Signal extraction at the edge of earnings dislocation, shaped by adaptive Agentic AI systems.

AI driven insights

Find SIGNAL in the NOISE

We surface transient mispricings for wealth managers within earnings-driven volatility regimes using agentic, self-improving systems. At each earnings event, distributed AI agents extract orthogonal signals spanning implied moves, historical reaction functions, sentiment flows, earnings surprise vectors, and peer-relative price action, which are fused by a central orchestration layer into structured trade recommendations with calibrated timing, sizing, and probabilistic confidence. The system continuously adapts through advisor feedback and supports modular data integration via MCP-compliant architecture, enabling persistent evolution of its signal stack.

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Deep Research Reports

Research reports combining AI-generated insights with human expertise

Premium research reports that fuse AI-generated signals with human domain expertise to decode earnings-driven market dynamics.
Our system synthesizes multi-factor inputs, from options-implied expectations to sentiment and peer-relative movements, into actionable insights. Each output is refined through expert validation, ensuring relevance within institutional portfolio construction frameworks. The result is a high-conviction view on mispricing, volatility, and post-earnings price discovery. Designed for wealth managers seeking signal clarity in increasingly noisy and reflexive markets.

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Pre Earnings Strategies

Our AI agents run continuous surveillance across the earnings calendar, ingesting transcript data and sell-side revisions to model sector-level beat rate distributions and update probability-weighted expectations on a rolling basis. Executive commentary is parsed for forward guidance divergence and tone shifts that may front-run consensus re-rating cycles, calibrated against FactSet S&P 500 aggregate data to isolate idiosyncratic signals from macro earnings drift. Other signals like options market implied volatility skew, unusual volume in near-term contracts, and dark pool flows also provide valuable insights. A core setup we surface is pre-announcement drift — where informed flow and momentum exposure drive asymmetric price discovery in the 5–10 day window ahead of the print.

Real time
Strategies

We monitor earnings data across the market comparing reported results, post-earnings price movements, and valuations against sector peers to identify recurring patterns in earnings beats and misses. Our models monitor and analyze earnings call transcripts, Factset data and management commentary for forward-looking signals that inform exit timing and position management. On the analyst side, our agents decode EPS forecasts by examining the underlying models, management guidance, and fundamental drivers that shape market expectations. Rather than stopping at consensus, we go deeper: critically evaluating analyst assumptions and surfacing divergences that the headline estimate obscures, so you're positioned with an edge before the number drops.

Earnings Drift Strategies

AI tracks post-announcement price behavior, cross-referencing realized EPS surprise magnitude against sector-level beat rate distributions to isolate names where consensus re-rating remains incomplete and residual drift is statistically probable. NLP-parsed transcript tone shifts and forward guidance divergence are layered as cross-sectional amplifiers, flagging names where informational asymmetry between institutional and retail flow creates exploitable mispricing windows extending weeks to quarters beyond the print. Recency bias relative to the 52-week high is incorporated as a momentum conditioning factor, systematically identifying cohorts where anchoring effects and delayed sell-side estimate revision cycles perpetuate directional price continuation.