Built for
Franchise Consulting Portfolio
IFPG Turned Cluttered Listings Into Verified, Confident Answers
A preprocessing pipeline strips HTML and standardizes every financial field. A verification framework confirms the number before a member sees it.
<3S RESPONSE TARGET • 600→1,000+ LISTING • STLS 1.2+ ENCRYPTED • 7-WEEK BUILD
The problem
Prospects asked simple questions, and the chatbot kept getting the numbers wrong.
Industry pain. Franchise research runs on money questions. When a chatbot pulls from messy source data, it mixes up fees and returns, and buyers stop trusting the answers.
IFPG's version. Their existing assistant read listings carrying stray HTML tags, then buckled under too much context. Similar financial fields blurred together, so members got confusing replies on details that decide purchases.
Feed It Messy Data. Get Verified Answers.
Six systems working together, from cleaning your data to training your team, so every financial answer ships verified.
Clean data in, confident answers out
A custom process strips stray HTML and standardizes financial fields from IFPG's listings, without touching the source files the website still renders.
Every answer checked before it ships
Chain-of-Thought prompting and Few-Shot examples teach the assistant to tell similar financial fields apart, then a verification check confirms the value before it answers.
A live interface built for testing
Real-time streaming responses, clear indicators for new and continuing conversations, and error handling that tells members when something interrupts the connection.
Connects to what you already run
Integrates with IFPG's existing PHP-based website and its automated daily update process, with documentation for ongoing maintenance.
Tested against your hardest questions first
A dedicated OpenAI Assistant instance replays previously problematic queries, measuring accuracy improvements before anything reaches production.
Your team owns it after we leave
Stakeholder training sessions, troubleshooting guides, and documented best practices for future prompt and data refinements.
Ready to Make Your Chatbot Trustworthy Again?
Bring us your data and your hardest questions. We will show you what verified answers look like.
Our process
From Messy Exports to a Tested, Fully Integrated Assistant.
Discovery
Pipeline
Prompting
Integration
Before launch, a dedicated test environment replays previously problematic queries, and we train your team to maintain the assistant.
How we built it
Inside the Pipeline That Verifies Every Answer
Franchise data leaves IFPG's system as JSON, then passes through a preprocessing pipeline that strips HTML tags and standardizes financial fields. Chain-of-Thought reasoning and Few-Shot examples verify the assistant's logic before OpenAI generates a response, which streams back through the conversational interface built for testing.
Figure 1. IFPG's JSON exports pass through HTML cleansing, financial-field formatting, and schema validation, then feed Chain-of-Thought prompting and OpenAI processing that streams structured, verified answers back to your website.
The outcome
From Cluttered Data and Wrong Numbers to Clean, Verified Answers.
Before
Messy Data, Confused Answers.
HTML-cluttered listings and blurred financial fields produced confusing replies, with no isolated environment to catch failures before members did.
AFTER
Clean Data, Verified Replies.
Preprocessed data, step-by-step verified reasoning, and a streaming interface built to answer within the three-second target.
Response Target
Target response time per query
Listings Today
Franchise listings in current dataset
Scale Target
Listings the architecture is built for
Encryption
Data encrypted in transit
Build Timeline
Full implementation timeline
Test Environment
Isolated OpenAI Assistant for QA
Recognised By The Platforms That Vet AI Companies
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Let's Make Your Franchise Chatbot Answer with Confidence.
Share your listings and the questions members ask. We will map a path to reliable answers.
Frequently asked
Chain-of-Thought reasoning and Few-Shot examples built specifically for financial-data disambiguation guide the assistant's logic, and a verification check confirms it referenced the correct field before the answer goes out.