Start your Envera journey.
Energy Intelligence
Turn meter and process data into insight you can act on.
Energy Performance
Benchmark against a baseline and see exactly where you stand.
Energy Savings
Capture the quick wins first, then the bigger ones.
Carbon Reduction
Turn those savings into a measurably smaller footprint.
Continuous Improvement
Keep the cycle running, one reporting period at a time.
A senior engineer for your industry, working on your data.
AI Engineer is built for each client: set up around your business, your sites and your industry. It works from the strongest proven engineering sources in your sector and applies that engineering to your own data, so every answer is one you can act on and defend.
- Built for you: your business, your sites, your processes, your industry
- Grounded in the strongest proven sources for your sector: DOE, LBNL, IFC, IPCC and the GHG Protocol, cited to the page
- Applies that engineering to your own readings, with the dashboard's own formulas, never a guess
- Checks the web for what changes: tariffs, fuel prices, emission factors
- Answers in seconds, in English or Arabic: the saving in EGP, kWh and tCO₂e, and who should act
Give me the three best ways to cut our energy bill, with numbers.
Estimating savings… · Searching the engineering library…
Three measures save about EGP 13.6 million a year across your sites.
| Measure | EGP / yr | tCO₂e / yr |
|---|---|---|
| Humidification fan VSDs | 8.58 M | 1,930 |
| Compressed-air leak repair | 3.75 M | 843 |
| Boiler tuning | 1.23 M | 235 |
- Weekend ultrasonic leak survey (Maintenance)
- Quote VSDs for air-washer fans and pumps (Utilities)
Sources (3): US DOE 2016 · LBNL 2010 · your dashboard, Oct 2025 to Sep 2026
Illustrative answer on a sample textile dataset.
What are you actually comparing us to?
Three real alternatives: the spreadsheet you have today, a generic BI or GHG platform you'd have to configure yourself, and Envera.
| Spreadsheet | Power BI | Envera | |
|---|---|---|---|
| Getting started | |||
| Setup time | Already there | Weeks to model your data from scratch | Days to first load, from your existing workbook |
| Who maintains it | One person, informally | Whoever built the dashboard — if they're still around | A defined migration and reconciliation process, documented |
| Your existing methodology | Hand-built, undocumented | You configure it yourself, from scratch | Migrated in and shown back to you, never replaced with a generic table |
| A new question about your data | Someone builds another sheet | Someone builds another report | Ask AI Engineer, in English or Arabic, with sources |
| Numerical integrity | |||
| Precision | Rounds before summing | Depends entirely on the model someone else built | Full double precision end to end |
| Data-quality visibility | Errors hide silently in cells | Garbage in, confident-looking chart out | Automated defect detection, with materiality |
| Standards & language | |||
| ISO 50001 / GHG Protocol / GWP-100 | Built in by hand, if at all | Generic — you configure it yourself | Built in, versioned, auditable |
| Arabic / RTL | Manual, inconsistent | Translated labels over an LTR layout, usually | Full mirrored interface, structurally CI-enforced |
| Deployment | |||
| Self-hosting | N/A | Rare — usually cloud-only SaaS | Available — see Security |