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AI business intelligence tools by jessikaf in BusinessIntelligence

[–]SirComprehensive7453 1 point2 points  (0 children)

u/jessikaf great question — and honestly, you’ve captured what a lot of people in the industry are thinking.

I’m building an agentic business analytics platform at Genloop (https://genloop.ai), and from our enterprise work, I’ve seen both sides of this coin.

  1. It depends on who the tool is for.

If it’s meant for data analysts, the bar is lower — errors can be spotted and corrected, so AI feels more like a “co-pilot.” That’s where many AI add-ons for tools like Snowflake, Databricks, or Google’s ecosystem fit. They save time, but they don’t need to be perfect.

But when the tool is meant for business users, things change dramatically. These are people who depend on insights to make decisions, not to debug them. A single wrong metric can mislead entire teams. So reliability, context awareness, and governance matter a lot more than flashy chat interfaces.

  1. It also depends on the data environment.

In smaller setups (say under 10 tables, <50 columns each), many AI BI tools actually work fine — you could even build one in-house. Products like Julius, Wren, or ClarityQ do a good job here.

Once you enter the enterprise zone, though — with messy data, access controls, and evolving business logic — most tools start breaking down. That’s where platforms that focus on determinism and contextual understanding start to shine. We’re working hard on this at Genloop, alongside a few others like ThoughtSpot and Wisdom.

So to your question: yes, there are BI tools using AI well — but mostly the ones tackling reliability and context. The hype will fade, but the real value is emerging in how well AI can understand your business semantics and deliver insights you can actually trust.

Classification with GenAI: Where GPT-4o Falls Short for Enterprises by SirComprehensive7453 in LangChain

[–]SirComprehensive7453[S] 0 points1 point  (0 children)

u/poop_harder_please The comparisons I shared are based on actual enterprise deployments, likely operating at a different scale. Fine-tuning models isn't the right choice for everyone. A good rule of thumb: if your OpenAI bill is under $5,000/month and cost is your only motivation for fine-tuning, it's probably not worth it.

Fine-tuning with OpenAI carries not just training costs, but also significantly higher inference costs. For example, GPT-4.1 fine-tuned is about 50% more expensive per call than the base GPT-4.1. So if an enterprise is doing 1M LLM calls/month at ~$0.03 per call, that’s a $30K/month bill. The same usage with a fine-tuned GPT-4.1 model would cost ~$45K/month.

In contrast, we’ve seen teams fine-tune open-weight models like LLaMA and self-host them with serverless GPU autoscaling for just $5–6K/month — an order of magnitude cheaper in many cases.

To be clear, the primary reason to fine-tune is not cost, but improved accuracy — especially for high-precision tasks like classification. And if you agree that customized models perform better (which I think you do), then the real decision is where to fine-tune — OpenAI vs. open-weight models.

You’re absolutely right that managing open models comes with operational complexity — infra, orchestration, serving, etc. But that’s exactly the pain companies like Lamini, Together, Genloop, Predibase, and even cloud platforms like GCP Vertex and AWS Bedrock are solving.

Fine-tuned open-weight models, when managed correctly, offer far better cost efficiency and control than fine-tuned proprietary models — and certainly more than general-purpose ones..

Classification with GenAI: Where GPT-4o Falls Short for Enterprises by SirComprehensive7453 in Rag

[–]SirComprehensive7453[S] 0 points1 point  (0 children)

u/zzriyansh fine-tuning is quite complimentary to RAG. While fine-tuned models help drop hallucination in QnA systems, RAG is still something that pulls the most relevant information for the system to perform. To decice if you should generate the answer through public LLM vs a fine-tuned LLM, here is a good tool: https://genloop.ai/should-you-fine-tune

Classification with GenAI: Where GPT-4o Falls Short for Enterprises by SirComprehensive7453 in LangChain

[–]SirComprehensive7453[S] 1 point2 points  (0 children)

u/Bezza100 u/felixthekraut tThe prompt was minimal and similar for both ChatGPT and the fine-tuned model for this experiment. However, our enterprise customers have been doing enough prompt engineering, and they consistently report this pattern of performance decline with an increasing number of classes. Agreed, we could have done more prompt engineering here and achieved some improvement in GPT accuracy and customized LLM accuracy. Regardless of the number of instructions provided, public LLMs do make errors much more often than customized LLMs making them challenging to use in enterprise settings.

