In the world of AI-powered property assessment, speed is an important part of the headline. The faster the output, the smarter the system—at least, that’s how much of the industry sees it. But from the start at Spotr, we’ve believed that speed without structure is a shortcut to risk. Instead of chasing instant outputs, we focus on verifiable ones—because in insurance and compliance, polished guesses aren’t good enough.
We’ve seen firsthand how confident AI can be, and how confidently wrong. Reports based on the wrong building. Descriptions of details that don’t exist. These aren’t just glitches, they’re liabilities.
That’s why we designed Spotr to be accountable. To do more than generate answers—to generate trust.
We call it Slow AI. Not because it’s slow to respond, but because it’s built to be right. Here’s why we believe this focus on accountability is the future of responsible AI in real estate.
The illusion of instant AI
With the rise of powerful LLMs like ChatGPT, Gemini, and Claude, we’re witnessing a fundamental shift in the computer vision landscape. Traditional vision models required months of training and iteration to detect specific features. Today, LLMs can reason through problems they’ve never been explicitly trained on.
Even better: LLMs don’t just identify features, they understand context. Instead of classifying "roof tiles", they describe a “Victorian-era property with original clay tiles showing typical weathering.” That opens up fascinating possibilities. Feed an LLM a few images, and it might just generate a building report on its own.
Right?
Not quite.
It’s tempting to think generating a property report is as simple as entering an address into a chatbot. But real-world data is messy. Multiple buildings can share a single address. Parcels often contain several buildings. And raw images without context or structure are more likely to confuse than clarify.
We learned this first-hand. Early experiments with single-shot LLM pipelines produced beautiful reports—about the wrong buildings. Our system could generate confident assessments of properties that didn’t exist, or include details from a neighbouring house. And because the outputs looked polished, the errors were even harder to spot.
The issue wasn’t intelligence. It was structure. LLMs are excellent at synthesizing information, but poor at verifying it. Feed them ambiguous data and they'll confidently generate ambiguous conclusions.
That’s why Spotr reports don’t start with a prompt. They start with a process.
Context and structure as the starting point
From day one, we’ve built our system around a simple idea: if it’s going to be used to make real decisions, it has to be right.
That’s why we didn’t just aim to generate property reports—we designed a process to validate every piece of data along the way. From input to insight, each step adds structure, context, and safeguards to ensure the result isn’t just fast, but also trustworthy.
Yes, the output still arrives in minutes. But it’s backed by layers of validation and human oversight. That’s what makes Spotr different.
What happens behind the scenes - in 2 minutes
Instead of a single LLM prompt, our multi-agent pipeline carefully constructs context around every property—turning messy real-world data into clear, verified insights.
🔍 Step 1: Input validation
Every report begins with a user-provided address. But as anyone working with real estate data knows: addresses are rarely clean. Typos, missing house numbers, legacy formats — small issues like these can derail an automated flow. So we start by expanding and validating the input, automatically correcting errors and ambiguities to ensure we have a solid foundation before moving forward.
🗺️ Step 2: Geocoding and parcel matching
Once the input is validated, we convert the address into precise geographic coordinates. But that’s not enough. We also match it to cadastral data to find the right plot, especially important in urban areas or mixed-use developments where one address might cover several buildings or multiple sub-units. By anchoring our analysis in authoritative parcel data, we make sure we’re looking at the right property - not the one next door.
🏢 Step 3: Building detection and image selection
Many properties contain multiple buildings, garages, or annexes. Based on the intended use of the report, we define which buildings should be included, and which should be left out. With a detailed footprint for each relevant building, we curate the clearest, most recent, least obstructed images from multiple sources. On those images, we isolate the main building and remove noise such as cars, trees, or adjacent buildings.This ensures the analysis is visually precise and contextually accurate.
📸 Step 4: Image-based analysis
With the correct building selected, we run a series of computer vision models to extract real, visual data. We detect and classify building elements like roof type, dormers, cladding materials, solar panels and visible wear and tear. These details become structured, machine-readable facts, giving the LLM the exact context it needs to reason, describe, and report accurately.
💬 Step 5: LLM based report generation
Instead of guessing, the LLM uses the structured data to generate descriptions, risk indicators, or regulatory flags with clarity and consistency. This reduces hallucinations, and ensures consistency across thousands of reports.
✅ The final step: Human intervention where it's needed
The final piece? A person.
Even with a robust AI pipeline, we recognize that human expertise is still essential, especially when reports impact financial decisions, compliance, or risk. That’s why we developed report-specific workflows that bring experts into the loop at key moments. Each report includes checkpoints where experts can confirm detected elements, adjust observations, and add notes based on their knowledge of the building. These aren’t open-ended edits, they’re structured interactions designed to make verification easy and consistent. The result is a reporting process that combines automation with human judgment, leading to accurate, trustworthy outputs.
Why we call this Slow AI
“Slow” doesn’t mean inefficient. It means deliberate. Verified. Trustworthy.
When someone opens a Spotr report, they’re not just seeing a summary. They’re seeing a chain of evidence: input data, transformation steps, confidence scores, and expert sign-offs. This is why our customers—insurers, housing associations, local governments—trust Spotr in their underwriting, compliance, and valuation workflows.
We believe the future of AI in property assessment—and beyond—isn’t just about better models. It’s about better systems. Systems that can show their work, admit uncertainty, and invite expert input when needed.
Curious to see our AI in action?
Spotr Reports is launching in beta soon, starting with use cases like flora & fauna assessments and rebuild valuations. Join the waitlist and we’ll walk you through every step, from input to insight.
Because the next era of AI isn’t about speed.
It’s about knowing you’re right.
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