How Agentic RAG Transformed a 30-Year Home Inspection Business

At Phazur Labs, we build agentic systems that go beyond simple AI chat. In this blog, we explore why Amazon Bedrock is becoming our go-to foundation for scalable, secure generative AI. From multi-model flexibility to built-in RAG and enterprise-grade privacy, Bedrock enables us to create smarter, more adaptable systems—without the usual infrastructure friction. Learn how we’re turning AI into real operational intelligence.

Design trust isn’t just about how things look—it’s how users feel while navigating your product. In this post, Phazur Labs breaks down what trustworthy UX actually means, how to measure it, and the subtle design choices that make or break user confidence. Learn how we identify friction, build behavioral clarity, and turn hesitation into high-converting flow through our UX Trust Audits.

A Hidden Goldmine in Public Records Across the United States, local governments mandate annual septic system inspections to ensure environmental safety and compliance. These inspection reports, often 20 to 30 pages long, are a matter of public record. Hidden within them are crucial insights—details on system health, maintenance history, homeowner contact information, and much more. At Phazur Labs, we recognized an opportunity to unlock this dormant data. Partnering with RoadRunner Septic Services, we set out to create a solution that would turn this underutilized trove of information into a scalable, AI-powered lead generation engine. Our innovation leverages Agentic Retrieval-Augmented Generation (RAG), enabling septic service providers to gain insight, predict maintenance needs, and reach customers proactively—before problems arise. The Problem: Manual Mining Is Expensive and Inefficient Before implementing AI, the traditional approach to mining these public inspection reports was entirely manual. It involved downloading PDFs, scanning each page, extracting relevant information by hand, and entering it into spreadsheets. This method was inefficient and unsustainable: Volume Overload : Thousands of reports published annually per county Time Intensive : Each report took 1–2 hours to process manually Error-Prone : Human error during extraction or transcription Missed Opportunities : Lack of timely outreach meant lost leads In this model, a single data entry assistant might only process 5–6 reports per day—far too slow to scale operations or compete effectively. The Innovation: Agentic RAG for Septic Intelligence To solve this problem, we applied Agentic RAG: a powerful AI architecture that combines natural language reasoning with real-time data retrieval and autonomous behavior. What Is Agentic RAG? Retrieval-Augmented Generation (RAG) connects an LLM to a vector database, allowing it to "retrieve" context before generating a response. Agentic behavior gives the AI autonomy to complete multi-step tasks without constant human input. Together, this allows the AI to: Read and understand scanned inspection documents Extract key insights from unstructured text Take action (e.g., send marketing letters or schedule follow-ups) Key Features Context-Aware Retrieval: Understands tank model, make, sludge levels, and homeowner data Predictive Action : Flags systems approaching maintenance thresholds Automated Communication : Generates letters, emails, or text messages with targeted offers How It Works: From PDF to Actionable Insight Our system transforms the septic inspection workflow through four core stages: Step 1: OCR + Vectorization Many reports are poorly scanned or handwritten. We employ Optical Character Recognition (OCR) to clean, extract, and normalize this data. The resulting text is broken into chunks and converted into embeddings (vectorized representations), making it queryable by GenAI. Step 2: Metadata Extraction During the vectorization process, we extract structured metadata such as: Homeowner name Address and parcel data Septic tank size and model Inspection company Sludge and scum levels Last service date This metadata is stored in a relational database (AWS Aurora), while the full text remains searchable in Pinecone , our vector database. Step 3: Dual Database Model Pinecone enables natural language GenAI queries across unstructured text. Aurora RDS supports SQL queries over structured fields, ideal for bulk reporting and dashboards. This hybrid setup gives us the best of both worlds: deep semantic search and traditional reporting. Step 4: Autonomous Outreach Once the system flags a tank as needing maintenance, it can auto-generate personalized: Flyers Marketing letters SMS alerts Emails These messages are timed to reach the homeowner when they are most likely to need service. Real Impact: From 6 Reports a Day to 1,000+ The transformation is dramatic: Pre-AI : 1–2 hours per report, maxing out at ~30 reports per week per analyst Post-AI : Thousands of reports processed per week ROI Modeled Average service call value: $500 Targeted outreach boosts conversion by 3–5x over traditional ads Reduced staff hours, fewer errors, and better timing This creates a scalable revenue stream for RoadRunner Septic, positioning them far ahead of competitors relying on reactive marketing. Competitive Advantage Speed: Act before a homeowner realizes they need service Precision : Hyper-targeted outreach based on real system conditions Trust : Personalized, helpful messages create higher conversion This isn't just automation—it's anticipation. Use Cases Beyond Septic Services Agentic RAG isn't just for septic inspections. The same framework can be adapted to: Government Compliance : Auto-flagging overdue permits or inspections Legal Discovery : Summarizing long case files and flagging inconsistencies Real Estate : Pulling highlights from home inspection reports and appraisals Any business reliant on public documents or internal forms can benefit from this approach. Why This Approach Wins Our solution wins because it delivers: Human-Centered Interface A simple dashboard with a search field and prompts Users can "chat" with each permit, ask follow-up questions, and generate outputs Transparent Outputs Metadata and summaries are shown clearly Users can trace AI-generated content back to original document pages Automation + Flexibility Daily cron jobs load new reports from the Comal County site Manual upload allows for ad hoc document ingestion System is extensible to other counties or industries Built with Flutter Enables both desktop and mobile access Quick deployment, scalable performance The Future: Smarter Local Services Through AI We believe the future of local services is predictive, autonomous, and human-aligned. Every small business sits on a goldmine of structured or semi-structured data. Whether it’s forms, PDFs, contracts, or inspection notes, that information can be turned into: Leads Insights Actions With Agentic RAG, these organizations can: Respond to customer needs before they’re voiced Build systems that improve with time and feedback Focus on service delivery instead of paperwork This isn’t just a tech story. It’s a business transformation model. Build or Partner with us At Phazur Labs, we don't just build AI. We help businesses unlock the power of their own data. If you're a: Service provider Civic agency Industry handling high volumes of documents …then you're sitting on untapped potential. Let’s build your AI assistant together.

Not all AI is helpful—and smarter doesn’t always mean better. In this blog, we cut through the noise to explore why so many AI tools fail to serve real users, and how Phazur Labs builds AI that actually helps. From clarity and control to measurable impact, discover what human-centered AI really looks like—and why it’s time to build tech that delivers, not just dazzles.

RAG (Retrieval-Augmented Generation) is more than a buzzword—it’s a foundational shift in how enterprise AI gets real work done. At Phazur Labs, we take RAG beyond passive Q&A into agentic territory: smart, context-aware systems that retrieve, reason, and act. In this blog, we break down what agentic RAG really means, how it transforms workflows, and why the future of AI is quiet, contextual, and built for action.