Empromptu empowers businesses to build full-stack, AI-native applications in minutes—no code required—by combining a conversational builder with powerful agents that handle data ingestion, logic, and deployment.
This is the 2nd launch from Empromptu AI. View more
Empromptu AI
Launching today
Most AI apps launch on someone else’s model and stay there forever. Empromptu AI turns live AI features into custom models you own. As your app runs, Empromptu AI captures real-world usage, human corrections, and edge cases from live AI workflows, then uses that signal to train a custom model you own. Improve accuracy, lower inference costs, and stop depending forever on rented intelligence from the same providers moving into your category.
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Launch Team
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?
Empromptu AI
@carter_son Worth addressing directly because you're pointing at a real gap in how most fine tuning tools are built. Held out test set performance is a proxy metric. What actually matters is whether the model makes better decisions in your specific production environment and those are often very different things.
The eval you define in Alchemy is not a one time benchmark, it runs continuously against live production interactions. So you are always measuring against real user behavior, real edge cases, real domain conditions rather than a static slice of data you curated before deployment. The comparison to the base model is ongoing not a one time checkpoint.
The other piece is that because the eval is domain specific and defined by your SMEs, the performance measurement is actually meaningful to your business. You are not optimizing for a generic accuracy score, you are optimizing for the exact outcomes your team defined as correct.
Empromptu AI
@carter_son yes you can actually see this live in our dashboard!
Empromptu AI
@carter_son Absolutely; our vertically-integrated approach begins with consistent, reliable evaluation as a precondition to everything else that comes after.
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?
Empromptu AI
@chen_hao3 To make that concrete, the instrumentation is intentionally lightweight. You are building on Empromptu's platform so the capture layer is built in, not bolted on. There is no separate SDK to integrate, no custom logging pipeline to stand up, no data pipeline to maintain.
The signals Alchemy needs, corrections, edge cases, application feedback, are byproducts of normal app usage on the platform. The developer experience is just building the app. The training data infrastructure runs underneath it automatically.
That was a deliberate architecture decision. If instrumentation requires meaningful engineering work it becomes a project that competes with shipping. We needed it to be invisible so teams could focus on the application layer and let the learning layer take care of itself.
Empromptu AI
@chen_hao3 said in another way it feels like you're vibe coding but then you start to notice all of the things devs need are there
Empromptu AI
@chen_hao3 It isn't invasive at all! We've set up a 'Try it free' pathway so you can see for yourself, and if you sign up with the 'Launch' plan by the end of June, we'll add a 1,000 credit bonus after your account is active for 30 days!
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?
Empromptu AI
@daniel_juan2 The eval is the anchor. No matter how much production data flows through, the model can only update in directions that pass the ground truth your SMEs defined upfront. Usage patterns evolve but correct stays fixed until you deliberately change it.
The versioned checkpoint architecture handles the rest. Every training cycle is inspectable and fully reversible so drift never accumulates silently. Healthcare and financial workflows shaped these requirements from day one, silent drift was never an option.
Empromptu AI
@daniel_juan2 automatic drift detection for the win! We think about all of these things on a daily basis
Empromptu AI
@daniel_juan2 Drift's the thing you have to design against from day one. Our posture is that continuous fine-tuning only works if every update is checked against a stable baseline before it ships, so you're catching regressions instead of discovering them later. Happy to go deeper if useful.
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?
Empromptu AI
@new_user___10520260379921a76fc2d64 Great question!! We've actually built in a data anonymizer! to randomize any PII before it goes into model training.
Empromptu AI
@new_user___10520260379921a76fc2d64 @shanealeven Building on that, the anonymizer runs automatically before any interaction touches the training pipeline so PII never reaches the labeled dataset in the first place. The compliance burden on the developer side is significantly reduced because the infrastructure handles it at the platform level rather than requiring custom data handling logic in every application.
Empromptu AI
@new_user___10520260379921a76fc2d64 we have several mechanisms to securely protect end-user data, and also offer the ability to mechanically substitute the 'data' from the 'identifying components' through 'john doe' substitutes that makes PII attribution impossible, especially in training applications.
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?
Empromptu AI
@nurlyzhann Yep! We enable you to connect to Github; as you mentioned, many of our users originate in Lovable or Claude Code and so this is a common onboarding pathway.
The 'bring your own expertise' angle is the right way to think about the next wave of AI. We struggle constantly with customer support AI missing the nuance of our specific software updates. If this plugs directly into customer usage signals to self-improve, it solves a massive operational headache. Amazing job @shanea_leven
Empromptu AI
@priya_kushwaha1 we agree this power is now really accessible for anyone to be able to fine-tune. And yes fine tuning with your own data does absolutely increase accuracy
Empromptu AI
@shanea_leven @priya_kushwaha1 And the part that usually surprises people is how small that labeled dataset needs to be when the signal is right. Real production corrections from your own users generalize far better than anything synthetic at 10x the volume.
Empromptu AI
@shanea_leven @priya_kushwaha1 We think this is a great application! Helping companies improve customer service outcomes, at scale, by training resolution frameworks on established policy and handling procedures that preserve brand credibility while also centering the interaction on the real-world use case from which the rougher edge cases most commonly originate.
the 'in minutes' claim for full-stack AI native applications is the part that creates the most skepticism. building something that demos well in minutes is straightforward. building something that handles production edge cases, scales appropriately, and doesn't require significant rework when requirements change is a different problem. what does a typical application look like 30 days after the initial build and how much ongoing maintenance does it require from a non-technical user