Data rescue & cleanup
Turn inconsistent spreadsheets, exports, instrument files, and hand-built processes into a documented, reproducible dataset you can trust.
- Data audit
- Cleaning pipeline
- Data dictionary
- Validated export
Data science · Databases · Automation · Internal tools
Your data should support the work—not become the work.
Orange House helps research teams, technical organizations, and growing operations replace fragile spreadsheets and manual processes with reliable databases, pipelines, dashboards, and focused software.
Practical architectureTransparent analysisMaintainable delivery
CSVAPISensorForms
Orange Housevalidate · structure · automate
DatabaseDashboardAlertsDecisions
Built for messy realityPostgreSQL-centeredMobile-friendlyDocumented & deployableNo black-box theater
Services
From scattered data to a dependable operating system.
We focus on the pieces that create leverage: trustworthy structure, repeatable processing, visible exceptions, and tools people will actually use.
Database design
Design or rebuild PostgreSQL systems that reflect how your organization actually works—without trapping critical knowledge in one person’s spreadsheet.
- Schema design
- Migration
- Role permissions
- Backup plan
Data pipelines & automation
Automate recurring imports, validation, transformations, notifications, reports, and handoffs so the process runs reliably without constant intervention.
- ETL workflows
- Scheduled jobs
- Exception alerts
- Runbooks
Dashboards & internal tools
Build focused Flask applications and decision tools that match your workflow instead of forcing your work into a generic platform.
- Web application
- Responsive UI
- Authentication
- Deployment
Sensor & research data systems
Connect instruments, devices, field teams, samples, locations, QA/QC, metadata, and alerts into a single operational system.
- Device registry
- Data ingestion
- QA rules
- Fleet health
Analysis & decision support
Create transparent analytical workflows for geospatial prioritization, monitoring design, scenario comparison, and technical reporting.
- Analysis plan
- Reproducible code
- Visualizations
- Decision brief
Our approach
Make the normal path automatic. Make exceptions visible.
The goal is not more software. It is a clearer system: known inputs, explicit validation, an authoritative database, reliable automation, and an interface organized around the next decision.
01Map the real workflow
02Find failure points and hidden rules
03Design the data model
04Automate and surface exceptions
05Deploy, document, and transfer
See it work
Interactive examples—not generic promises.
Explore small demonstrations of the kinds of data problems Orange House solves.
Interactive
Messy-data profiler
Paste a CSV and inspect shape, missing values, duplicates, inferred types, and field-level quality concerns.
Open example →InteractiveSensor QA/QC pipeline
See how transparent quality rules turn raw telemetry into reviewable flags.
Open example →InteractiveGeospatial site ranking
Adjust population, vulnerability, coverage, and logistics priorities and watch candidate sites re-rank.
Open example →ArchitectureOperational database design
A representative model connecting projects, people, devices, samples, measurements, files, and an audit trail.
Open example →WorkflowException-driven workflow
A pipeline that handles normal work silently and routes only cases requiring judgment.
Open example →Product designDecision-first dashboards
Interfaces organized around what someone needs to decide next—not how many charts fit.
Open example →A good first project
Tell us about itBring the workflow everyone complains about.
A fragile spreadsheet, a recurring manual report, an unreliable import, an instrument-data bottleneck, or a database that no longer fits the organization.