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
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.
Empromptu AI
@mehmet_s_taskesen we totally and completely agree. Better yet. What if you could monetize it as well?
Empromptu AI
@mehmet_s_taskesen Right? this is the "what if [any person you rely on] gets hit by a bus problem" -- just SOLVED by AI systems that can basically build / learn / do anything you can plan and train.
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?
Empromptu AI
@andrew_paul11 that's the beautiful thing about the app creating the data. The very first time you run the app it creates data!
Empromptu AI
@andrew_paul11 The part that makes that work under the hood is that even a small number of high quality runs beats a large volume of synthetic data. Because the signal comes from real interactions against your actual eval, even early data is already shaped around your domain. You're not waiting for volume, you're waiting for signal and those are very different thresholds.
@andrew_paul11 From a true cold start you generally will want to use the SOTA frontier model to get the best results you can for first use cases. Then once you've built up a small corpus you can use that to fine tune your own model and get improved results for lower cost at volume compared to always using the off the shelf frontier models.
Empromptu AI
@andrew_paul11 There are a few ways, including generating simulated responses, or actual responses using calls that mimic an array of user intents to create that volume, and in each case we incorporate the way the user interacts with the training data set to help us understand what "good" looks like in terms of training directionality.
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?
Empromptu AI
@ana_popescu2 no. We built our own proprietary frameworks specifically designed to get highly accurate AI outputs. So many of our users build their entire products on our platform like we have had users build Soc 2 platforms, AI tax products, full healthcare products for their communities. We have a grant program winner who is building AI workflows for airlines! This is sophisticated tools that ml and ai engineers use made accessible for everyone
Empromptu AI
@ana_popescu2 To double click on Shanea's point, the proprietary architecture is what makes those wildly different use cases possible on the same platform. The fine tuning loop needs to close reliably whether you're building a healthcare product or an airline workflow, and that's a very different design requirement than general purpose orchestration frameworks are built for.
Empromptu AI
@ana_popescu2 No - we have a full builder, supported by function-focused templates in our new Studio area in the platform (studio.empromptu.ai), the planner, and the Builder, all of which dramatically accelerate time-to-market.
We also enable people coming from other builders like Lovable or Claude Code or Codex, where you can / probably do have a Github Repo connected, to just simply connect that repo to Empromptu and begin integrating their functions to our vertical-integration & optimization stack.
@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.
Empromptu AI
@xavair This data comes from a few different examples, but I think instead it's better to simply reframe: think about the accuracy gains in terms of opportunity cost, where 30% is always relatively better. Our platform optimizes toward the outcomes the user specifies, and the training data helps us understand and contextualize the decisions and edge cases that make each individual model characteristically unique.
In my experience, I've found that the foundation builder assistants sort of lose things by contrast to the degree that now when I'm building outside Empromptu, I feel like my personal environment is missing things that I try to build, but they're always worse off and they always break. The contrast, again, is that the functions I build on the platform work, because they're built and deployed at production-grade standards, and that extends to the way the model is being refined in real time, as I help train it on edge cases, because I find it needs me to nudge and correct it less and less.
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.
Empromptu AI
@antonio_manuel1 The most common way is that the way you train the model gives us some sense of directionality, but we also have some proprietary systems that help us optimize toward 'good' automatically.
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?
Empromptu AI
@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.
Empromptu AI
@joshua_cooper2 yes absolutely we give teams full autonomy to correct responses. We actually encourage this as it increases accuracy
Empromptu AI
@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.
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?
Empromptu AI
@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.
Empromptu AI
@carlos_leonardo1 we take privacy and data very seriously
Empromptu AI
@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).