mlops
(62)
Cloudangles wants AI to earn its keep
The company pairs its own software with the people who put it to work. Its most revealing stories begin with an unruly cloud bill and a price prediction that kept getting things wrong.
Intuitive.ai and the case for agreeing first
Before AI can give a useful answer, a company has to agree on the question. Intuitive.ai is building a business around that surprisingly difficult task.
Wits Innovation Lab builds the missing connections
A Shopify adapter. An artisan storefront. A team that stays through launch. Wits Innovation Lab’s most revealing work happens where one system has to meet another.
AI is loud. TWIML listens.
Sam Charrington built a business around asking AI researchers how things actually work. The interesting part is what happened when his listeners started learning together.
Practical AI makes the demo face reality
Two working AI practitioners built a show around a stubborn question: what happens when the technology leaves the demo? Eight years of conversations have made Practical AI a useful place to ask it.
io.net and the art of renting somebody else’s GPU
The AI compute shortage has an awkward companion: idle hardware. io.net wants to connect the two, but its most revealing lessons concern trust, incentives and the price of a finished job.
Anaconda made Python easy. Now it sells peace of mind.
The company that helped millions get started with Python found its paying customer in a different room: the enterprise worried about what all that free software might bring through the door.
Provectus puts AI where the work gets stuck
A pastry order, a hundred-page risk report, a support ticket: Provectus builds AI around the awkward business chores that hold everything else up. Its proposition is a working system, with the keys handed to the customer.
Blackstraw sells the work that comes after the AI demo
A model can impress a room and still disappoint a business. Blackstraw makes its living in that awkward interval, turning scattered data and promising algorithms into systems people can actually use.
MoBagel and the art of making AI earn its keep
A failed marketing model, a motorcycle-parts seller and a shipping forecast reveal MoBagel’s practical wager: AI becomes useful when it changes a decision someone already has to make.
Blaize and the Trouble with a Clever Chip
Blaize built a processor for AI that has to live on a power budget. Its harder experiment is turning clever silicon into a dependable business.
RandomTrees and the tyranny of the tiny task
An invoice, a shipping document, a database migration: RandomTrees builds AI around the work that quietly holds an enterprise hostage. Its bet is that reusable agents need a very particular kind of human help.
Tecton and the Price of a Millisecond
Tecton made a business of getting fresh data to a model before the moment to decide has passed. Its journey from Uber infrastructure to Databricks acquisition shows why the least glamorous part of AI can be the part customers pay for.
Simplismart and the Expensive Second After the Model Thinks
The glamour in artificial intelligence belongs to the model. The bill belongs to inference. Simplismart built a business in that awkward second between a clever answer and one that arrives fast enough, cheaply enough, and inside the right security perimeter.
Canonical gave away Ubuntu. Then it found the paying customer.
The company behind Ubuntu chased a phone that never shipped. Its more durable business is keeping other people’s open-source software running - from 5G networks to space-mission infrastructure.
Open Innovation AI wants to put your GPUs to work
Buying AI chips is the easy part. The Abu Dhabi company is building the software that lets enterprises share the compute, govern the models and keep the whole operation under their own control.
The Insurance Lab That Never Has to Pitch a Client
Liberty IT has spent nearly three decades turning Belfast engineering talent into software for one enormous customer: its parent, Liberty Mutual. Its best work shows what a captive technology company can do when cloud experiments are expected to survive contact with claims, compliance and millions of policyholders.
CapeStart's Real Product Is the Last Mile of Enterprise AI
Most AI projects do not die in the demo. They stall in the unglamorous stretch between a clever model and a system that regulated teams can trust. CapeStart has built a 600-plus-person business around crossing that gap.
Dataiku Built the Control Room for AI - Now It Has to Prove the Room Matters
The Paris-born software company made a $350 million-a-year business out of getting analysts, engineers and risk teams into one room. Its next act is deciding whether the agent boom needs a hall monitor - or a control system.
The Company Betting That AI Still Needs a Human in the Loop
Label Studio put data labeling in the hands of a million AI practitioners. Now HumanSignal is wagering that the harder generative-AI models get, the more they need human judgment behind them.
