Customer Service LLM Chatbot Program

@Microlent

Hello! This is exactly the kind of LLM-backed, production-minded chatbot work I enjoy building. I’m comfortable working hands-on with offline LLMs and wrapping them in clean, testable services that are fast, predictable, and safe enough for real customer support use. I can build a text-generation service around local models like Llama 3 (Opus) or Gemma, focusing on response quality, latency, and guardrails rather than raw experimentation. The emphasis will be on reliable answers that stay on-brand and don’t hallucinate policies or numbers. How I’d approach it: • Offline model setup and tuning (llama / gemma) with latency targets in mind • Prompt pipeline that injects conversation history + customer metadata cleanly • Response controls for tone, verbosity, and policy alignment • Safety layer for profanity, PII, and restricted content Relevant AI / LLM experience: • EZPZ – AI-powered trip planning using ChatGPT-style APIs (prompt control + UX alignment) • My Magic Moment – AI-driven content generation with strong tone and narrative constraints I’ve deployed AI systems where response quality, consistency, and speed mattered more than novelty, and I’m confident hitting your “8 out of 10 under 2 seconds” acceptance bar with the right prompt and model setup. Let’s open the chat and align on brand voice, policy constraints, and your preferred offline model so I can shape this cleanly from day one. Best regards, Jenifer

@AwaisChaudhry

With a decade of experience in the software development field and a team that thrives on complex projects, ZAWN Tech is well-equipped to meet your needs for the Customer Service LLM Chatbot Program. Our AI and computer vision department has successfully integrated GPT systems in the past, which aligns with your project requirements perfectly. We have a wealth of knowledge when it comes to working with large-language-model frameworks and are familiar with llama 3/opus and gemma that are mentioned as preferred choices. At ZAWN Tech, we believe in building not just functional but reliable and scalable systems. Your core needs of model handling, prompt/response logic, safety & tone controls and a simple API wrapper all come under our strong suit. We have proficiency in FastAPI and can deliver the clean Python code you need for your project's API endpoint. In terms of acceptance test, our excellent track record in the industry where security, speed and accuracy are paramount would eassure you. With over 200 recommendations from clients completely satisfied with our performance, I'm confident we can tackle your anonymized tickets skillfully - correctly answering at least eight with policy-aligned replies within the specified timeframe. As always with ZAWN Tech projects, we will provide clear documentation and any necessary support for integration into your existing system.

@LiveExperts

Hello, As an engineering and development team with an extensive range of skills, we are highly qualified to tackle your Customer Service LLM Chatbot Program project. Our expertise in Python and PyTorch coupled with significant experience building large-language-model frameworks makes us a natural fit for the task on hand. We assure you the highest level of accuracy while managing latency through our adept utilization of frameworks like llama 3/opus and gemma. One key strength that sets us apart is our ability to craft personalized algorithms, such as your prompt/response logic requirements. With these, we guarantee that the bot captures context effectively, steering clear from wild hallucinations while ensuring it returns concise yet informative answers. Our team excels not only in technical proficiency but also in prioritizing the finer details crucial to maintaining brand tone, identity and safeguarding against sensitive information leaks. Lastly, we recognize and understand your need for simplicity. Our fast delivery time coupled with the provision of clear documentation and a simple single API endpoint just as you requested is representative of our work ethic. Allow us the opportunity to demonstrate our abilities by using your provided ten anonymized tickets for customized acceptance testing, after which you'll appreciate why we’re a formidable choice for bringing your customer service interface to life. Thanks!

@gunslinger

Hi! This is exactly the type of system I built recently. I previously developed a GenAI customer-support reply pipeline, including: - safety controls (PII/profanity filtering + brand voice consistency) - context-aware prompt orchestration with conversation history + metadata - offline/local LLM deployment (Llama/Gemma-class models) - FastAPI service exposing a clean POST /reply endpoint I can deliver a production-ready solution that meets your <2s latency requirement and passes the 8/10 ticket acceptance benchmark. Happy to share a working example from my implementation. Best, Yuda

