Lovelace

We make autonomous agents work for mission-critical analysis. We bring a new level of rigor and accuracy to enterprise AI decision-making. // Founded by @awm_ai

[Lovelace @LovelaceWorks]

Can an agent powered by a lightweight LLM and the Lovelace YottaGraph (and nothing else) provide deep research-grade reports significantly cheaper and faster than a flagship Deep Research model? The answer is yes. Here's how.

https://t.co/9kJsV3wV5C

[Andrew Moore @awm_ai]

I put pen to paper on my passion. We AI technologists can all do better than relying on LLM-only reasoning when the questions we are asking are deep. There are other ways to compute, and I believe deep AI investigations are best made practical with great computer science, not great amounts of new data center power generation. https://t.co/DbAe2CEohM

[Lovelace @LovelaceWorks]

Per CEO Andrew Moore: "We made a decision early on. Before Lovelace came out of stealth, we would prove our technology on the hardest dataset possible: the world itself...We call this work the YottaGraph.“ https://t.co/bCcVIFJhKX

[Lovelace @LovelaceWorks]

@Its_Nova1012 @karpathy @ylecun @fchollet @AndrewYNg @lilianweng @demishassabis @drfeifei Would add @awm_ai to that

[Lovelace @LovelaceWorks]

What is a context engine, and why does it matter? AI agents are powerful reasoners, but they can only reason over what they can see. In most enterprises, what they can see is a mess: siloed databases, unstructured documents, disconnected systems that were never designed to talk to each other. Point a powerful model at that chaos and you get hallucinated connections, incomplete answers, and token costs that spiral as your data grows.

A context engine sits between the chaos and the model. It ingests raw data in real time, resolves entities across sources with 99.5%+ accuracy, maps everything into a scalable knowledge graph, and exposes that graph to agents with full citations on every fact. The result is agents that can investigate complex problems with the depth of a research team and the speed of a search engine. That's Lovelace's Elemental. If your enterprise AI is underwhelming, the model probably isn't the problem. The context is.

[Lovelace @LovelaceWorks]

We couldn't agree more — "The most interesting enterprise AI systems emerging today do not start from a prompt in the narrow sense; they start from context: persistent, structured, governed context."

[Enrique Dans @edans]

"When enterprise AI finally works, it won’t look like AI", my article on @FastCompany
https://t.co/wLgmWZZ9Cg

[Lovelace @LovelaceWorks]

Enterprise AI has a dirty secret: it mostly doesn't work for the questions that matter most. Chatbots work. Summarization works. But when an executive needs to know whether today's news threatens their loan collateral, when a customs inspector needs to know which of five hundred ships entering port is worth boarding, when a disaster coordinator needs to know which evacuation route is actually passable right now, the AI fails. Not because the models aren't powerful. Because they have no idea what's actually true about your world.

The real bottleneck isn't the model, it's context. Specifically, the lack of a structured, trusted, verifiable layer sitting between raw enterprise data and the AI doing the reasoning. Without it, you get hallucinated connections, incomplete answers, and costs that spiral as data grows. With it, you get agents that investigate, synthesize, and decide, not just summarize and chat. That's the gap Lovelace closes.

https://t.co/jtqenpUjbN

#enterpriseai #aiagent #contextengine #knowledgegraph #aistrategy #worldmodel

[Lovelace @LovelaceWorks]

Lovelace Emerges to Launch Context Engine Platform for AI Agents https://t.co/F3kUIxRjdP

[Lovelace @LovelaceWorks]

“This is the story of what we made, why we made it, and why I believe it changes something fundamental about what enterprise AI can accomplish.” Read Andrew Moore's article on @medium: https://t.co/XM5VByek7q

#enterpriseai #aiagent #contextengine #knowledgegraph #aistrategy #worldmodel

[Lovelace @LovelaceWorks]

https://t.co/DdFKy5EP21 Great reporting @TheFuturumGroup

[CIO.com @CIOonline]

Startup tackles knowledge graphs to improve AI accuracy
https://t.co/UwrqQ11PKu

[Lovelace @LovelaceWorks]

Lovelace CEO, Andrew Moore on why context is the defining problem in AI right now. Your enterprise AI is only as good as what it knows. And right now? It doesn't know enough. @awm_ai

https://t.co/U23ZZRLtnU

#enterpriseai #aiagent #contextengine #knowledgegraph #aistrategy #worldmodel

[Lovelace @LovelaceWorks]

@edans @FastCompany We couldn't agree more, and we have the fix. Our context engine builder, Elemental. We're out of stealth today. https://t.co/cgThPI0xTL

[Andrew Moore @awm_ai]

Today is a big day for us.

When we started this company, we had one conviction: that AI needed to be worthy of the decisions that truly matter. Not helping with homework or recommending the next song to play, but the decisions where fortunes are won or lost, lives are on the line, and national security cannot afford to be wrong. The more we dug in, the clearer the obstacle became. It was never the quality of the models. It was the absence of a structured, verified, agent-navigable layer of context sitting between those models and the chaos of real data.

We kept running into this problem everywhere we looked, so we stopped working around it and built the solution. Lovelace's answer? Instead of waiting for models to learn the world, we give agents the context they need to navigate it today, built from your data, verifiable at every step.

[SiliconANGLE @SiliconANGLE]

Startup Lovelace targets contextual AI engine at mission-critical use cases https://t.co/4lpZFYoGTP

[The New Stack @thenewstack]

Lovelace founder Andrew Moore says enterprise AI keeps failing investigative tasks. His answer is a context engine that claims 1000x the investigative power at 1/1000th the token cost — built for environments where being wrong is not an option. https://t.co/HfXzKfldLy

[Andrew Moore @awm_ai]

This past year at Lovelace, I've been working with some of the best ML engineers in the world. We’ve solved a critical piece of enterprise AI. Stay tuned.

[Lovelace @LovelaceWorks]

Lovelace is officially out of stealth. Today, we're introducing Elemental, an enterprise context engine platform built for speed, scale, and accuracy in high-stakes environments – finance, national security, supply chain, and beyond.

Here's the problem: as enterprises deploy AI agents into increasingly complex and dynamic environments, the lack of reliable context has become a critical barrier to adoption. AI agents are powerful, but without verifiable context, they can't be trusted when the stakes are high. Elemental fixes that.

Elemental is a context engine builder that dramatically increases the investigative power of AI agents by 1000x on complex queries. By unifying data ingestion, entity resolution, and graph construction into a single pipeline, and enriching it with real-time intelligence, Elemental gives AI agents the contextual awareness needed to form fast, high-confidence conclusions in rapidly changing conditions. Elemental creates secure, enterprise-specific context engines that transform fragmented enterprise data at scale into data structures that agents can navigate and query within milliseconds with verifiable citations – delivering deep-research insights at the speed and cost profile of a simple query.

Enterprises using Elemental also have access to the ground-breaking Lovelace YottaGraph, a proprietary context engine scaling to trillions of global data points, enabling real-time conclusions about the state of the world at any given moment. The YottaGraph augments an enterprise’s context engine to deliver unmatched real world insight, knowledge, and decision-making support, tailored to each client’s needs.

Thank you for being part of this moment. We're just getting started.

https://t.co/Fx6g0eQ7CR

[Lovelace @LovelaceWorks]

Wondering why enterprise AI has been moving so fast but improvements have been so incremental? We’ve been building the missing piece. Can’t wait to share. End of April.