looking for AI expert to fix chat bot urgent

@extreamcode

Hello, I’ve read your urgent need and I’m confident I can diagnose and fix the NLU gaps in your transformer-based chatbot quickly. With 15+ years in AI, NLP, and production-grade chatbots, I’ll audit your Python stack, inspect the Rasa NLU pipeline, trainer configs, and the model serving layer to pinpoint misclassification drivers, missing entities, and low confidence signals. I’ll regroup training data, tune intents/entities, and adjust confidence thresholds so off-script utterances map to the right intents and provide stable responses. I’ll retrain or fine-tune the model (TensorFlow/PyTorch as appropriate) and implement a robust evaluation script. You’ll receive an updated codebase, refreshed training data (if needed), and a short test script that proves at least five varied queries are handled correctly. I’ve shared an initial estimate based on your description, and once we go over a few technical details, I’ll confirm the exact cost and delivery schedule. Asad- I sense the hidden bottleneck is mismatched intent boundaries and noisy confidence scores; addressing that directly will restore accurate, on-brand replies and improve conversions. What are the top five user utterances that currently trigger misclassifications, and do you have a preferred baseline for the expected accuracy on those cases? Additionally, would you like me to include an automated test suite that runs nightly checks against a reproducible dataset to catch regressions? Best regards, Asad

@NabeelAbid22

Hi, I am Nabeel Abid, a software engineer with 10+ years of experience in web and software development. I’ve successfully led projects across various domains, ensuring they meet both user needs and business goals. My expertise in languages like JavaScript, Python, and frameworks such as React and Django allows me to deliver robust, scalable solutions that perform seamlessly. I understand the importance of clear communication and timely delivery in this industry, and I pride myself on being a reliable team player. I am confident in my ability to bring both innovation and efficiency to your project, ensuring that we not only meet deadlines but also exceed expectations. I look forward to the opportunity to contribute my skills and help drive your project’s success. Regards, Nabeel Abid

@koolvishruth

Hi Sir, I have 8 years of experience in AI chatbot development.I will fix. Let’s connect through chat

@Chris9753

Hi, This looks like an NLU/model quality issue rather than an integration problem. When a chatbot starts falling back on slightly different phrasing, it usually means the model isn’t generalizing well or confidence thresholds are too strict. My experience is more on the transformer side—I’ve worked on fine-tuning models using QLoRA, which is very effective for improving performance without heavy retraining. I can use that approach to make your model handle more varied user inputs and reduce fallback cases. I’ll review your current pipeline and training data, identify why intents and entities are being missed, then fine-tune the model and adjust thresholds so responses trigger correctly. I’ll also provide a small test script with a few different queries to show the improvement clearly. I can start right away and focus on a fast fix since this is impacting a live system. Best regards. Chris

@ashinnd84

Hello there, we are a senior Full Stack Web and Mobile App Developers and we can do this project in no time. Thanks Ashish Kumar.

@RadoslavPompura

Hello, I can quickly step in and resolve the NLU breakdown impacting your production chatbot. From your description, the issue likely stems from a combination of intent drift, insufficient training coverage, and poorly calibrated confidence thresholds—common failure points when transformer-based models and Rasa pipelines are not tightly aligned. I will begin by auditing your current NLU pipeline, including training data quality, intent distribution, entity annotations, and model configuration. I’ll identify misclassification patterns, uncover gaps in training examples, and analyze why confidence scores are collapsing. From there, I will fine-tune or retrain the model using PyTorch or TensorFlow (depending on your current stack), improve intent generalization, and recalibrate confidence thresholds to ensure reliable response triggering without increasing false positives. Additionally, I will optimize entity extraction, refine the Rasa pipeline if needed, and ensure smooth integration with your existing REST setup. You will receive a clean, updated codebase, enhanced training data (if expanded), and a concise test script demonstrating accurate handling of at least five diverse real-world queries. I understand the urgency of a live production issue and can deliver fast, stable improvements with minimal disruption.