Sixteen Years, No Funding, and the AI Nobody Demos
ITRex Group has spent sixteen years turning other companies' hardest problems into working software. Now it is doing the same with AI - the part that runs in production, not the part that looks good on stage.
The Database Fixers Who Want to Keep Enterprise AI Alive
Pythian spent nearly three decades learning how enterprise data breaks. Now it is using that operational muscle to move AI out of the demo room and into the systems companies cannot afford to lose.
TensorPool
TensorPool (YC W25) is a San Francisco startup that turns cloud GPUs into a git-style command line. Instead of wrestling with SSH sessions, provider dashboards, and idle-instance billing, machine learning engineers describe a training job in a config file and run 'tp job push' - TensorPool spins up a multi-node cluster across partner clouds, streams logs, saves outputs, and shuts everything down, billing only for runtime. It advertises GPU access at roughly half the cost of major cloud providers, with H100s starting around $1.99/hr. Founded by three Stanford dormmates, the company was accepted into Y Combinator's Winter 2025 batch.
Understudy Labs
Understudy Labs is a San Francisco AI infrastructure startup (Y Combinator S26) that helps data-rich teams replace expensive frontier LLMs with smaller, specialized models they own. Its open-source toolkit captures production traces from existing agent workflows, benchmarks cheaper open-weight models against them, and only ships a replacement once a held-out evaluation beats the incumbent - promising comparable quality at a fraction of the cost and latency.
Astronomer
Astronomer is a New York-based data infrastructure company and the commercial force behind Apache Airflow, the open-source standard for data orchestration. Its flagship product, Astro, is a fully managed, cloud-native platform that lets data teams build, run, and observe data pipelines at scale, and its Astro Observe layer extends that into data operations and observability. Astronomer employs a large share of Airflow's core committers, serves 700+ enterprise customers, and raised a $93M Series D in May 2025 to power orchestration for enterprise AI workloads.
Sigmoid
Sigmoid is an AI-first data solutions company that helps large enterprises turn complex data into measurable business value. Founded in 2013 by three IIT Kharagpur batchmates, the firm pairs deep data engineering with data science, MLOps, generative AI and agentic AI to build and operate the pipelines, models and analytics that Fortune 500 companies in CPG, retail, financial services and life sciences rely on for faster, data-driven decisions.
Thread AI
Thread AI is a New York enterprise software company building composable infrastructure for AI-powered workflows. Founded in 2023 by former Palantir AI/ML leaders Angela McNeal and Mayada Gonimah, its flagship platform Lemma pairs a drag-and-drop workflow builder with orchestration infrastructure so organizations can connect disparate systems, data, and AI models into governed, observable, end-to-end workflows - without ripping out their existing stack. The company raised a $20M Series A in 2025 to help enterprises and public-sector agencies deploy mission-critical AI with control, traceability, and security.
CLOUDSUFI
CLOUDSUFI is a Silicon Valley data-driven digital transformation company that helps enterprises turn raw, siloed information into decisions - a mission it captures in the phrase 'we make enterprise data dance.' Founded in 2019 and headquartered in San Jose, California, the firm pairs data engineering, advanced analytics, and generative AI with a Google Cloud Premier partnership to modernize data platforms, automate supply chains, and unlock data monetization for customers across manufacturing, retail, financial services, energy, and digital-native industries.
DataPattern
DataPattern is a San Ramon, California based IT services and consulting firm founded in 2019 that helps regulated and data-heavy enterprises move from legacy systems to modern data, AI and IoT platforms. Its teams build data engineering pipelines, generative AI and machine learning solutions, IoT use cases, cloud migrations and DevOps/MLOps practices, with a stated emphasis on data governance, quality and lineage as the foundation for reliable AI. The company works across manufacturing, healthcare, finance and energy, positioning itself as a hands-on transformation partner rather than a single-product vendor.
DataRobot
DataRobot is an enterprise AI platform company founded in 2012 that helps organizations build, deploy, govern, and monitor predictive and generative AI. A pioneer of automated machine learning (AutoML), the company has since expanded into MLOps, generative AI, and agentic AI, letting technical and business teams move models from experimentation into production with governance and observability built in. More than 1,000 organizations - including enterprises, hospitals, and government agencies - rely on its platform, now centered on an Agent Workforce Platform co-engineered with NVIDIA.