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

@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.

@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

@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!

@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.

@daniel_juan2 automatic drift detection for the win! We think about all of these things on a daily basis

@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.

@joshua_cooper2 Building on the ground truth architecture we designed around, every correction gets scored against the eval before it ever touches training. The eval is what decides whether a correction is signal or noise, not volume, not recency, just whether it meets the quality bar your team defined.

On the review question, yes completely. Nothing deploys silently. Teams get full visibility into what corrections are queued, can approve or reject learning cycles manually, and can also trigger retraining on demand for the tinkerers who want that control. The autonomy is configurable depending on how much oversight your team wants in the loop.

@joshua_cooper2 yes absolutely we give teams full autonomy to correct responses. We actually encourage this as it increases accuracy

@joshua_cooper2 actually Alchemy doesn't, you (or your SMEs) would, and our system can adapt to what 'good' looks like and get even more accurate over time.

@carlos_leonardo1 Data isolation is non negotiable for the customer segments we serve. Healthcare organizations and financial workflows were in production on Empromptu before we ever talked to a general market so the architecture was built around those requirements from day one, not retrofitted later.

Every customer's training data is fully isolated. Your corrections, your edge cases, your labeled dataset never touch shared infrastructure and they never inform another customer's model. The model you train is trained exclusively on your data and the weights are yours.

The broader point is that data residency is actually core to the product thesis. The reason Alchemy exists is that your data should build your asset, not someone else's. Letting customer training data bleed across tenants would contradict the entire value proposition.

@carlos_leonardo1 we take privacy and data very seriously

@carlos_leonardo1 your data is your data! we don't mix up those things, and we believe it so strongly that we have created a pathway for you to train weights that you own (and even export them / host them somewhere else, if you needed to, for whatever reason).

@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.

@carter_son yes you can actually see this live in our dashboard!

@carter_son Absolutely; our vertically-integrated approach begins with consistent, reliable evaluation as a precondition to everything else that comes after.

@new_user___10520260379921a76fc2d64 Great question!! We've actually built in a data anonymizer! to randomize any PII before it goes into model training.

@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.

@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.