Empromptu AI

Train Fine Tuned Models With AI Apps You're Already Building

Developer Tools

Artificial Intelligence

No-Code

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Parameter efficient fine tuning methods like LoRA have changed the economics of this space significantly does Empromptu leverage these under the hood and does the developer have any control over the fine tuning strategy being applied?

As a tool in the 'Vibe Coding' space, how much control does a user retain over the underlying architecture? If the conversational builder creates the full stack, is there a way to export the code or infrastructure configurations, or are users locked into the Empromptu ecosystem?

the feedback loop approach is smart. the part that usually trips teams up isnt the training pipeline though, its the quality of the corrections feeding it. if the humans correcting the AI output dont have a systematic way to evaluate whats actually wrong you end up fine-tuning on noise. curious how you handle that signal quality problem

How does Empromptu approach the tricky intersection of user privacy and training data collection specifically how do you help developers stay compliant when end users haven't explicitly consented to having their interactions used for model training?

Continuous fine tuning from live data sounds powerful but it also risks model drift over time how does Empromptu protect against a model that gradually shifts away from its intended behavior as usage patterns evolve?

Woo, love seeing this ship! Already mulling through some of the fun stuff I could add to my companies in terms of being able to fine tune some models, hah.

The positioning of apps you're already building is really compelling what does the actual developer integration look like and how invasive is the instrumentation required to start capturing usable training data?

Most fine tuning tools treat evaluation as an afterthought does Empromptu have a built in framework for measuring whether a fine tuned model is actually outperforming the base model in production not just on a held out test set?

the self improving AI angle is really interesting. how do you balance continuous learning with maintaining model stability and consistency? can customers roll back changes if needed?

One concern enterprises always raise around fine tuning pipelines is data residency can you speak to how Empromptu isolates customer training data and whether it ever touches shared infrastructure?

Congratulations on the launch!
how does alchemy decide which user corrections are valuable enough to incorporate into future model updates?can teams review or approve those learning cycles before deployment?

I keep thinking about how much institutional knowledge disappears when someone leaves a company. Most organizations have years of expertise locked inside conversations, corrections, and unwritten rules. The idea of turning those signals into a continuously improving system feels like a much bigger opportunity than no-code app building itself.

Fine tuning from live app behavior raises an interesting data quality challenge what mechanisms does Empromptu use to filter out bad or edge case interactions that could quietly degrade model performance over time?

The idea of turning your own app's usage into fine tuning data is genuinely clever but how do you handle the cold start problem for teams whose apps don't yet have enough interaction volume to generate meaningful training signal?

@jordan_hanson1 congrats guys, about the 98% figure. Is that something customers typically achieve after the model has learned from their application data, or is that the starting point?

It would be interesting to understand what the baseline was before the learning process.

@shanea_leven Congratulations on the launch.

One thing I’m trying to understand from your positioning. If the underlying model providers keep improving rapidly every few months, how do you measure whether the gains your customers see are actually coming from Empromptu’s learning layer versus improvements in the foundation model itself?

It seems like that’s a pretty important distinction because both could lead to better outputs over time, but only one creates a real competitive advantage for the customer. Are you able to quantify that difference in a meaningful way?

Can you walk us through what AI apps you're already building means in practice are you integrating with specific frameworks like LangChain or LlamaIndex?

Love the name Alchemy, it makes sense to own the outputs and control your own data.

Congrats on the launch 🎉
Curious, when the dynamic prompt optimization kicks in after 30 runs, does the user get visibility into what changed, or does it just happen silently in the background?