You will get a fact-checker that catches your AI chatbot's wrong answers
Rising Talent
Rising Talent
Project details
AI chatbots sometimes answer with total confidence and get it wrong, inventing a refund policy or a discount you don't actually offer. Those made-up answers erode customer trust and can create real liability.
I add a fact-checker that sits between your chatbot and your customer. Before an answer goes out, it checks that answer against your own documents, your help centre, policies, product info and decides whether it's genuinely supported or made up, in under a second. You choose what happens to a flagged answer: block it, warn, or log it.
What sets this apart: it runs on your own systems, so your data stays private, and it works with the chatbot you already have; nothing to rebuild. You also get a plain-English report showing how often your bot was wrong and where, so you can fix the cause, not just the symptom.
Proof I can build it: I'm the author of GroundCheck, an open-source answer-checker that beats GPT-4 at catching AI mistakes on a public test, published on GitHub, Hugging Face and PyPI. Checking AI answers is what I do, including 18 months of grading model quality professionally.
I add a fact-checker that sits between your chatbot and your customer. Before an answer goes out, it checks that answer against your own documents, your help centre, policies, product info and decides whether it's genuinely supported or made up, in under a second. You choose what happens to a flagged answer: block it, warn, or log it.
What sets this apart: it runs on your own systems, so your data stays private, and it works with the chatbot you already have; nothing to rebuild. You also get a plain-English report showing how often your bot was wrong and where, so you can fix the cause, not just the symptom.
Proof I can build it: I'm the author of GroundCheck, an open-source answer-checker that beats GPT-4 at catching AI mistakes on a public test, published on GitHub, Hugging Face and PyPI. Checking AI answers is what I do, including 18 months of grading model quality professionally.
AI Development Type
Deep Learning, Model TuningAI Tools
Keras, PyTorch, TensorFlowAI Development Language
PythonWhat's included
| Service Tiers |
Starter
$300
|
Standard
$600
|
Advanced
$1,200
|
|---|---|---|---|
| Delivery Time | 7 days | 12 days | 21 days |
Number of Revisions | 1 | 2 | 2 |
AI Model Integration | |||
Detailed Code Comments | |||
Knowledge Graph | - | - | - |
Model Documentation | |||
Ontology | - | - | - |
Source Code | |||
Taxonomy | - | - | - |
Optional add-ons
You can add these on the next page.
Live walkthrough call
+$75Frequently asked questions
About Pranshu
AI Engineer | LLM Applications | RAG | AI Agents | FastAPI
Gurugram, India - 9:10 am local time
What you get when you hire me:
- An AI model built for YOUR specific task, sorting support tickets, checking AI answers for errors, detecting sensitive information, and extracting data from documents
- It works even if you have no training data: I generate and quality-check the data myself, so you skip months of manual labelling
- It runs on cheap servers, and your data never leaves your company
- Delivered ready to use: installable package or live API, with documentation that your team can maintain
Why my models cost less to run: Instead of renting a giant AI for every small job, I train a compact specialist that does one job extremely well. For a defined task, it matches or beats the big AI services at a fraction of the cost and responds in under a second.
Proof (for your technical team to verify): GroundCheck, my open-source hallucination detector 0.682 F1 on the RAGTruth benchmark vs 0.634 for the GPT-4-as-judge baseline, sub-second CPU inference, published on GitHub, Hugging Face and PyPI with a reproducible benchmark harness and full technical writeup.
I work fast, test honestly, and document everything so your team owns the result.
Steps for completing your project
After purchasing the project, send requirements so Pranshu can start the project.
Delivery time starts when Pranshu receives requirements from you.
Pranshu works on your project following the steps below.
Revisions may occur after the delivery date.
Confirm scope and connect your sources
We agree on what counts as a "supported" answer and what happens to flagged ones, and I get your documents and example answers. You approve the plan before I start.
Set up the checker on your answers
I connect the fact-checker to your chatbot and tune it on your real examples, so it reliably tells a supported answer from a made-up one for your specific content.