property
Property and location analysis combining housing, permits, demographics, flood, weather, environmental and loss data at city, ZIP and other geographic levels.
Sentiment
Sentiment, news and market analysis combining text scoring, trends, anomalies, source behavior and time-based patterns to uncover signals in large volumes of unstructured data.
Analytics
Data quality, completeness and cross-dataset diagnostics showing where information is missing, inconsistent or unsuitable for reliable analysis.
Have a difficult dataset?
If Your data does not join cleanly, location information is inconsistent, public data needs to be integrated, an existing pipeline is too slow or expensive, or a workflow may benefit from AI-assisted automation, send us a small sample and tell us what You are trying to accomplish.