Samaya AI

Samaya AI is now listed in @Microsoft Marketplace. We're bringing AI-powered investment research to Microsoft's expanding Copilot ecosystem for finance. Explore Samaya ↓

Samaya AI's FrontierFinance benchmark is joining @FireworksAI_HQ's Specialized Intelligence Index (SII). Built with financial domain experts, FrontierFinance evaluates AI across 220 queries, six investment research use cases, and 11,543 grading criteria. Explore the results ↓

Introducing Samaya Sterling: our first post-trained model for state-of-the-art financial research. On FrontierFinance, Sterling outperforms Claude Fable 5.1 (58.9% vs. 55.9%) at 11× lower cost. Using a model with just 3B active parameters, Sterling demonstrates what's possible when specialized training meets a purpose-built financial research harness. Read the full research ↓

At @aiDotEngineer NYC, Samaya's @AshwinParan built on our earnings prediction research, exploring how AI models reason through predictions and how we might combine the best aspects of human and AI reasoning in the future. Watch below ↓

When a model says it's 70% sure, is it right 70% of the time? We compared Jev with two alternative decision models. Jev worked out of the box, and when it was unsure, pointed to the queries worth double-checking. 🔗 Full write-up in comments.

To train better earnings forecasters, we need events whose outcomes the model hasn’t already learned. But how far back can we reliably evaluate AI earnings predictions without prior knowledge affecting the results? When prompted directly, Nemotron Ultra recalled exact EPS for just 2 of 272 historical events. Yet its answer probabilities showed a different pattern: the oldest earnings answers received ~75x the probability of the most recent ones. Recall can miss prior-knowledge signals. Measuring them could help us choose more usable post-training data. Read the research ↓

New in Samaya: Memory. Tell Samaya you want margin moves in basis points and comparisons against your five names. Three weeks later, ask about Q3 earnings. Samaya remembers your preferences and applies them automatically. Your research assistant learns how you work.

We gave AI agents the same earnings prediction task as expert analysts. Using Samaya’s finance-specific prediction harness, the latest frontier models (Fable 5.1, Opus 5.5, GPT-6 Astra) outperformed a harder, bias-corrected consensus baseline. The models gathered new evidence, calculated the financial impact of events, and adjusted their predictions away from consensus. This is a critical inflection point for AI in finance. Full research ↓

AI research agents often fail on the details, not the reasoning. We trained an advisor model using Hindsight Structured Advice Distillation (HSAD) to learn from our research agent’s past failures and guide it with a question-specific plan. The result: better research quality without spending more compute. On FrontierFinance, our public benchmark: 🔹 56.6% quality, #1 on the leaderboard 🔹 Beats our previous best at 37% lower cost 🔹 Outperforms the best frontier model (Claude Fable 5.1, 55.9%) More on how it works ↓