Today we're so excited to announce AI Policies, a new platform capability that gives enterprises a centralized, compliance-ready way to govern how AI applications are built across their organization.
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Today I'm so excited to introduce Golden Pipelines integrated directly into our AI App Builder that ingests, structures, cleans, and generates data to power reliable, production-ready AI applications.
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Empromptu AI, the company leading enterprises through the transition of static SaaS to self-improving AI-native applications, today announced an oversubscribed $2 million pre-seed round to accelerate development of its Self-Managing Context Engine: a breakthrough technology that allows AI features to manage, train, and improve themselves in production.
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We thought businesses wanted better AI optimization. After 200+ customer conversations, we learned they wanted something completely different: AI systems they could actually ship to production.
Read MoreThe most successful AI development teams have discovered a counterintuitive truth: the more structured your setup, the more creative freedom you gain during actual development. While 92% of developers now use AI coding tools according to GitHub's 2024 Developer Survey, the productivity gains vary dramatically—and the difference lies in preparation.
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Empromptu AI, the company leading enterprises through the transition of static SaaS to self-improving AI-native applications, today announced an oversubscribed $2 million pre-seed round to accelerate development of its Self-Managing Context Engine: a breakthrough technology that allows AI features to manage, train, and improve themselves in production.
We thought businesses wanted better AI optimization. After 200+ customer conversations, we learned they wanted something completely different: AI systems they could actually ship to production.
The most successful AI development teams have discovered a counterintuitive truth: the more structured your setup, the more creative freedom you gain during actual development. While 92% of developers now use AI coding tools according to GitHub's 2024 Developer Survey, the productivity gains vary dramatically—and the difference lies in preparation.
The AI coding revolution promised to transform how developers work with complex codebases. Yet according to recent industry data, we're seeing a surprising disconnect between expectations and reality. A comprehensive study by METR found that experienced developers using AI tools actually took 19% longer to complete tasks than those working without AI assistance—despite predicting they'd be 24% faster.
As AI-generated code floods production systems, the security community has scrambled to develop tools that can actually catch the vulnerabilities that traditional code review misses. The challenge is unique: AI-generated code often passes functional tests while harboring serious security flaws that only become apparent under specific conditions.
AI builders promised that anyone could build sophisticated applications. Some even claimed it would be the last piece of software we'd ever need to build. And here's the dirty little secret: they're full of hallucinations, inaccuracies, and leave you with a ton of work to get it deployed in any business, nevermind an enterprise environment.
While AI development tools make it easier than ever to build sophisticated applications, they don't change the fundamental legal reality: if you collect user data, you're legally responsible for protecting it. And the consequences of failure aren't just technical—they're financial, reputational, and in some cases, criminal.
Every week, I see another "built this in 30 minutes with AI" post on social media. The screenshots look impressive—polished interfaces, smooth user flows, features that would have taken traditional teams days to implement. But there's something these viral success stories don't mention: what happens after the demo.
While everyone's celebrating how AI tools like ChatGPT and Cursor can build entire apps in minutes, the security community is watching in horror as vulnerable code floods production systems. Recent data from Apiiro shows that AI-assisted developers are creating 3-4 times more security vulnerabilities than traditional coding approaches—and most developers don't even realize it's happening.
Every finance team knows the pain: stacks of invoices waiting for manual data entry, human errors in transcription, and hours spent on repetitive work that could be automated. What if you could build an AI-powered invoice processor that extracts data with confidence scoring and handles multiple invoice formats—actually working reliably in production?
At my last company, sales was the thing that broke me. I cried every day trying to learn it. Rejection after rejection—it was a gut-wrenching, vulnerable, and downright terrifying place to be as a technical founder.
Customer support teams face a constant challenge: answering the same questions repeatedly while ensuring consistent, accurate responses. What if you could build an AI assistant that instantly answers customer questions by searching through your company's documentation—and actually works reliably in production?
Remember when we used to hire designers, devs, SEO freelancers, and project managers to build a startup website?
In today's fast-paced AI landscape, businesses deploy sophisticated AI applications to gain competitive advantages. However, these advantages quickly diminish when AI systems produce inaccurate, inconsistent, or unreliable outputs. This is where AI application monitoring becomes essential - not just to observe performance but to actively improve it.
OpenAI’s Practical Guide to Building Agents outlines a visionary framework for LLM-driven agents. But following that guide from prototype to production isn’t easy. That’s where Empromptu comes in.
