Evidence-Based Answers
Every claim is backed by primary citations: papers, patents, datasets, and standards.
Beta Testing
Scientific Intelligence, Built for Discovery.
MadScientist is building a domain-specific AI conversational engine for deep-tech startups to access accurate, contextual, and domain-specific knowledge while automating R&D workflows for complex queries.
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Use cases
Where evidence wins
Decision support for domains where mistakes are expensive.
Space Tech
Propulsion, materials, thermal constraints, and reliability evidence.
Robotics
Perception, control, sensor fusion, and real-world failure modes.
Defense Tech
Detection methods, signal processing trade-offs, and constraints.
Semiconductor Research
Devices, processes, packaging, and benchmarked comparisons.
Climate Engineering
Evidence synthesis across studies, datasets, and uncertainty.
Energy
Targets, assays, comparative methods, and primary literature trails.
How it works
From question to evidence to answer
Three steps, minimal ceremony.
01
Ask complex technical questions
Write queries the way you think: exploratory, comparative, and deeply specific.
02
Retrieve scientific evidence
Pull primary sources and surface what matters: methods, assumptions, and results.
03
Generate contextual answers
Evidence-grounded summaries with citations and confidence calibrated to the sources.
Features
Evidence-first answers for technical decisions
Calm, precise, research-native. Designed to help you validate claims, compare methods, and trace results back to primary sources.
Research Paper Retrieval
Find relevant literature quickly, with methods, results, and limitations in context.
Multi-Source Verification
Triangulates answers across multiple sources to reduce single-paper risk.
Domain-Specific Intelligence
Understands deep-tech constraints: benchmarks, trade-offs, and failure modes.
Research Workspace
Organize queries, evidence, and citations into a clean trail you can share.
Citation-Aware AI
Generates answers with inline citations and explicit uncertainty when evidence is thin.
Testimonials
Research credibility, not marketing
Feedback from builders and reviewers on user interviews.
"MadScientist makes it easy to trace answers back to specific papers. That changes how we evaluate technical claims."
"The confidence signals are calm and honest. We know when we need deeper validation instead of over-trusting a summary."
"This feels like a diligence tool for deep-tech: clean evidence trails, clear uncertainty, and strong context."
Got any questions? Let's discuss.
FAQ
Frequently asked questions
Short answers. Clear evidence policy.
What is MadScientist?
MadScientist is an evidence-based AI search engine for deep-tech startups to access accurate, contextual, and domain-specific answers to complex queries with research papers as the primary source of evidence.
Who is MadScientist built for?
MadScientist is built for deep-tech startups and R&D organizations where research is a first priority across domains like AI, robotics, space tech, semiconductors, and quantum computing.
Why should enterprises adopt a vertical AI search platform instead of relying only on general-purpose AI?
In deep-tech environments, broad AI assistance is often not enough. Enterprises need systems that prioritize evidence quality, technical relevance, and traceability, especially for research, product, and engineering decisions.
How does MadScientist accelerate internal R&D workflows?
MadScientist accelerates internal R&D by reducing time spent searching fragmented technical information and returning evidence-backed answers from high-signal sources in one workflow.
How is MadScientist different from general AI search tools?
General AI tools optimize for common queries and conversational helpfulness. MadScientist is built for domain-specific retrieval and evidence-grounded synthesis, prioritizing primary sources over generic web summaries.
Is MadScientist available for early access or pilot usage?
MadScientist is currently doing pilots with selected early-stage deep-tech startups for feedback, model improvements, and deeper domain accuracy.
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