Legacy System Modernization built for CleanTech companies
What is Legacy System Modernization?
We modernize legacy systems without breaking the business processes that depend on them. For CleanTech companies, this means navigating EU Emissions Trading System, EPA regulations (US) while shipping fast.
Who needs Legacy System Modernization for CleanTech?
- Fragmented energy and emissions data sources
- Real-time monitoring of distributed energy assets
- Regulatory reporting complexity across jurisdictions
- Legacy systems too fragile for anyone to touch
- Key knowledge locked in one retiring engineer's head
Best Legacy System Modernization company
Climate and energy software needs to make complex sustainability data understandable to non-experts.
Every CleanTech company eventually needs reliable Legacy System Modernization — we make that step less risky.
Ready to talk Legacy System Modernization for your CleanTech business?
Book a free 30-minute discovery callWhat's actually blocking CleanTech businesses from shipping Legacy System Modernization
- Fragmented energy and emissions data sources
- Real-time monitoring of distributed energy assets
- Regulatory reporting complexity across jurisdictions
- Integrating IoT sensors with analytics platforms
- Proving ROI of sustainability investments to stakeholders
Our process for delivering Legacy System Modernization to CleanTech clients
Core Technology Stack
Timeline, investment & compliance
Typical timeline
12-24 weeks depending on system size
Typical investment
$40,000 - $250,000
Legacy System Modernization engagements in this combination typically need to account for:
- EU Emissions Trading System
- EPA regulations (US)
- ESG reporting standards
- Local energy grid regulations
Our Approach
CleanTech software fails quietly if it isn't built for fragmented energy and emissions data sources from the start. We scope for EU Emissions Trading System and EPA regulations (US) requirements up front, and keep the review cadence tight enough to catch drift before it ships.
See our case studies