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Case Studies — Representative Builds
Equity Valuation Engine
Adaptive Learning Platform with LLM Tutoring
Adaptive Learning Platform with LLM Tutoring
Challenge: Analysts manually rebuild DCF, WACC, and relative valuations across fragmented data — slow, error-prone, and quickly out of date.
Approach: A comprehensive application (React 18 / FastAPI / Postgres in production) utilising sector-beta and country-risk datasets, with a 300 ms input debounce to ensure WACC and value-per-share update interactively as assumptions change. Result: Reliable, source-traceable valuations — every input bounded, every formula linked to published source. Single-DCF runs complete well within the 2-second target specified.
Adaptive Learning Platform with LLM Tutoring
Adaptive Learning Platform with LLM Tutoring
Adaptive Learning Platform with LLM Tutoring
Challenge: Most study tools serve the same content to every learner and can't grade the open-response work that exam boards actually reward.
Approach: A Next.js 14 / TypeScript / Supabase platform combining adaptive MCQ practice on a difficulty-weighted SmartScore mastery metric, a streaming AI tutor with skill-scoped persistent sessions,, AI-seeded skill-prerequisite graphs driving a personalised study path, and KaTeX-rendered structured lessons with comprehension-gated progression and post-lesson effectiveness measurement.
Result: A virtual tutor that diagnoses what each learner should study next, teaches it in paced sections, marks their work against examiner standards, and measures whether mastery actually improved.
Uncertainty-Aware Clinical Scoring
Uncertainty-Aware Clinical Scoring
Uncertainty-Aware Clinical Scoring
Challenge: In clinical settings, acting on an AI score is risky if the model cannot express its confidence — over‑confident errors and silent failures undermine trust and safety.
Approach: A Swin Transformer backbone with patient‑level set aggregation predicts the Mayo Endoscopic Score (ulcerative colitis) on the dataset. Two complementary uncertainty estimators — evidential deep learning (Dirichlet/epistemic) and a heteroscedastic head (aleatoric) — are fused into a single confidence score. Confidence is mapped to a data‑derived, three‑tier triage policy (autonomous / review / refer), so low‑confidence cases are flagged for clinician review.
More Builds On Request
Uncertainty-Aware Clinical Scoring
Uncertainty-Aware Clinical Scoring
These are representative builds that show demonstrated capability. We can share further examples and discuss how a similar approach would apply to your use case — book a consultation to start the conversation.