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Building AI-Powered Solar Prospecting With Automated Lead Generation For SunSniffer | Case Study
Industry
Renewable Energy
Target Market
Germany
Project
Internal Portal For Lead Generation & Outreach
Key Outcomes
90% reduction in manual research time | Scalable lead generation | Personalized outreach campaigns
Services
Ideation, Design, Development, and Deployment
User Interface Snapshots
About Our Client
SunSniffer GmbH & Co. KG is a German-based solar technology company headquartered in Nürnberg. The company specializes in AI-powered photovoltaic (PV) plant monitoring and optimization solutions, providing module-level monitoring systems that use artificial intelligence and precise sensor technology to detect issues, optimize performance, and reduce O&M costs for solar installations. and help asset managers and operators improve energy yield and reduce operations and maintenance costs
With their patented technology, SunSniffer helps asset managers and operators improve energy yield and reduce operations and maintenance costs, achieving maximum yield from their solar plants through automated analysis and actionable insights at just 1 cent per watt peak.
As their core operations began to scale, their sales and lead generation processes remained manual and difficult to scale. Identifying buildings with strong solar potential, researching decision-makers, and running outreach campaigns required significant manual effort, limiting growth despite strong market demand.
To support expansion, SunSniffer needed a data-driven, automated sales intelligence platform capable of identifying high-potential solar leads, generating personalized proposals, and running scalable outreach campaigns.
What Were The Business Challenges
SunSniffer’s sales team encountered several operational bottlenecks that restricted scalability and conversion efficiency.
Some of the key challenges include:
- Manual process for identifying buildings suitable for solar installations
- Time-intensive research to find business contact details and decision makers
- Generic outreach campaigns with low engagement and conversion rates
- Inability to scale lead generation efforts effectively across cities and regions
- Lack of data-driven insights into rooftop solar potential by location
- Inefficient follow-up processes for outbound email campaigns
- No centralized system for managing leads, campaigns, and responses
- Difficulty creating personalized, data-rich proposals for potential solar installations
How We Solved These Challenges
Quixta designed and built a centralized AI-driven sales intelligence platform that automated lead discovery, qualification, proposal creation, and outreach, allowing SunSniffer to scale sales without increasing manual effort.
Key solution components included:
- Integrated Google Solar API to identify buildings with high solar installation potential based on location, rooftop area, sunshine hours, and energy savings data
- Built automated web scraping tools using Apify.com to collect verified business information from Google My Business and LinkedIn
- Implemented Snov.io integration to identify and verify email addresses of key decision makers in target businesses
- Developed an automated proposal generation system by creating customized PDF proposals using solar potential data and business information
- Integrated Instantly.ai for sophisticated cold email campaign management with sequences and follow-ups
- Leveraged OpenAI to generate personalized email outreach content and sales sequences tailored to each prospect
- Implemented Emailtree to analyze incoming emails and automate contextual and personalized responses
- Built a centralized lead and campaign management dashboard for tracking performance and engagement
- Developed modular frontend interfaces using ReactJS and VueJS to ensure a smooth, scalable user experience
What Was Our Process
Step 01
Discovery & Sales Workflow Mapping
Solar lead qualification logic, outreach workflows, and automation opportunities
Step 02
Data Architecture & Integration Planning
Solar data ingestion, scraping pipelines, AI processing, and system orchestration
Step 03
AI & Automation Design
Personalized content generation, proposal logic, and follow-up automation
Step 04
Platform Development
Lead discovery, proposal engine, campaign management, and analytics
Step 05
Testing & Optimization
Data accuracy checks, email deliverability validation, and workflow tuning
Step 06
Deployment & Scaling
Production rollout with support for multi-city and regional expansion
What Were The Results
- The AI-powered sales platform delivered measurable improvements across lead generation, efficiency, and scalability.
Key achievements:- Automated identification of solar-potential buildings across entire cities and regions
- 90% reduction in manual research time for lead qualification
- Personalized outreach campaigns with data-driven solar proposals for each prospect
- Scalable lead generation supporting thousands of potential customers simultaneously
- Improved response rates and engagement through AI-powered and contextually aware email sequences
- Centralized visibility into campaign performance and lead quality through advanced analytics
- End-to-end sales funnel automation from prospect discovery and identification to engagement and conversion
- Automated follow-ups, ensuring no leads were missed
What Our Clients Say About Our Work?
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Program Director,
Tech Stack Used
Payment Gateway
Next.js 14
Backend
Django
API
Google Solar API
API
LinkedIn API
API
Next.Js API Route
Why Choose Quixta For Your Automated Lead Generation Platform
This project reflects Quixta’s strength in building practical AI systems that drive real business outcomes, not experimental or demo-only tools.
1. Proven experience integrating AI with real-world, imperfect data sources such as solar potential APIs, geospatial datasets, and large business directories — and making them usable at scale.
2. Strong capability in end-to-end data engineering, from data ingestion and normalization to AI-driven scoring, segmentation, and automation logic.
3. Ability to combine AI automation, search, and B2B sales workflows into a single production-ready platform that supports prospect discovery, prioritization, and outreach.
4. Clear understanding of B2B growth mechanics, including lead qualification, personalization at scale, and reducing manual sales effort without sacrificing relevance.
5. Architecture built with scalability in mind, enabling seamless regional, city-level, and future national expansion without re- engineering the core system.
6. Focus on performance and reliability, ensuring fast search, accurate recommendations, and stable operations even as data volume grows.
7. Emphasis on measurable efficiency gains with faster lead discovery, reduced manual research, and improved sales throughput, rather than shipping AI features for their own sake.
Let’s Build An AI-Powered, Automated Lead Generation Platform For You
If your growth is hindered by manual research and operations, inefficient campaign management, or limited insights, Quixta can help build robust, automated, and centralized lead generation platforms that allow you to capture and convert high-potential leads with ease.
Frequently Asked Questions
How much does it cost to build an AI-powered sales intelligence platform?
- A platform combining data scraping, AI personalization, proposal automation, and campaign management typically costs €45,000–€135,000, depending on integrations, data volume, and automation depth.
How long does development usually take?
Most platforms of this complexity take 3 to 5 months, including discovery, integrations, AI workflows, development, and testing.
How accurate is the solar potential and lead data?
Solar insights are sourced directly from Google Solar API, while business and contact data are collected and validated through multiple trusted sources to improve accuracy and relevance.
Can this platform scale to new regions or markets?
Yes. The system is designed to support multi-city and multi-region expansion with minimal configuration changes.
Is the platform customizable for other industries?
Absolutely. While this implementation focused on solar energy, the underlying architecture can be adapted for real estate, energy, logistics, or other B2B sales use cases.
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