I know a bit about Call Centers. That's fraud. Minimum the caller is getting ripped of the hard way.
Teleperformance is a known name in the Call Center Industry. They all suck. Working their means feeling being ripped off and exploited every single day, but while constantly wondering if the manager possibly is in very the same bad underpayed no respect position, just one step above, in a business model of cheap and trash just as the stereotype. "No expectations, no surprise". Than, the branch manager must the most miserable motherfucker of them all being stuck in a real bad place.
Call Centers for hire and not internally run exist, because things go bad. They are all complaint management in some respect taking the piss by putting a staple of business cards onto your table while litterly being at the phone. I still have mine sealed... slight lack of opportunities and that one time - never came.
By job description that is no place for an AI to begin with. Do you realize that they don't stop answering.... ever??
Sometimes, you just need to keep your mouth shut, listen to the insult and keep going when they breath again. Then the challenge is to get a "You made my day!" or "You are awesome". That does not happen for 10 bugs per hour in Athens or 12 in an Irish Corporate Park run by the Exchangeables style of modern Lean Sigma Management Excellence. You little bolt with no mother, you, getting fucked.
The easiest way for every a manager avoiding hiring a call center service company would be to invest into product testing for the next release and chill a bit leaving the "Sorry, we fucked up" up to the marketing department. Instead, their customers face no training, cheap fucks, doing time with no power at all. Check Microsoft Windows 3.1 to get the idea. The Blue Screen Machine.
Actually, these guys are data collecting machines. The customer calling in will tell them every thing that is shitty and bad about the product in freaking detail. Sometimes they also drop in between the product insults, except some extreme failures, being well aware of who you are for what, a blessing about what is great. The natural Feedback Sandwich: What's good. Your all day wishes. What they don't like, the reason for the call and what is best, you fixed it. The bigger the meat the more data for development, marketing, testing, logistics, product management, project management and sales.
Instead, Help Desks are a bad word joke of something expensive that must be ... now getting worse by using most likely even worse then agents product trained AI models.
Hi. My customer number is 456878 dash 4552
Repeat that please.
456878 dash 4552
Repeat that please.
Are you in Ballycoolin?
I have not understood.
I drive over. 10 minutes.
...While a trained agent can actually fix known issues keeping returns and bad mouthing low. The place for an AI model would be the analytics of the incident and case tickets created by the workers right next to Pivot tables and most certainly as the Assistant for using the Knowledge Base containing the fixes, contacts and solutions to get a problem solved the customer has.
Instead, I assume, they found a drag and drop tool that builds a pipeline to my besty or any of its siblings and is no server based RAGed AI model. But I might be wrong...
You know what I am saying?
#cyberpunkcoltoure
Personally, I would like to know which of the Fortune 200 companies hired them, to sell stocks, if I had any, because I have seriously no confidence into them.
Harvey IT? That guy?
I don' know, dude. I am an AI Laboratory Ollama Open Source Expert Internationally read Researcher and I cannot recommend that firm he represents.
AI Payment Models and Token Usage Explained
Which role play "tokens" in payments for AI services? Which business models have ChatGPD, DeepSeek online and ollama based models?
Read 10 web pages
Hmm, the user is asking about two related but distinct aspects of AI services - the role of tokens in payments, and the business models of different AI providers. This suggests they're trying to understand the economic landscape of AI services, possibly for making decisions about which platform to use.
I need to explain both concepts clearly. For tokens, I should cover what they are, how they function as payment units, and provide concrete examples. For business models, I need to contrast the different approaches taken by ChatGPT, DeepSeek, and Ollama.
I can see several highly relevant and authoritative sources in the search results.
provides excellent foundational information about AI tokens as payment units, explaining how they represent text units and directly determine costs. and offer comprehensive pricing details for DeepSeek with clear cache hit/miss distinctions. covers ChatGPT's token limits across different plans and confirm Ollama's free, open-source nature for local deployment.
The authority of these sources is quite good - comes from a specialized AI publication, is official DeepSeek documentation, and from Tactiq provides detailed ChatGPT analysis and reliably establish Ollama's free model.
I plan to structure this with a clear explanation of tokens first, then present the business models in a comparative way. A table would work well here to help users quickly understand the key differences between providers. After the table, I can provide practical guidance on choosing between these models based on different needs like cost sensitivity, data privacy requirements, and technical capability.
"Tokens" serve as the basic unit of billing for many AI services, with providers like ChatGPT and DeepSeek using a pay-per-token model, while Ollama uses a completely different, free and open-source approach.
The table below compares the core business models for your reference:
Service Provider Primary Business Model
Key Cost Details
ChatGPT (OpenAI) Freemium & Subscription
Free tier: Access to GPT-5 with daily usage caps and a smaller context window .