@modesorehari6

Hi, there. I will diagnose your chatbot’s NLU layer by auditing the Rasa pipeline, transformer outputs, and training data quality to identify why intent classification drops below usable thresholds and causes fallback loops. I will rebalance datasets, fix entity extraction gaps, and fine-tune the model to achieve 90%+ intent accuracy and reduce fallback triggers by 60%+ across real queries. I have improved 12+ production chatbots with measurable gains in response relevance and stability. I will retrain the model using optimized hyperparameters in PyTorch or TensorFlow, recalibrate confidence thresholds, and validate predictions across 50+ test utterances to ensure consistent mapping of user intent. I will refine tokenization, embeddings, and pipeline components to eliminate stalls and ensure <300ms response latency while maintaining reliable entity capture in 95% of cases. I will also verify API behavior to ensure seamless integration with your existing REST setup. I will deliver the updated codebase, improved training data, and a clear test script demonstrating at least 5+ varied queries handled correctly with stable confidence scores. I will keep changes focused and production-safe to restore performance quickly without disrupting live operations. If this sounds good, connect in chat and we can start. Thanks, Malix

@FutureNOWPK

Hello I hear you, an automation is supposed to be dependable and reliable. What makes it unreliable is my first point finding out. If you level with me and let me know the scope of the chatbot, I will help troubleshoot and share PoC concept that works for me. I have chatbot running for training and registration for ISACA. Lets talk

@rishi9343

As an extensively experienced developer fluent in both backend and mobile solutions, I can confidently state that I have the skills to fix your chatbot issue efficiently. With over 8 years of dedicated experience in not only Python, Java, and JavaScript but also AI automation, my expertise in natural language understanding will be particularly instrumental in rectifying your NLU pipeline shortcomings. Able to adapt between PyTorch and TensorFlow, whichever you deem perfect, fine-tuning or retraining the model to accurately handle user queries is second nature to me. To ensure nothing falls through the cracks in my thorough evaluation and fixes, I'll inspect not just the NLU pipeline but also your training data and model configuration. I guarantee to make necessary adjustments with confidence thresholds so your bot responds exactly as it should. Moreover, given my reputation for delivering high-quality, production-ready applications on time – a key quality needed for this project –- I am more than ready to do a speedy yet thorough job for you. Let's quickly take charge of the situation, revitalize your codebase with fresh training data if required and optimize your bot's performance within the tight timeframe you need. Communication is key so don't hesitate to reach out to me now!

@geryybangsawan

Hi I will diagnose and fix your NLU by auditing the Rasa pipeline, training data quality, and transformer configuration. I’ll rebalance intents, improve entity extraction, retrain/fine-tune the model, and adjust confidence thresholds to reduce fallback errors. Deliverables include updated code, improved dataset, and test script with validated queries. Timeline 1–2 days. Price $30.

@Dliteinformation

With over a decade of experience in the field of software development, I've tackled similar challenges as you're facing right now. My in-depth understanding of both Python and transformers-based models will equip me perfectly for this project. I will exhaustively inspect your NLU pipeline, training data, and model configuration to identify the root cause of your bot's misfires. Being comfortable working with both TensorFlow and PyTorch, I'll use whichever best for solving this issue. In conclusion, hiring me means hiring a proven problem-solver who is dedicated to delivering high-quality results on time, every time. Through my strategic approach to software development, I will transform your chatbot into one that intelligently understands user utterances and responds accurately. Don't miss out on bringing your AI chatbot back to its peak performance. Let's work together and turn around your situation from terminal frustration to conversions.

@joseteixeira06

Hello Mate!Greetings , Good morning! I am professional mobile programmer with skills including Natural Language Processing, AI Text-to-text, AI Development, AI Model Integration, JavaScript, AI Model Development, Software Architecture, Python, AI Chatbot Development and Java. Please contact me to discuss more regarding this project. Feel free to contact us to discuss your project

@usamaansari786

Hello, I can help you debug and improve your chatbot’s NLU layer by reviewing your Rasa pipeline, training data, and transformer setup to pinpoint why intents are being misclassified and confidence scores are collapsing; I’ll then fine-tune or retrain the model (using TensorFlow or PyTorch depending on what fits your stack best), adjust thresholds and preprocessing where needed, and return an updated, production-ready setup along with a small validation script showing correct handling of multiple real user queries. Warm regards, Usama H

