Google for Startups Accelerator –Application
1. Applicant Contact Information
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What is your full name (first and surname)?
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Your Business/Company Email Address
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What is your role?
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Preferred Contact Number
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Contact Number Type
4. Additional Information
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Why are you interested in joining this program? How can Google help?
Response:
Nearly Free Energy builds community-owned microgrids to deliver affordable, reliable electricity in underserved communities. Our mission is to lower energy costs while improving resilience through decentralized systems.
As we scale beyond pilots, we are increasingly using software and AI for distribution, forecasting, billing, and reliability. Google can help us accelerate this by enabling scalable microgrid OS development on GCP with advanced analytics and AI.
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How did you hear about this program?
Response:
LinkedIn startup and AI founder community.
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If selected, are you interested in participating in possible interviews with local press outlets, as requested?
Response:
Yes.
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Please list any past accelerator or startup program participation.
Response:
Please list links to any press coverage, awards or nominations.
n/a
https://enaccess.org/open-source-energy-access-community-showcase-with-aaron-tushabe/
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Do you want to receive updates or communication from the Accelerator program about other programs within Google?
Response:
Yes.
5. AI & Technology
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How is your company primarily leveraging AI?
Response:
We are building AI as an intelligent grid operator for our microgrids—optimizing distribution, forecasting demand, managing battery dispatch, and detecting anomalies. We are also developing agents to automate support, billing, and outage notifications.
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What are the key 1–3 challenges that your company is facing in adopting AI?
Response:
(1) Limited high-quality real-time energy data; (2) Integrating AI with physical infrastructure and edge devices; (3) Cost-efficient deployment in low-resource environments.
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What kind of data does your AI model use?
Response:
Energy consumption data; meter readings; system performance data; environmental and load patterns.
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What data do you use as part of your AI solution?
Response:
Smart meter data, usage logs, battery and solar performance data, and system telemetry.
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How would you categorize your product?
Response:
AI-enabled energy infrastructure platform.
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Do you have a dedicated AI team?
Response:
Cross-functional team with growing AI specialization.
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In 2 sentences, explain what problem you are solving with AI.
Response:
Energy systems lack real-time intelligence to manage distributed supply, storage, and demand, leading to outages and inefficiency. We use AI as a grid operator to optimize distribution, forecast demand, manage storage, and automate customer interactions.
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AI maturity
Response:
Early production-stage AI with live data pipelines and initial models in deployed microgrids, transitioning toward autonomous operations.
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What kind of AI does your product use?
Response:
Currently AI-assisted development; roadmap includes predictive analytics, time-series forecasting, and optimization models for demand, dispatch, and anomaly detection.
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Cloud platform
Response:
Google Cloud Platform (GCP).
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Which Google products are you using?
Response:
GCP (Compute Engine, Cloud Run, Cloud Storage) and BigQuery.
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Do you use AI/ML today?
Response:
Yes—AI-assisted development and early data-driven monitoring, with planned rollout of forecasting, optimization, and automated operations.
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System architecture
Response:
Edge devices (smart meters via RS-485/Modbus) feed data to local controllers, then to GCP (Cloud Run, Storage). BigQuery supports analytics. We are adding AI for forecasting, anomaly detection, and optimization within a modular microgrid OS.
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Accelerator goal
Response:
Build an AI-driven microgrid OS that autonomously balances supply/demand, forecasts load, optimizes battery use, and manages operations. This includes agent workflows for forecasting, anomaly response, billing, support, and outage alerts, targeting improvements in uptime, cost/kWh, and customer experience.
6. Traction & Financials
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Investors
Response:
Founder, friends, and family.
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MRR
Response:
~$120–$150 MRR from a live pilot (~UGX 450,000–550,000/month), with expansion pipeline.
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Revenue source
Response:
Electricity sales (NFE-owned) plus upcoming SaaS fees and revenue share from partner-owned microgrids.
7. Product & Market
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Stage
Response:
Live pilot with revenue (10 customers) and active expansion pipeline.
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Customer
Response:
Primary: developers, landlords, and community operators. End users: residents benefiting from reliable power, automated billing, and improved service.
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Customers now
Response:
10 paying customers on a live pilot microgrid, with expansion pipeline.
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Business model
Response:
(1) NFE-owned: revenue from electricity sales; (2) Partner-owned: SaaS fee per connection + % of energy sales; plus maintenance and value-added services.
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Industry
Response:
Energy / Climate Tech
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Verticals
Response:
Climate tech; energy infrastructure; smart grids.
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Company description
Response:
Nearly Free Energy advances energy resilience, reliability, and abundance through community-owned solar and battery-backed smart microgrids. Grid-connected systems improve stability by managing peak load.
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Problem
Response:
Electricity is unreliable, costly, and lacks real-time intelligence. Peak demand strains grids, while communities lack local control.
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Solution
Response:
Grid-connected, community-owned microgrids with an AI-driven OS that optimizes distribution, manages peak load, automates operations, and improves reliability and cost.
8. Team
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Full-time founders
Response:
1
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Founder
Response: Aaron Tushabe – Co Founder
Hillary Arinda - Co founder
Dansturn Kimbowa - Co Founder
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Employees
Response: 2
9. Uploads
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Pitch deck
Response:
[]NFE Pitch - Funders.pdf
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Links
Response:
N/A
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Photos
PXL_20251217_100220589~2.jpg
[PENDING]
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Logo
Response:
NFE site logo.png
10. Consent
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Submit
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Communications
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