AI for Scientific Research: Building Tools for Data Analysis, Literature Review, and Discovery
How AI accelerates scientific workflows — literature synthesis, data analysis, hypothesis generation. From a team whose founder has a PhD in Math/Physics and published astronomy papers.
Where scientific training meets AI engineering
This is personal for me. My primary education is in astronomy — I have an MS in astronomy and a PhD in mathematics and physics. I’ve published papers in Monthly Notices of the Royal Astronomical Society, Icarus, Astronomy and Computing, and Frontiers in Psychology. I developed a Python package for identifying celestial bodies trapped in mean-motion resonances and used machine learning to classify asteroid orbits.
This background isn’t just biographical colour — it directly shapes how we build AI systems. Scientific training teaches hypothesis testing, systematic experimentation, statistical rigour, and healthy skepticism of results that look too good. These habits transfer directly to AI development, where the gap between a demo that impresses and a production system that works reliably is enormous.
AI applications in scientific research
Literature review and synthesis is where AI saves researchers the most time. A systematic review in any scientific field requires reading hundreds or thousands of papers, extracting key findings, identifying methodological patterns, and synthesising the state of knowledge. AI tools can search semantic similarity across paper databases, extract methods and results into structured formats, identify gaps in the literature, and generate initial synthesis drafts — reducing weeks of work to days.
The architecture is the same RAG system we build for legal and financial applications, but with domain-specific adaptations: scientific paper parsers that handle LaTeX, extract equations and figures, and understand citation networks; embeddings fine-tuned on scientific text; and retrieval that understands the distinction between methods, results, and discussion sections.
Data analysis automation addresses another bottleneck. Experimental scientists generate large datasets that require statistical analysis, visualisation, and interpretation. AI can automate routine analysis steps (normalisation, outlier detection, statistical tests), suggest appropriate analysis methods based on data characteristics, generate publication-ready visualisations, and identify patterns that manual analysis might miss.
When we built HumanRace — a running app that needed to track runners’ positions in real time with high accuracy — we developed a sophisticated analysis methodology combining smoothing filters, Kalman filters, and frequency analysis. The challenge was essentially scientific: extracting a clean signal from noisy sensor data (GPS, gyroscope, accelerometer) in environments where the GPS signal was unreliable.
“Strangely enough, you can trace the connection between this problem and my dissertation in astronomy. In my dissertation, I analyse time series and identify librations of certain variables. Due to the presence of multiple perturbations from planets and dwarf planets, the resulting series are not a pure sinusoid but a corrupted one. Both to determine the stability of asteroid orbits and to track running people, you need special techniques — filters, periodograms. The mathematical foundations transfer across domains.”
Why scientific rigour matters in AI development
The AI industry has a reproducibility problem. Demo-driven development produces impressive showcases that fall apart in production. Scientific methodology provides the antidote: define hypotheses before running experiments, use proper evaluation metrics, test on held-out data, report negative results, and document methodology thoroughly enough for others to reproduce.
We apply this to every AI project. Before building a legal research tool, we define evaluation metrics (retrieval precision, answer faithfulness, citation accuracy) and benchmarks. We test systematically, not anecdotally. We document what doesn’t work alongside what does. This rigour is what separates production AI from demos.
Building research tools
If you’re building AI tools for scientific research — a literature search engine, a data analysis platform, a lab notebook assistant — the key considerations are domain specificity (scientific text has unique characteristics: equations, references, methodology descriptions, statistical results), evaluation rigour (researchers will test your tool more critically than almost any other user group), and integration with existing workflows (researchers use specific tools — Jupyter, R, LaTeX, reference managers — and any new tool needs to fit into that ecosystem).
Budget: a scientific literature search tool with semantic retrieval runs $40K–$80K, 6–10 weeks. A data analysis assistant with domain-specific capabilities: $50K–$100K, 8–12 weeks. A comprehensive research platform combining both: $100K–$200K, 4–6 months.
Building AI tools for research or scientific applications? Contact us — we bring both the AI engineering and the scientific methodology.