@vertechsolutions

Hi, I can fix your AI chatbot NLU issue quickly. I've solved this exact problem many times. Here is what I will do: Inspect the Rasa NLU pipeline, training data, and model config to find misclassification causes. Retrain/fine-tune the transformer model, improve intent/entity mapping, and tune confidence thresholds. Deliver the updated Python codebase, fresh training data if needed, and a test script for 5+ live queries. 10 days free support after delivery Milestone-based payment Reply "YES" and Best regards, syed ribal

@ihorz5

Hi, I specialize in Python-based AI chatbots and transformer models, with hands-on experience in Rasa NLU, TensorFlow, and PyTorch. I can inspect your NLU pipeline, review the training data, and pinpoint why intents misclassify or entities fail to register. I’ll fine-tune or retrain the model as needed, adjust confidence thresholds, and ensure your chatbot responds correctly to off-script queries. Deliverables include the updated codebase, any refined training data, and a short test script verifying at least five varied queries are handled accurately. This will restore reliable conversational flow quickly, minimizing downtime and lost conversions. Looking forward to working with you.

@nicolasalexandro

Hey — saw your post about your production chatbot suddenly missing the point of conversations and falling back. When a live bot starts doing that, it usually kills user trust fast and hurts key metrics. Quick question before I suggest a fix: Did anything change recently (model version, prompt, retrieval setup, or logs/tracing tools) right before this behavior started? I’ve debugged and stabilized multiple production chatbots (RAG, tools, and custom prompts) where things were working and then suddenly went off the rails after a small change or traffic spike. If you send a link to the bot, logs, or a brief architecture/prompt spec, I can review what’s going on and point to the most likely failure points.

@toqeer74

A misclassification of intents and low confidence scores in your chatbot's natural-language-understanding layer can severely impact user engagement and conversions. The current reliance on a transformer-based model with Rasa NLU supervision lacks the fine-tuning needed for nuanced user queries. By inspecting the existing NLU pipeline and training data, I will identify the root causes of these errors. I propose employing TensorFlow for precision in retraining the model and adjusting confidence thresholds. Expect the initial deliverable within 5 days: an updated codebase, refined training data, and a test script validating improved intent mapping. Quick question: what's the one thing that needs to work perfectly on day one?

@dholadipak09

Hi there, Best solution: I’ll debug the chatbot’s NLU layer directly — training data, Rasa pipeline, transformer config, intent mapping, entity extraction, and confidence thresholds — then retrain/fine-tune it so normal user questions stop falling into the “sorry, I don’t understand” black hole. I have experience with Python, NLP, transformer-based models, Rasa NLU, REST API model serving, intent classification, entity extraction, and chatbot tuning. Since you already confirmed the webhook/integration layer is healthy, I’ll focus only on the NLU failure points and avoid wasting time on unrelated areas. I’ll inspect misclassified utterances, improve weak training examples if needed, adjust thresholds carefully, retrain/test the model, and provide a short test script showing at least 5 varied queries mapped correctly with proper responses. Cost: $30 || Timeline: 1–2 days Payment and timeline details can be discussed further to align with your expectations. Best regards, Dipak

@taraso1

?I can do it, no problem! But I will also try!? ✨Clean and convenient, accurate and delicate, maximum perfection✨ Hello, my name is Taras. I specialize in NLU troubleshooting and optimization for production chatbots, ensuring intent classification and entity recognition work reliably under real-world traffic. I will start by reviewing your Python codebase, Rasa NLU pipeline, training data, and model configuration to pinpoint why user utterances are misclassified or fallback responses are triggered. This preparatory step ensures fixes target the root problem without introducing new errors. Next, I’ll retrain or fine-tune the model using TensorFlow or PyTorch, optimize confidence thresholds, and adjust entity extraction so typical user inputs consistently map to the correct intents. I’ll test using varied sample queries to confirm accuracy across multiple scenarios. Finally, I’ll deliver an updated codebase, improved training data, and a brief test script that demonstrates the bot now handles user interactions correctly and reliably. I take full accountability for delivering a stable, high-performing NLU system and am eager to collaborate to restore smooth, accurate conversational flow on your site.