@neerajdhameliya

Hello, I understand you’re looking for a Python developer to build an offline LLM-powered customer-service chatbot that delivers natural, on-brand replies quickly and safely. I can deliver a FastAPI-based wrapper optimized for sub-2-second inference that maintains strict brand alignment without the "hallucination tax" common in standard setups. My Approach to Your Core Needs: => To hit your <2s target, I recommend deploying Llama 3 (8B) or Gemma 2 (9B) using vLLM or NVIDIA TensorRT-LLM. These frameworks utilize continuous batching and PagedAttention to ensure that even with conversation history, we keep latency at a minimum. => Context-Aware RAG Logic: I will implement a "Context Injection" layer. By structured-formatting customer metadata and ticket history into the system prompt, the model stays grounded. To prevent hallucinations, I’ll use constrained sampling or a secondary "verification" check for specific numbers and policy keywords. => I’ll integrate NeMo Guardrails or a custom regex/LLM-eval filter to scrub PII and block off-brand responses before they ever reach the /reply endpoint. => You’ll receive a containerized FastAPI application. The POST /reply endpoint will be asynchronous to handle multiple concurrent tickets without bottlenecking. Do you already have GPUs like A100 or L40 for offline deployment, or should I suggest hardware based on your ticket volume? Best regards, Niral

@OutsourceMan

⭐⭐⭐⭐⭐ I can build an offline LLM-powered customer-service reply engine using Llama 3/Opus or Gemma with optimized quantization to keep responses accurate and under two seconds. I’ll design a prompt pipeline that injects conversation history and customer metadata, applies policy retrieval to prevent hallucinations, and enforces concise, brand-aligned outputs. Safety layers will include profanity filtering, PII masking, and tone-control rules. The system will be delivered as a clean FastAPI service exposing POST /reply with structured logging and easy integration into your support interface. CnELIndia will provide architecture design, model optimization, testing workflows, and deployment readiness, while Raman Ladhani will focus on prompt engineering, guardrail tuning, evaluation against your ten-ticket acceptance test, and documentation including README and cURL examples. Prior chatbot deployments used Python, PyTorch, FastAPI, and retrieval-augmented pipelines achieving strong policy accuracy and low-latency performance.

@softsolution2000

I am confident that my expertise in Python, Software Architecture, Pytorch, FastAPI, and Large Language Model align perfectly with the requirements of the Customer Service LLM Chatbot Program. My experience in developing and deploying customer-service chatbots will ensure a seamless integration with your existing support interface. Once we discuss the full project scope, we can adjust the budget accordingly. Let's collaborate to bring this chatbot program to life efficiently and effectively. Please go through my profile its 15 years old see the work I did over the years. No Win No Fee means that your satisfaction is my utmost priority. Lets discuss the job details. Moreover, I am willing to start the job and perform tasks without even being hired; it is just to show my commitment to this project. Looking forward to hear from you.

@tangramua

Dear ,   We carefully studied the description of your project and we can confirm that we understand your needs and are also interested in your project. Our team has the necessary resources to start your project as soon as possible and complete it in a very short time.   We are 25 years in this business and our technical specialists have strong experience in Python, Software Architecture, Pytorch, FastAPI, Large Language Model, AI Chatbot Development, AI Model Development and other technologies relevant to your project.   Please, review our profile https://www.freelancer.com/u/tangramua where you can find detailed information about our company, our portfolio, and the client's recent reviews.   Please contact us via Freelancer Chat to discuss your project in details.    Best regards, Sales department Tangram Canada Inc.     

@nikhilgarg96

As a seasoned developer with a strong background in AI Model Development and Python programming, I am well versed in working with large-language-model frameworks - a perfect fit for your project. I have hands-on experience with offline LLMs such as llama 3/opus and gemma and can ensure optimal latency and accuracy, crucial for any text-generation program. My previous projects have consistently focused on devising interactive and effective customer-service chatbots. An area where I believe my expertise stands out is in crafting prompt/response logic that captures not just the context, but the customer's metadata as well. This means that the bot's responses will not only be accurate but also tailored to individual clients' needs. Furthermore, I can seamlessly integrate safety measures to protect against profanity or sharing of personal data, ensuring brand voice consistency at all times. My ability to provide clean yet efficient code (FastAPI preferred) with comprehensive quick-start documentation will enable you to deploy and integrate the program smoothly into your support interface. Lastly, having developed similar chatbots before, I understand the value of reliable performance. I stand by my work and assure you that the program will faithfully emulate policies while delivering concise answers. Looking forward to demonstrating my skills and delivering excellent results for your project. Thanks.....

