Alphasense : Real-Time AI Engine for Market Intelligence & Research Automation

Fintech

Alphasense – AI for Market Intelligence and Research Automation

Alphasense integrated our AI models to enhance their financial research engine with real-time insights and document analysis. The platform scans and extracts data from thousands of earnings calls, analyst reports, and SEC filings using NLP and entity recognition. Our AI framework automatically tags, indexes, and summarizes content for instant decision-making. Investors and analysts now get alerts on emerging risks, trends, and competitive movements with just a few clicks. This transformation helped reduce research hours and delivered deeper, more actionable market intelligence. It’s AI-driven research at the speed of thought.

Project Overview

Client: Alphasense (Market intelligence platform trusted by hedge funds, Fortune 500 firms)
Challenge: Time-intensive research cycles + fragmented access to key financial insights
Goal: Deploy AI-powered automation to:
- Process and uncover key findings from voluminous financial files
- Summarize and tag earnings reports, filings, and analyst commentary
- Alert users to emerging risks, trends, or strategic changes
Team: 9 (3 NLP Engineers, 2 Data Pipeline Experts, 2 Frontend Devs, 2 QA Analysts)
Timeline: 7 months (R&D → Pilot with research teams → Global rollout)

“GenX helped us turn noise into knowledge. Our analysts now focus on strategy, not scanning.”

Director of Product, Alphasense

The Challenge

Critical Pain Points:

Research analysts spent hours scanning calls and PDFs for material updates
Important financial signals were often buried in large volumes of unstructured data
Competitive intelligence and risk alerts were reactive instead of real-time

Technical Hurdles:

Accurately parsing noisy data sources (e.g., scanned PDFs, diverse audio transcripts)
Building context-aware summarization that preserved sentiment and nuance
Aligning AI output with regulatory and internal compliance standards

Tech Stack

Component	Technologies
NLP & Summarization	BERT, Pegasus, T5, spaCy, HuggingFace Transformers
Data Processing & Storage	Apache Airflow, AWS Glue, S3, DynamoDB
Search & Indexing	ElasticSearch, Faiss, PostgreSQL
Frontend & Notification Layer	React, GraphQL, Firebase, Slack & Teams APIs
Monitoring & Compliance	Sentry, AWS CloudTrail, Vanta

Key Innovations

The AI scanned earnings calls, filings, and analyst reports, summarizing insights for fast decisions. Real-time tagging and alerts highlighted emerging risks and trends. Analysts saved hours while gaining deeper, searchable intelligence.

AI-Powered Document Intelligence

Scanned and parsed 10K+ financial docs daily with semantic indexing

Result: 62% reduction in time spent on manual research

Context-Aware Summaries

Highlighted only what mattered—guidance changes, revenue trends, red flags

Result: 41% increase in analyst content engagement

Live Trend & Risk Alerts

Analysts received proactive notifications on key events, competitive shifts

Result: 28% improvement in research accuracy and speed

Our AI/ML Architecture

Core Models

Document Parser & Classifier:
- BERT-based entity tagging for financial terms, orgs, and macro indicators
- Smart indexing by sector, sentiment, and event type (M&A, guidance, etc.)
Summarization Engine:
- Extractive + abstractive summarization hybrid using Pegasus & T5 models
- Highlights material changes, forward-looking statements, and anomalies
Insight Alert System:
- Live topic modeling (LDA + embeddings) for emerging trend detection
- Personalized user alerts based on watchlists and portfolios