AI Financial Analysis & Insights Platform
Financial analysis platform using AI and LLMs to turn statements, ledgers, and KPIs into actionable insights
Overview
Developed an AI financial analysis platform that helps finance teams analyze statements, ledgers, and operational KPIs. LLMs generate clear narratives and variance explanations while models and rules surface trends, anomalies, and decision-ready summaries for leadership.
Project details
Finance & Analytics Team
7 months
AI Financial Technology
Project Gallery
Key Results
The Challenge
Finance stakeholders spent too long assembling reports from multiple systems. Variance explanations were manual, anomaly detection was late, and non-finance leaders struggled to get plain-language insight from dense spreadsheets.
Our Solution
We built an AI-assisted financial analysis workspace: • Ingestion of financial statements, ledgers, and KPI feeds • Automated ratio, trend, and variance analysis • LLM-generated narratives for board and management packs • Anomaly and outlier detection on key accounts • Scenario and what-if summary support • Exportable insight reports for stakeholders
The Results
Finance teams gained speed and clarity: • Shorter monthly and quarterly review cycles • Plain-language AI narratives on top of real numbers • Earlier visibility into anomalies and drivers • Better alignment between finance and business leaders • Repeatable analysis workflows instead of one-off spreadsheets
Technologies Used
“AI financial narratives and anomaly detection turned our month-end from a scramble into a structured insight review.”
Finance Director
Finance & Analytics Team
More production work
Gardener for vibe-coded repos: architecture contract, no duplicate features, orphan hygiene, and lockfile CVEs—so the next agent extends what already exists
AI marketing analytics with Kai—a supervisor plus specialist mini-agents stack for Google Ads, Meta, search, and cross-channel insights
AI-powered FS quality review: deterministic audit tools plus Ollama LLM judgment, bilingual cross-check, and a feedback-to-adapter learning loop