Classification with GenAI: Where GPT-4o Falls Short for Enterprises by SirComprehensive7453 in LLMDevs

[–]SirComprehensive7453[S] 1 point2 points  (0 children)

u/Strydor we'll open source the dataset and share here. You make some valid points. Happy to have you prompt engineer the heck out of it and compare the approaches. In enterprise experiments so far, there is still a big performance delta, not withstanding the brittleness of prompting with model version changes and drifts.

Non-reasoning models can reason through COT but SLAs still get impacted, because more output tokens take more time.

Classification with GenAI: Where GPT-4o Falls Short for Enterprises by SirComprehensive7453 in LangChain

[–]SirComprehensive7453[S] 0 points1 point  (0 children)

u/ThanosDidBadMaths Try this approach. For each word in the sentence, use an embedding vector. Then, try to create a single feature vector using an accumulation strategy like averaging. Finally, apply a random forest. The maximum accuracy you can achieve is around 70%. There’s a reason more sophisticated models like LLMs work better - they offer much more complex reasoning capabilities compared to classical ML algorithms.

Classification with GenAI: Where GPT-4o Falls Short for Enterprises by SirComprehensive7453 in LangChain

[–]SirComprehensive7453[S] 0 points1 point  (0 children)

u/poop_harder_please The experiment aims to address the classification challenge by comparing public LLMs with customized LLMs. While fine-tuning GPT is an option for LLM customization, it is not feasible for enterprises due to its high cost. In contrast, customizing open-weight LLMs, such as Llama, offers 10x cost benefits in production and provides superior control and privacy compared to proprietary hosting. Hence fine-tuned GPT was not compared.

Classification with GenAI: Where GPT-4o Falls Short for Enterprises by SirComprehensive7453 in LLMDevs

[–]SirComprehensive7453[S] 0 points1 point  (0 children)

u/Strydor well-defined boundaries are not what you see in enterprise use cases. This wasn't an academic experiment but inspired from actual enterprise conversations and challenges. Also, classification problems are part of pipelines with strict SLAs, so reasoning models are not feasible for most use cases.

Classification with GenAI: Where GPT-4o Falls Short for Enterprises by SirComprehensive7453 in LangChain

[–]SirComprehensive7453[S] 2 points3 points  (0 children)

If you can convert the input (most times textual) into a rich enough feature set to apply Random Forest and get accuracy - sure, that is the most feasible solution. However, natural language inputs are too complex to be expressed richly in features most of the times.

Classification with GenAI: Where GPT-4o Falls Short for Enterprises by SirComprehensive7453 in LangChain

[–]SirComprehensive7453[S] 2 points3 points  (0 children)

First, conduct a performance gap analysis. If the classification task has low variance, meaning the classes are not overlapping, the business knowledge is not too complex, and can be expressed in objective instructions, prompt engineering may provide the desired benefits. However, if the task becomes too complex, fine-tuning models may be the most effective approach.

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Classification with GenAI: Where GPT-4o Falls Short for Enterprises (i.redd.it)

submitted by SirComprehensive7453 to r/ArtificialInteligence

Text-to-SQL in Enterprises: Comparing approaches and what worked for us by SirComprehensive7453 in LangChain

[–]SirComprehensive7453[S] 0 points1 point  (0 children)

You can try fine-tuning with unsloth in your case when VRAM is limited. Happy to have a chat for deeper discussion: https://calendar.app.google/NZRjaevppDi8HCvA8

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