@intelliresponse

With over 13 years of experience in AI solutions, my expertise aligns perfectly with your project requirements. I have a proven track record of delivering high-impact, result-driven solutions through customized Python web automation, scraping, and AI projects. My extensive experience in web automation and data extraction makes me well adept at handling large language models (LLMs) - precisely what you need for this project. In addition, I have a deep understanding of building chatbot systems and creating the intricate prompt/response logic that you require. Having integrated safety measures like profanity filters and brand voice consistency control into various applications before, I can ensure the development of a secure and on-brand chatbot program for you. My proficiency extends to developing clean API codes which will expose a single endpoint as required in your project specifications. Furthermore, as someone who aims to create scalable, powerful solutions, your acceptance tests are right up my alley. I am thrilled to potentially collaborate with you on this project and deliver immaculate results that meet or exceed your expectations. Let's connect today and discuss how my expertise can benefit your Customer Service LLM Chatbot Program!

@hireageek

Hi there, I'm excited about the opportunity to help create a customer service chatbot program utilizing large language models. As a top freelancer from California, I have extensive experience in AI development and have successfully deployed several customer service chatbots that not only meet but exceed client expectations. My expertise includes working with Llama 3 and Opus frameworks, ensuring that responses are accurate, contextually relevant, and consistent with the brand's voice. I understand the importance of managing the prompt-response logic for your chatbot effectively while integrating safety measures to filter out inappropriate content. My approach involves crafting a solid pipeline that captures all necessary context and executes in less than two seconds, as you require. I can also deliver a simple FastAPI wrapper for clean integration and provide detailed quick-start documentation to facilitate the process. I would love to discuss your specific needs further. What ticket metrics do you currently have to better gauge expected outcomes? I look forward to your response!What ticket metrics do you currently have to better gauge expected outcomes?

@Herculesz

Hi I can build your offline-LLM customer-service reply engine using models like Llama 3 / Gemma, optimized for low-latency generation and tightly controlled prompt pipelines. The key challenge is balancing natural, on-brand responses with strict safety constraints while running fully locally—no cloud dependencies. I’ll address this by designing a context-aware prompt stack (history + metadata), adding guardrails for tone, PII, and profanity, and enforcing policy-safe outputs through classifier checks. Your FastAPI wrapper will expose a clean POST /reply endpoint returning concise, accurate answers with consistent style and minimal hallucination. I’ve deployed customer-service chatbots using offline LLMs, quantized weights, and optimized decoding paths to stay under 2 seconds per reply. Deliverables will include clean Python code, model-loading logic, and a lightweight README with sample cURL tests. Thanks, Hercules

@divumanocha

Hello, I came across your project and found it truly interesting. With over eight years of hands-on experience in this field, I have successfully delivered high-quality solutions to clients worldwide. My dedication to excellence is reflected in the 180+ positive reviews from satisfied clients. I’d love to bring this expertise to your project and ensure outstanding results. However, I do have a few important points I’d like to clarify to align perfectly with your vision. Let’s connect via chat, where I can also share relevant examples of my past work. I'm looking forward to hearing back from you! Best Regards, Divu.

@corpmember29

I HAVE BUILT AND DEPLOYED OFFLINE LLM-POWERED CUSTOMER SUPPORT CHATBOTS THAT DELIVER FAST, POLICY-SAFE, ON-BRAND RESPONSES IN PRODUCTION ? I’m an AI/ML engineer experienced in LLM-based text generation systems for customer service, with a strong focus on offline models, latency control, and safety. I can build a clean, production-ready text-generation service that plugs directly into your existing support interface. Core Features I’ll Deliver • Offline LLM setup using LLaMA 3 / Opus / Gemma (latency-optimized) • Prompt pipeline with conversation history + customer metadata • Guardrails for hallucination control, profanity, PII, and brand tone • Concise, policy-aligned response generation • Python backend with FastAPI • Simple API: POST /reply { user_message, conversation_id } Technical Approach • Efficient model loading & inference (quantization if needed) • Deterministic prompting + response filtering • Clear separation between prompt logic, safety layer, and API • Easily extendable for future fine-tuning or RAG Deliverables • Complete Python source code • FastAPI service ready for deployment • README + sample cURL request • Acceptance-test support using your anonymized tickets You’ll receive full source code ownership and 2 YEARS OF FREE ONGOING SUPPORT post-delivery for fixes, tuning, and minor enhancements.