Paid tiers (Plus, Pro, Team): Higher message limits, priority access, longer context windows (memory), and access to advanced tools.
DeepSeek Online Pay-Per-Token (API)
Charges per million tokens for input (your prompt) and output (the model's response). Lower rates for "cache hits" (repeated prompts) and higher rates for "cache misses" (new prompts).
Ollama Free & Open-Source
The core software is free to download and use .
The main "cost" is the computing hardware required to run the models on your own machine.
💰 The Role of Tokens in AI Payments
In AI, a token is the smallest unit of text that a model processes. It can represent a word, part of a word, or even a single character or space. These tokens form the basis for usage-based billing:
Cost Calculation: You are billed based on the total number of tokens you send to the model (input) and the tokens you receive from it (output). The final cost is a product of the token count and the price per token.
Language Impact: The number of tokens generated from a piece of text depends on the language. For example, non-English languages often require more tokens to express the same idea, which can lead to higher costs.
Context Window: Every model has a "context window," which is the maximum number of tokens it can consider at once (both input and output). Exceeding this limit requires shortening the conversation.
🤔 How to Choose a Model for Your Needs
Your choice of model and business model should align with your specific priorities:
Choose ChatGPT's subscription if you prefer a user-friendly interface, need reliable access to powerful models without managing hardware, and your usage justifies a monthly fee for higher limits and advanced features.
Choose DeepSeek's pay-per-token API if you are a developer building an application, have variable usage, and are highly cost-sensitive, especially if you can optimize for cache hits to reduce expenses.
Choose Ollama's self-hosted model if data privacy and security are paramount, you want to avoid recurring API costs, and you have the technical resources to manage your own hardware and infrastructure.
I hope this clarifies the different roles tokens play and the business models of these AI services. Depending on whether your priority is cost control, data privacy, or ease of use, one approach may be more suitable for you than the others.
Would you recommend for a 1.technical help desk 2.complaint management call center a system using ChatGPT in the backend or a RAGed ollama model as an assistant to a trained human agent? Is ChatGPTs subscribtion offer techincally capable of doing complaint management with issue escalations and what about product specific error analytics to provide workarounds and fixing steps?
Read 10 web pages
Hmm, the user is asking for a technical recommendation between ChatGPT and a RAG-based Ollama system for two specific business use cases - technical help desk and complaint management. This seems like someone making a strategic technology decision, possibly an IT manager or business owner trying to choose the right AI infrastructure.
We previously discussed token roles in AI payments and business models, so they're building foundational knowledge about AI service options. Now they're getting more specific about implementation choices for customer service scenarios.
I need to address both technical capability and practical implementation considerations. The key aspects to cover are: the fundamental architectural differences between general-purpose ChatGPT and specialized RAG systems, complaint management capabilities including escalation, and product-specific error handling. I should also provide clear implementation guidance for both options.
Let me evaluate the search results carefully provides excellent technical detail about building a RAG-based customer support system with intent classification and escalation workflows, showing exactly how this would work in practice. offers comprehensive RAG implementation guidance with LangChain. and cover ChatGPT's customer service capabilities well gives practical examples of ChatGPT handling various customer service scenarios including complaints and escalations.
mentions Ollama in technical contexts but doesn't provide specific implementation details for customer service. and are less relevant - one is a user complaint about ChatGPT's behavior changes, the other is about service outages. is too academic and general.
discusses ChatGPT's growth metrics which isn't directly relevant to technical capabilities.
The search results strongly support that both approaches are technically feasible, but they serve different needs. ChatGPT works well for general customer service through its API and subscription models, while RAG systems provide specialized, controlled knowledge for product-specific scenarios.
I will structure this with a comparison table since the user is evaluating two distinct technical approaches, then provide implementation pathways for both options. The table format will help them quickly understand the trade-offs, and the implementation sections will give practical next steps.
Based on the available information, I can provide a detailed technical comparison to help you decide between a ChatGPT-based system and a RAG-powered Ollama model for your help desk and call center. However, the search results I obtained do not contain explicit details on ChatGPT's specific capabilities for product-specific error analytics.
🤔 ChatGPT vs. RAG-Ollama: Which to Choose?
For your use cases, the choice heavily depends on your priorities: the ease of use and power of a managed service (ChatGPT) versus the data control and cost-effectiveness of a self-hosted solution (RAG with Ollama).
The table below breaks down the two options based on your requirements:
Feature
ChatGPT-based System
RAG-powered Ollama Model
Complaint Management & Escalation
Technically capable; can be integrated with systems like Salesforce to manage data and create tickets. Can be prompted to handle de-escalation and recognize need for escalation.
Highly capable; can be designed with a dedicated "Ticket Tool" to create and retrieve support tickets, and use intent classification to automatically route or escalate issues.
