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.
Payment Required
Launch Team
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.
I like the part abt capturing corrections and edge cases from real usage. That feels more useful than trying to guess everything upfront. One thing I wonder, how do you keep the model from leaaning the wrong patterns when user feedback is inconsistent or when diff experts correct the same situation in diff ways?
Empromptu AI
@busra_seker1 that's a great question. There is one ground truth so SMEs can override customers but SMEs have to agree what is ground truth
Empromptu AI
@busra_seker1 Exactly right on the ground truth architecture. On the inconsistency problem specifically, that's where the eval becomes the arbitration layer. Conflicting corrections don't both make it through, the eval scores against a defined expected outcome so noise and contradictions get filtered before they touch training. The model learns from signal that passed a quality bar, not raw feedback volume.
Empromptu AI
@busra_seker1 @sean_robinson1 Evals are the most important thing and yet some tools make evals really inefficient for people learning this technology to access. and take advantage of it's true power.
@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.
Empromptu AI
@jordan_hanson1 @xavair yes! It is. Whats unique about our platform is that we can agenticly get really high accuracy rates as we built a custom model ourselves to make corrections in real time dynamically. But the last percent you can correct responses. So because these tools are integrated into one platform you really get very high accuracy rates
Empromptu AI
@jordan_hanson1 @xavair Building on what Shanea said, the 98% is what the system converges toward over time, not where it starts. The baseline depends on how far the foundation model is from your specific domain out of the box. The more specialized your use case, the bigger the delta and honestly the more dramatic the improvement curve once your own production data starts feeding the loop.
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?
Empromptu AI
@antonio_manuel1 this is really a New paradigm. Subject matter experts can self-correct data to always ensure performance is high for the first time alongside a tool that feels like you're simply vibe coding
Empromptu AI
@antonio_manuel1 This is exactly the right question to ask and honestly one of the harder engineering problems we solved. Raw production data is noisy by nature so we never let it flow directly into training.
Every interaction gets scored against the eval you defined upfront. That eval is your ground truth and anything that doesn't meet the quality threshold gets filtered before it touches the training pipeline. Edge cases don't get discarded though, they get flagged for SME review because edge cases are often where the most valuable signal lives. The difference is they go through a human in the loop step before they become training data.
The other layer is the SME override architecture Shanea mentioned earlier. When experts disagree on a correction, the eval arbitrates. You're never training on conflicting signal, you're training on verified ground truth.
The result is a labeled dataset that stays small and clean over time rather than growing noisy with volume.
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.
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.
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.
Empromptu AI
@mehmet_s_taskesen This is exactly the framing that drove the original architecture decision. The problem was never that foundation models were bad. It was that every organization was essentially starting from zero every time because there was no infrastructure to capture what their best people actually knew.
The accountant who knows the exception. The support lead who knows when an escalation is real. That knowledge compounds inside a person over years and then walks out the door. Alchemy is fundamentally an infrastructure problem solved, not an AI feature added. The signal was always there in the corrections, the edge cases, the judgment calls. It just had nowhere to land permanently.
The no code angle is actually secondary to us. What we are really building is the layer that turns institutional knowledge into a durable asset that survives the people who created it.