@koolvishruth

Hello, I have already worked on similar project on Ollama local model integration for my other client. I can fine tune as mentioned. I have 8 years of experience in AI Engineering. Let’s connect

@badarmadni927

As a seasoned full-stack developer with specialized expertise in AI-driven solutions, I am extremely proficient in the critical elements you are seeking for your customer service LLM chatbot program. My extensive experience with large language models such as ChatGPT, Gemini, and Loveable AI aligns perfectly with your preference for LLM frameworks like llama 3/opus and gemma. In addition to my proficiency in model handling, I bring valuable knowledge in prompt/response logic – authoring a pipeline that effectively captures relevant context such as conversation history and customer metadata. This ensures the chatbot provides concise yet personalized answers, avoiding policy confusion or the exhibition of unnecessary numerical information. To enhance safety and maintain brand voice consistency, I understand the importance of integrating robust controls against profanity and personally identifiable information within your program - features that echo throughout my previous work. Moreover, I prioritize clean code development which extends to delivering a simple API wrapper using FastAPI alongside comprehensive documentation for ease of implementation into your existing support interface. Together, let's meet your acceptance test head-on and exceed expectations!

@usamae

I am experienced in developing customer-service chatbots using large-language-model frameworks like llama 3/opus and gemma. I specialize in crafting prompt/response logic that captures context and delivers concise, on-brand responses. With a focus on safety and tone controls, I ensure profanity filters and brand voice consistency are integrated. My expertise includes creating simple API wrappers in Python (FastAPI) and providing comprehensive documentation for easy integration. Let's collaborate on this project!

@Mahihassi

With a particular focus on my AI chatbot development expertise and proficiency in Python, I possess the necessary background and skills needed for your Customer Service LLM Chatbot Program. During my career as a Software Engineer, I have not only fine-tuned and deployed customer-service chatbots but have also worked with large-language-model frameworks, similar to what you require. My deep understanding of model handling, prompt/response logic, safety & tone controls allows me to create programs that deliver accurate, brand-aligned responses while taking context into account. In terms of deployment specifics, I have experience with offline LLMs like llama 3/opus and gemma while never compromising on latency and accuracy. Building a simple API wrapper with clean Python code (particularly FastAPI) exposing the necessary endpoint has always been standard practice in my work. Additionally, always keen to ensure a smooth integration process for clients, I will provide you with comprehensive quick-start docs including a README and sample cURL call to make embedding into your existing system a breeze.

@Julian29

Hello, I have 10+ years of experience, I have built and deployed LLM-based support agents, focusing on low latency, safety controls, and production-ready API layers. I have reviewed your requirements and understand you need a CUSTOMER-SERVICE LLM CHATBOT PROGRAM that runs offline, delivers policy-aligned replies, and can be integrated into your support interface with a simple API. What I Will Deliver Offline MODEL STACK using Llama 3 / Opus or Gemma (based on your performance constraints), optimized for sub-2-second responses. Prompt Pipeline capturing conversation history + customer metadata, while preventing hallucinations and ensuring concise answers. Safety & TONE GUARDRAILS including profanity filtering, PII redaction, and brand voice enforcement. FastAPI wrapper with a clean endpoint: POST /reply payload: { user_message, conversation_id } Quick-start docs with README and sample cURL call. Key Highlights Experience fine-tuning and deploying customer support LLMs with strong accuracy and low latency Focus on CONTEXT MANAGEMENT, RESPONSE CONSISTENCY, and SECURITY I will work 40 HOURS PER WEEK FULL TIME FOR YOU, ensuring steady progress and prompt iterations I have a few queries to discuss in chat to proceed further. Awaiting your positive response. Thanks