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Pre-emptive AI
Domain-specific agents, built into your business

We Build
AI Products

Built by AI experts from top labs and companies, including Cambridge's Machine Intelligence Lab, Meta, AWS and Microsoft.
Blockchain.ai: Market intelligence on live data, also inside Hero.
Skuto: Video ads from a product link or a few photos.
AI.TV: Video generation for the YouTube of AI.
MedPrepPartner: Voice AI interviews and admissions advice for UK medicine.
PrepPartner: A live AI case interviewer for consulting candidates.
Anglai: An AI speaking examiner and writing marker for IELTS.
Market intelligence on live data, also inside Hero.See how it works
Some companies our team has worked with
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Read our Story
AI agents built for your domain, that work through your data before anyone asks and bring your people the answer
Connect to live data feeds, large databases and large volumes of documents
Get answers through conversation, voice or an interface built for the job
Every figure checked against its source, with a person approving what matters
Learn More About Our Approach
Pre-emptive agents are the next step for AIPre-emptive agents are the next step for AI
AI that can do more than LLMs
Pre-emptive AI agents work through complex business processes ahead of time, across every kind of data your business runs on
Large Volumes of TextCharts & DiagramsLive Data FeedsLarge Structured DatabasesImages, Audio & Video
Most chat assistants can only handle part of this
Tailored AI Solutions
Agents that derive deep insights.
Unlock the full value of your data through advanced analytics, rather than re-compiling existing knowledge
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Insights resurfaced by chat assistants
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Deep insights that remain to be derived from data
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Capabilities
AI that can do complex tasks, accurately.
We use agents to carry out complex multi-step workflows, so you can use AI for much more than text processing and coding
Large Language Models are good at
i.e. writing emails, summarising documents, correcting grammar
Coding Workflows
Live Data WorkflowsDatabase queriesWeb searchCalculators, code and specialist agents
Live Data Workflows
Database queriesWeb search
Calculators, code and specialist agentsLarge Language Models are good at
i.e. writing emails, summarising documents, correcting grammar
Coding Workflows
To build AI that works in production, we take a different approach.
General-purpose AI assistants try to cater to a broad range of use cases
We build agents tailored to your domain and your workflows. That way we unlock a much deeper set of AI capabilities.
How We Work
From first conversation to production
1
Scope the workflow, the data and what success looks like.
2–3 weeks
2
Build a working prototype on your own data.
4–6 weeks
3
Take it to production inside your systems.
3–6 months
4
Run it, measure it and keep improving it.
Ongoing
Typical timings. They depend on the scale of each project.
Why General-Purpose AI Falls Short
See the difference for yourself
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Routes questions to domain-specific expert agents built on top of LLMs.
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Works ahead of time, watching your data and preparing the analysis before anyone asks.
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Uses your own data and systems, with every figure traced back to its source.
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Reasons through workflows and logic built for your sector.
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Analyses, compares and acts, with a person approving what matters.
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Keeps improving from feedback in your domain.
Others
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Responds with a general model trained across broad datasets.
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Waits for a question and starts from scratch every time.
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Limited to public data or what you paste into the prompt.
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Struggles with deep domain logic or workflows outside standard prompts.
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Focuses on writing a response rather than doing the analysis.
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Feedback is spread across unrelated use cases, so it improves slowly.
Research
Reliability is a research problem. We publish on it.
Our team has published more than 50 papers at top machine learning conferences, with over 5,000 citations. Much of it is about the problems that keep AI out of production.
EMNLP 2023 · 1,400+ citations
SelfCheckGPT: Zero-Resource Black-Box Hallucination Detection for Generative Large Language Models
Detects hallucinations by checking a model's answers against each other, with no access to its internals.
EMNLP 2024
Is LLM-as-a-Judge Robust? Investigating Universal Adversarial Attacks on Zero-shot LLM Assessment
Shows how a short phrase can fool AI graders, and why the evaluators need testing too.
arXiv 2026
Hidden in Memory: Sleeper Memory Poisoning in LLM Agents
How poisoned memories can lie dormant inside an agent until something triggers them.
EMNLP 2024
Muting Whisper: A Universal Acoustic Adversarial Attack on Speech Foundation Models
A short audio clip that silences a widely used speech recognition model.
See Our Research
About Us
Who are we?
Founded by AI researchers from the University of Cambridge. Five of our team come from the Machine Intelligence Laboratory, and together we have published more than 50 papers at top machine learning conferences, with over 5,000 citations. Our team members have worked at leading institutions including Meta, Amazon Web Services and McKinsey. We bring that research into the products we build for our clients.
Discuss Your ProjectMeet the Team
Some companies our team has worked with
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