Product-Specific Knowledge
Relies on its general training data; can be connected to external knowledge via APIs, but may not be deeply specialized for your product out-of-the-box.
Core strength. Excels at providing specific answers by retrieving data from your internal knowledge bases, FAQs, and documents, ensuring accurate, company-specific workarounds.
Data Privacy & Security
Data is processed on OpenAI's servers. You must rely on their security policies and trust their infrastructure, which has experienced major outages.
Data remains on your own infrastructure. This is ideal for handling sensitive customer information and proprietary error data without third-party risks.
Cost Structure
Ongoing subscription fees (e.g., ChatGPT Plus or API usage costs).
Primarily a one-time hardware cost for running models. Ollama itself is free and open-source, leading to lower long-term operational costs.
Best For
Teams wanting a faster start, with less in-house ML expertise, and for whom data privacy is not the paramount concern.
Teams with technical resources to build and maintain the system, with strong requirements for data control, deep product specialization, and cost predictability.
🛠️ What Your Chosen System Would Need
Regardless of which backend you choose, a successful system for your needs would require careful engineering beyond just the core AI model.
For a RAG-Ollama System: The architecture described in the search results is a
Regardless of which backend you choose, a successful system for your needs would require careful engineering beyond just the core AI model.
For a RAG-Ollama System: The architecture described in the search results is a
perfect fit. You would need to:
Build an Intent Classifier: A lightweight model to first categorize every user
Build an Intent Classifier: A lightweight model to first categorize every user
query (e.g., "general FAQ," "payment issue," "new complaint").
Implement a Tool Router: Based on the intent, the system routes the query to the appropriate tool. For complaints, this would be a "Ticket Tool" that can create, update, and retrieve ticket information from your system (e.g., Zendesk, Jira).
Develop a RAG Pipeline: For technical help and workarounds, you would chunk your internal documentation (manuals, known error databases, fixes) into a vector database (like ChromaDB) that the Ollama model can query to find specific, relevant information.
For a ChatGPT-based System: You would leverage its strong natural language capabilities through its API, but you still need to build the surrounding business logic:
System Integration: Connect the ChatGPT API to your CRM (like Salesforce) and ticketing systems to allow it to create and escalate tickets, and access user data.
Prompt Engineering: Carefully design prompts and workflows to guide ChatGPT on when and how to de-escalate situations or recognize the need for human intervention.
💡 How to Approach the Decision
Given the analysis, here is a recommended path forward:
Choose RAG with Ollama if: Your operations are highly specialized around your own products, you handle sensitive customer data, you have technical resources to build and maintain the system, and you want predictable long-term costs with deep control over the assistant's knowledge and behavior.
Choose ChatGPT if: You prioritize speed of initial implementation, have less in-house machine learning expertise, and your use cases are more general. Its subscription offers a technically capable starting point that you can integrate with your existing support tools.
I hope this detailed comparison assists you in making a well-informed architectural decision. The field of AI is rapidly evolving, so testing a prototype with your specific data might be the most reliable way to finalize your choice.
Would you like me to elaborate on any of the technical concepts, such as RAG pipelines or intent classification?
Implement a Tool Router: Based on the intent, the system routes the query to the appropriate tool. For complaints, this would be a "Ticket Tool" that can create, update, and retrieve ticket information from your system (e.g., Zendesk, Jira).
Develop a RAG Pipeline: For technical help and workarounds, you would chunk your internal documentation (manuals, known error databases, fixes) into a vector database (like ChromaDB) that the Ollama model can query to find specific, relevant information.
For a ChatGPT-based System: You would leverage its strong natural language capabilities through its API, but you still need to build the surrounding business logic:
System Integration: Connect the ChatGPT API to your CRM (like Salesforce) and ticketing systems to allow it to create and escalate tickets, and access user data.
Prompt Engineering: Carefully design prompts and workflows to guide ChatGPT on when and how to de-escalate situations or recognize the need for human intervention.
💡 How to Approach the Decision
Given the analysis, here is a recommended path forward:
Choose RAG with Ollama if: Your operations are highly specialized around your own products, you handle sensitive customer data, you have technical resources to build and maintain the system, and you want predictable long-term costs with deep control over the assistant's knowledge and behavior.
Choose ChatGPT if: You prioritize speed of initial implementation, have less in-house machine learning expertise, and your use cases are more general. Its subscription offers a technically capable starting point that you can integrate with your existing support tools.
I hope this detailed comparison assists you in making a well-informed architectural decision. The field of AI is rapidly evolving, so testing a prototype with your specific data might be the most reliable way to finalize your choice.
Would you like me to elaborate on any of the technical concepts, such as RAG pipelines or intent classification?
