Adaptive ML

The RL conversation has left the research lab. At @NVIDIAGTC this week we’ve talked to teams from @Google, @AIatMeta, @Microsoft, @Apple, @awscloud and so many more. Everyone is asking the same question: how does RL fit into our existing pipelines? Not a research question. An ops question. The pattern is consistent. Companies prototype on frontier models, then move to open source for production. They want performance, control and lower cost. Specialization is how you get all three. Everyone is thinking about RL. Almost nobody has the tooling to do it at scale. That's exactly what we built Adaptive Engine to solve. Adaptive Engine. The RLOps platform for enterprise AI. Book a demo: lnkd.in/emh29BMU

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$500 billion to $1 trillion. One quarter. That's how fast the inference era is arriving, as Jensen Huang stated at @NVIDIAGTC this week. The enterprises that come out ahead won't be the biggest consumers of AI infrastructure. They'll be the ones that started owning their intelligence layer, their AI future. Specialized models, private deployments, continuously improving through reinforcement learning. The window to get ahead of this is now. Our team is on the ground at GTC. Book time with us here - calendar.app.google/Br8gHLrcXTzAcB…

Day 1 is well and truly underway at @NVIDIAGTC, after a great keynote from Jensen Huang. The team are ready to talk all things AI, but more specifically about how reinforcement learning will help your organization level up your industrialized AI Agents. 📍Meet us at booth 4024 in the NVIDIA Inception Startup section. The team onsite includes Joy Boulbil - Chief of Staff, Andrew Jardine - Head of Sales, Jack Langenback - Enterprise GTM, Wyatt Kyte - Data Engineer, William Trefiak- GenAI Solution Advocate. Book a meeting with the team here - calendar.app.google/Br8gHLrcXTzAcB…

.@NVIDIAGTC starts today in San Jose. The Adaptive ML team is on site this week meeting with companies building enterprise #AI systems. At our booth, we’ll share how organizations are using #ReinforcementLearning to create, train and deploy their own proprietary Specialized Language Models. If you're attending GTC, come meet the team. Andrew Jardine - Head of Sales Jack Langenback - Enterprise GTM Wyatt Kyte - Data Engineer William Trefiak- GenAI Solution Advocate 📍 Booth 4024 Book a meeting in the calendar - calendar.app.google/Br8gHLrcXTzAcB…

The world's first Reinforcement Learning Ops platform is changing what enterprise AI actually costs. Most large organizations start with a generalist LLM. It's flexible and capable, but it's expensive and not built for the specific tasks your business runs at scale, and that becomes a serious problem. Adaptive Engine takes smaller, open-source models and specializes them using reinforcement learning. The result is a Specialized Language Model purpose-built for a specific industrialized task, outperforming generalist LLMs at a fraction of the cost. For some of our customers, that's translated into millions of dollars of savings. Generalist models are built for everyone. Specialized models are built for your specific business tasks. That's what Adaptive Engine is. And that's what #RLOps makes possible. If you're heading to @NVIDIAGTC in San Jose from March 16 to 19, come find us at booth 4024. You can drop us a meeting request using the link below and we'll get something in the calendar. calendar.app.google/Br8gHLrcXTzAcB…

Book a meeting now using the following link: calendar.app.google/Br8gHLrcXTzAcB…

5 days to go until @NVIDIAGTC. Book a slot with us before the calendar fills up. Meet the team, discuss how enterprises move from #AI experiments to production systems, and discover a few sessions we recommend catching at the event. 📍 Booth 4024 NVIDIA GTC · San Jose · March 16–20 Request a meeting. Our team: Andrew Jardine - Head of Sales, Jack Langenback - Enterprise GTM, Wyatt Kyte - Data Engineer and William Trefiak - GenAI Solution Advocate

Generalist AI works well for creative tasks and one-off questions. But when a business runs the same process millions of times a day, that's where specialization wins. As our CEO Julien Launay told @Challenges: "We're moving from artisanal intelligence to industrial intelligence." That's exactly what we do at @AdaptiveML. We use reinforcement learning to fine-tune smaller, open-source models for specific enterprise tasks, customer support, fraud detection, document analysis. The result is a model that outperforms the big generalists on the tasks that matter, at 50 to 90% lower compute cost. The enterprises that figure this out first don't just have better AI. They have a compounding advantage. Full article in french below: challenges.fr/entreprise/tec…

@MWCHub Want to find out more? Book a demo with our team today - bit.ly/4csSMR2

With @MWCHub closing, it’s clear that the telecoms industry is leading the way in Enterprise AI. High call volumes. Complex customer journeys. Legacy infrastructure. The margin for error is thin, and the cost of a slow or inaccurate model adds up fast. We're working with some of the world's largest telcos to swap generalist LLMs with specialized agents and deploy them in production at scale. With AT&T we generated significant cost reduction using reinforcement learning to fine-tune a specialized model for customer support functionality that handles nearly a million requests per day.

Fine-tuning isn't just a technical step. It's how organizations take ownership of their AI, specialize it for the tasks that matter, and make it work in the real world. Check out the full AI Magazine list here: aimagazine.com/top10/top-10-l…

Proud to be recognized as one of the top LLM fine-tuning platforms driving enterprise adoption in 2026. At Adaptive ML, we built Adaptive Engine — the RLOps platform enterprises need to bridge the last mile from generalist prototypes to autonomous production.

"The value of products and services is increasingly defined by AI capabilities. If you're buying these capabilities off the shelf, it means your competitors can too. And then how do you differentiate yourself?" You can read the full interview here - sifted.eu/articles/adapt…

The answer isn't the biggest model or the latest release. It's owning the system entirely, from the model itself to how it's deployed and improved over time.

95% of enterprise generative AI pilots have failed to deliver financial returns. That's not a technology problem. It's a specialization problem.

The North American market exploded in 2025. There's no reason Europe can't follow in 2026. Read the full interview (in French): usine-digitale.fr/entretien/les-…

As Julien put it: "These technologies will redefine economic growth. If we fall too far behind, the cost will be massive." Today, North American clients like AT&T, Manulife, and SK Telecom are already using Adaptive Engine to deploy specialized AI agents at scale.

Enterprise AI is scaling faster in North America than in Europe. And the gap isn't about technology. Our CEO, Julien Launay, spoke with @lusinedigitale at the @WAICANNES about what's driving that divide.