Everyone is building voice AI agents. Almost nobody is testing them properly. Roark has processed over 10 million minutes of calls and wants to be the QA infrastructure the entire category is missing.
Most AI analytics tools generate dashboards. Operand wants to generate decisions. Three college dropouts from Dartmouth and Cornell are building AI systems that handle pricing, forecasting, and allocation for retailers and manufacturers. The pitch is less 'here's your data' and more 'here's what to do with it.'
Your customer data lives in Stripe, PostHog, HubSpot, Slack, and email. Outlit stitches it into one record and lets AI agents act on it. The positioning is smart. Whether they can hold the center against CDPs with a head start is the harder question.
Every developer has wasted a week integrating an API that should have taken a day. ReJot wants to replace the SDK-plus-backend-wiring pattern with reusable building blocks that include the data layer. Two ex-Adyen engineers from Amsterdam who spent years thinking about distributed data.
Grid-scale battery storage is a fast-growing, data-heavy market where operators are making million-dollar dispatch decisions with spreadsheets and stale reports. Dartboard Energy wants to replace the analyst, not the dashboard.
Growth teams drown in dashboards that answer the wrong questions. Lopus AI builds an agentic semantic layer that speaks your company's language and unifies structured and unstructured data into answers that actually make sense.
Most AI companies are obsessed with text and images. Overstand Labs is building foundational models for audio: speech-to-text, speaker diarization, and classification at scale.
Kafka is powerful, popular, and notoriously difficult to work with. Quix built a platform that lets engineering and data teams develop, deploy, and monitor stream processing pipelines without wanting to throw their laptops out the window.
Somewhere in your analytics dashboard right now, there is a growing pile of traffic you are almost certainly not thinking about correctly.
Most productivity tools try to stop you from wasting time. Shepherd just shows you how much you already have.
Every AI tool I've looked at this year answers questions. Viktor is the first one that seems genuinely annoyed by the idea.
SQLite for the web is a solved problem with about five solutions. Bunny Database is betting its CDN roots make it the one that actually sticks.
Every few months a new tool shows up promising to make data analysis accessible to people who don't speak SQL, and every few months I have to figure out if this one is actually different.
Everyone can generate a video clip now. Almost nobody can generate ten of them that look like they belong together.
Every AI analytics tool promises to answer your data questions. Cube says the others are lying to you — and it has 19,000 GitHub stars worth of infrastructure to make that case.
Your analysts are drowning in ad hoc requests, and your Slack is full of questions nobody can answer fast enough. Alkemi thinks it can fix that without anyone leaving the chat.
Knowing what software a company runs is only valuable if the data is fresh, sourced, and queryable — which is exactly what most technographics providers have historically failed to deliver.
Postgres scaling has always meant painful tradeoffs. PgDog, built by the engineer who sharded Postgres at Instacart during peak pandemic growth, wants to make horizontal scaling something you drop in rather than something you dread.
An SEO agency got tired of GA4 lying to their face, so they built the thing that actually tells you who's crawling your site — and it turns out, it's mostly not humans.
Your data pipelines are broken more often than you think. Acceldata built an observability platform that monitors data quality, pipeline health, and infrastructure costs so you find out before your CFO does.
Every AI analytics tool promises to answer your data questions. Relta says the others are guessing and it can prove its answers are correct through formal verification of generated SQL.
Morphik is an open-source retrieval platform that gives AI apps access to real documents instead of confident fabrications. Space-tech teams and brokerage developers are already using it.
nao is an open-source framework for building analytics agents that actually understand your data warehouse. Not another chatbot with a SQL generator bolted on.
Describe the prediction you want to make, connect your data, and Plexe builds, evaluates, and deploys the model for you. That is either the future of ML or a very good demo.
Lab instruments generate mountains of data that nobody can actually use. Labric wants to turn that mess into something AI can work with, and the timing might finally be right.
Researchers spend more time cleaning data than analyzing it. Percival writes the code, suggests the analysis, and interprets the results, all inside your existing workspace. It is what Jupyter notebooks would be if they were invented today.
Spreadsheets have not changed fundamentally since VisiCalc. BitBoard is building an AI-native version where you talk to an agent that builds, updates, and analyzes your data while you keep full control over every cell. Think of it as the spreadsheet rebuilt for people who would rather describe what they want than write formulas.
Every digital product generates conversation. Support tickets, in-app chat, feedback forms, onboarding flows. Costream is building infrastructure to capture, index, and make all of that dialogue searchable and actionable. Two Cornell dropouts who already scaled an AI product to 100K users think they can turn product conversations into structured intelligence.
Every AI team knows they need better evals. Most of them are still building annotation pipelines by hand, burning weeks on infrastructure that has nothing to do with their actual product. MangoDesk generates custom eval and post-training data pipelines in seconds. That is either a huge time-saver or a huge shortcut, and the difference matters.
