AI-Powered Market Intelligence & Forecasting
We partnered with a San Francisco financial services firm to build a next-generation research engine. By fusing LSTM Price Forecasting with Real-Time Sentiment Analysis, we created a platform that delivers institutional-grade insights, accelerating investment research by 5x.
The Impact Dashboard (Metrics)
The "Fragmented Research" Problem
Investment analysts were struggling with a disjointed workflow, toggling between news terminals, social media feeds, and static charting tools. This fragmentation made it impossible to correlate “Market Sentiment” with “Price Action” in real-time. Furthermore, existing forecasting tools were “Black Boxes”—providing predictions without explaining the underlying drivers, leading to low trust among portfolio managers.
Key Bottlenecks
Data Overload
Analysts spent hours manually gathering fundamentals, news, and earnings call transcripts.
Lagging Indicators
Legacy tools couldn't interpret intraday social sentiment shifts (e.g., a viral tweet affecting a stock).
Quant Gap
Fundamental analysts lacked the tools to apply quantitative scoring to unstructured text data.
Trust Issues
AI predictions were ignored because they lacked explainability.
Client Profile
Industry
Region
San Francisco, USA
Focus
Investment Research
Core Tech
LSTM, Prophet, Transformers (BERT), RAG, React
Multi-Signal Intelligence Engine
Inexture.ai engineered a comprehensive Market Intelligence Platform. We combined Predictive Models (LSTM/ARIMA) for price trends with a Sentiment Fusion Engine that ingests news and social signals. A RAG-based Research Copilot sits on top, allowing analysts to ask “Why is this stock moving?” and receive an answer grounded in both data and recent news.
Engineering The Platform
Hybrid Forecasting Engine
Combined LSTM (Long Short-Term Memory) networks for pattern recognition with Prophet for trend decomposition.
Achieved a 35% improvement in forecast accuracy over standard baselines by accounting for seasonality and momentum.
Real-Time Sentiment Fusion
An NLP pipeline that aggregates financial news, Reddit threads, and earnings call transcripts, assigning a "Sentiment Score" (-1 to +1) that correlates with price volatility.
Provides early warnings for market anomalies before they reflect in price.
Research Copilot (RAG)
An interactive chat interface where analysts can query specific companies. The LLM retrieves recent 10-K filings and news to generate an "Investment Thesis Summary" automatically.
Reduced time-to-insight for new stock coverage by 50%.
Automated Report Generation
A pipeline that auto-compiles daily "Morning Briefs"—PDF reports containing price forecasts, sentiment heatmaps, and key risk factors for watched assets.
Eliminated the manual drudgery of morning report creation.
Business Impact
Research Velocity
5x faster insight generation, allowing the team to cover more sectors and assets with the same headcount.
Decision Confidence
40% increase in analyst confidence due to the "Explainable AI" features that detail exactly why a forecast was made (e.g., "Bullish due to positive earnings sentiment").
Risk Management
Early detection of anomalies allowed asset managers to hedge positions hours before significant market corrections.
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