Nikita Polyakov
Former litigator with a decade of courtroom experience turned backend engineer and LegalTech architect. After 10 years of navigating the unpredictability of the court system, I transitioned into software development to build systems that replace legal intuition with computable data. A pragmatic developer (PHP for business logic, Go for high-load parsing, Python for LLM tooling and experimentation) who builds tools to solve the exact problems I faced in practice. Currently developing AidaLex - a predictive analytics engine that evaluates the success probability of appellate court decisions. Recently published research exposing LLM "sycophancy" in legal analysis, proving that AI models artificially inflate a lawyer's chances of winning when fed biased arguments, and designed a multi-model consensus architecture to neutralize it.
2000+
users
2 Projects
B2C AI Litigation & Appeal Risk Predictor
A production B2C AI service [https://neshemyaka.ru] that predicts litigation and appellate risk for individual users and small businesses. It ingests raw case text, classifies case types, and returns a 0–100 risk score with a confidence score, along with structured risk factors and practical recommendations. Built on a Go backend with PostgreSQL and a multi-model LLM layer with fallbacks, the system is optimized for low COGS, robust JSON error handling, and fast UX (magic-link auth, HTMX frontend), making advanced legal risk analytics accessible without any enterprise integration.
Lexometrica (R&D Phase) — Predictive Justice System
An experimental B2B predictive justice system currently in the R&D phase. Designed to eliminate uncertainty in commercial arbitration, Lexometrica functions as high-load legal infrastructure. The core architecture (Go/Python/PostgreSQL) utilizes a multi-LLM consensus pipeline to parse complex judicial patterns, evaluate litigation risk at scale, and build dynamic AI profiles of individual judges based on their past decisions and behavioral tendencies. The goal is to shift legal strategy from subjective human intuition to a statistically calibrated, API-first risk assessment model.
Featured In
- Habr — Should You Appeal a Court Decision? AI Titans Battle in 2026
Article (in Russian) where I benchmarked Claude, GPT‑5.2 and Gemini on predicting appellate outcomes using real arbitration cases, measured Brier scores and calibration, and demonstrated how LLM sycophancy can dangerously inflate a lawyer’s perceived chances of winning.