The Challenge
Home upgrade decisions are among the most complex consumer choices — shaped by house type, budget, local subsidies, and expected energy savings. Prets recognized that guiding homeowners through this process required far more than a static recommendation engine. They needed an AI system that could reason, personalize, and proactively navigate users through a highly individualized, multi-step decision journey. Three core challenges stood in the way:
- Reasoning across complex, diverse data: The system needed to connect and reason across structured user data, energy models, regulatory content, and financial information simultaneously — something traditional tools were not equipped to handle.
- Replicating human advisory expertise: Generic automation couldn’t capture the nuance and intuition of a human advisor. The system needed to adapt dynamically to each homeowner’s unique situation and guide them proactively, not just respond reactively.
- Navigating a multi-step decision journey: A single interaction wasn’t enough. The AI needed to accompany users across multiple touchpoints, maintaining context and continuity throughout a complex, personalized decision process.
Our Approach
Following a design sprint to define the architecture and identify the right intervention points across the customer journey, Enjins collaborated with Prets to design and build a production-ready multi-agent AI system that acts like a team of proactive experts.
- Multi-Agent Orchestration: Agents were embedded throughout the customer journey — not as simple chatbots, but as autonomous collaborators that trigger the right actions and guidance at the right moment, adapting dynamically to each homeowner’s context.
- Deep Platform Integration: We built the underlying AI and data infrastructure, connecting structured user data, energy consumption models, regulatory content, and analytics. LLMOps and retrieval-augmented generation enabled agents to reason dynamically across all inputs in real time.
- Team Enablement: We onboarded and trained Prets’ internal data and AI team, equipping them with the tools and expertise needed to scale and evolve the system independently after delivery.