Most BI tools demand weeks of setup, SQL fluency, and a dedicated analyst to get anything useful out of them. Pleom connects your data sources in seconds and generates AI-powered visualizations, workflows, and analytics with zero learning curve. Founded by an 18-year-old Georgia Tech ML grad, it is the anti-Tableau.
B2B sales teams spend hours stitching together data from ZoomInfo, LinkedIn, and a dozen other sources. Orange Slice replaces the entire enrichment workflow with a spreadsheet where every column runs AI-generated TypeScript, pulling from 100-plus data sources in one pass.
Cursor and Copilot built AI coding for software engineers. Lotas built it for data scientists who live in RStudio, write R, and have been completely ignored by the coding assistant wave.
Biology labs spend more time wrestling with data visualization than doing actual science. Novaflow takes raw experimental data, a plain English question, and returns publication-ready plots in minutes.
GPU data centers are burning through power at unprecedented rates and most operators have no idea how much they are wasting. DeepAware AI uses reinforcement learning to optimize workload placement and cut energy waste by up to 30%.
Traditional computer vision requires months of labeling data and training custom models. OnDeck uses vision language models to analyze video footage out of the box, no training needed, and their NeurIPS paper says it works better.
Voice AI is eating the world, but the training data supply chain is still held together with duct tape and crowdsourced recordings of questionable provenance. Panels wants to fix that with 10,000 vetted contributors across 100+ countries.
AI models that generate speech and video need training data that sounds and looks like real humans. Liva AI collects it with consent, diversity, and production quality baked in from the start.
Every sales engineer has experienced the empty-state demo disaster. Eden generates production-quality synthetic data on demand so your product looks like it has actual customers in it, without touching production databases or spending a week building seed scripts.
Every AI company needs human annotators but finding, vetting, and managing them is a nightmare. Fixpoint automates the entire pipeline from sourcing to quality control.
Google is no longer the only search engine that matters. ChatGPT, Perplexity, Claude, and Gemini are sending real traffic to real websites. Bear tracks where your brand shows up in AI recommendations and helps you show up more.
A million data points per game. Automatic play tagging. A transfer portal database with 2,400 players. PlayVision is turning game film into searchable, queryable intelligence for basketball programs that cannot afford a full analytics department.
Kafka is overkill for most streaming use cases and everyone knows it. s2.dev treats streams as a storage primitive with a REST API, sub-50ms latency, and no cluster to manage.
Spindl built the attribution and analytics platform that crypto companies needed years ago. Track where your users come from, what they do onchain, and pay the people who brought them in.
What if you could run a 10,000-person survey in an hour for pennies? Expected Parrot is building the tooling to simulate human research subjects with AI agents, and the implications for market research, product development, and social science are wild.
Business intelligence has been promising self-service analytics for fifteen years and mostly delivering dashboards that nobody updates. Scoop takes a different approach: connect your data warehouse, ask a question in plain English, and get an AI that actually investigates the answer.
Personalized messaging is supposed to be easy by now. It is not. Polymorph builds living user profiles that actually drive 3.6x conversion increases, and the numbers are hard to argue with.
Luel runs a two-sided marketplace where content creators get paid for their videos, voice, and images while AI companies get rights-cleared training data.
Rubric AI builds the human and computational layer that converts expert judgment into training signals for frontier models, focusing on regulated industries like healthcare.
Velum Labs monitors production query traffic to detect when teams calculate the same metric differently, traces root causes through query lineage, and generates enforceable data contracts to prevent future breaks.
Shofo aggregates public and private video sources into a continuously updating index of billions of videos, then cleans, searches, and labels them to build custom datasets for AI research labs.
Axis builds AI-powered trading systems that let commodities traders deploy models to monitor markets, analyze strategies, and execute trades across physical and financial markets.
Travo provides the most comprehensive real estate data set with instant access to rental comps, ownership, zoning, GIS data, and financial analysis for any property or parcel in the US.
Voxel Energy builds energy-independent data centers that capture, store, and consume solar energy onsite using second-life EV batteries and a novel DC microgrid architecture, bypassing utility grid connection delays entirely.
Librar Labs is building self-healing data infrastructure that turns fragmented library records into agent-ready systems. Their school library product reportedly doubles student reading rates.
Most AI training data has clear right answers. Traverse focuses on the hard part: capturing how humans make subjective decisions in law, healthcare, sales, and writing to train models that need judgment.
Menza connects to Shopify, Klaviyo, ad platforms, and 650+ other tools so any team member can ask questions in plain English. 100% customer retention since April 2025 and 234% MRR growth since January 2026.
Datost is an AI data analyst that lives in Slack, connects to your databases and data lakes, and lets anyone on the team query, debug, and analyze data right where they already work, without writing SQL or waiting for the data team.
The pitch is simple and kind of unsettling: let an AI observe everything your team does, find the patterns you never noticed, and start automating them.
Somewhere between 'just export a CSV' and 'we need to schedule a BI onboarding call,' there's a gap that Embedful is betting a lot of people actually fall into.
