Samaya AI

An AI-powered Knowledge Discovery Platform

[Samaya AI @samaya_AI]

👉 https://t.co/optyyOqMcX.

[Samaya AI @samaya_AI]

Samaya now builds datasets from any public web page.

The best alt data often sits on obscure public pages (TSA counts, EIA refinery runs, trade association PDFs), and analysts rebuild it by hand every week.

Paste a URL to a web table, CSV, Excel file or PDF. Samaya turns it into a dataset that can be queried and cited, then refreshed in one click when new data drops.

Build your first dataset today.

[Samaya AI @samaya_AI]

Try social search on Samaya: https://t.co/wyNuLX2Alo.

Select SOCIAL from the dropdown in the Sources menu, or ask for Social sources in your MCP query.

[Samaya AI @samaya_AI]

Samaya now searches X and Reddit.

The earliest reactions to a theme in the market often show up on X and Reddit, long before appearing in official company communications. Social media captures reactions to events like the midterm elections, or the first signs of disruption in a supply chain. Watching only press releases and 8-Ks means missing a whole layer of commentary and perspectives on how events are unfolding.

Ask about:

- Social posts to understand sentiment from key public figures: "What has Abdul El-Sayed said about data centers on X?"

- How people are thinking about market reactions: “What does commentary on social suggest for oil prices post US midterm elections?”

- Channel checks for on the ground sentiment on a product or a catalyst: “What are people saying on X and Reddit about the iPhone Duo vs other folding phones?”

Samaya lets researchers capture the pulse on socials as part of the complete picture, adding real-time perspectives and commentary to financial research.

Test it today ↓

[Samaya AI @samaya_AI]

Read Microsoft's announcement: https://t.co/nEWqTj9B5d

Check out Samaya in Microsoft Marketplace: https://t.co/pJaczESwwM

[Samaya AI @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 @samaya_AI]

👉 https://t.co/TwAfwp2N4q

[Samaya AI @samaya_AI]

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 ↓

[Samaya AI @samaya_AI]

Our full research covers the four-stage training process, the role of our financial research harness, and early results with larger models.

https://t.co/cGybKXErNx

[Samaya AI @samaya_AI]

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 ↓

[Samaya AI @samaya_AI]

Explore the research: https://t.co/s1Yac9doQo.

[Samaya AI @samaya_AI]

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 ↓

[Samaya AI @samaya_AI]

👉 https://t.co/EXTr0bwoQo.

[Samaya AI @samaya_AI]

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.

[NEA @NEA]

📺 Spotted on @theinformation TV: @samaya_AI founder & CEO @maithra_raghu was featured to discuss her company’s AI agents for finance that aim to transform global financial market information into investment conviction

👉🏽 Check it out at the 28:55 mark. https://t.co/MuIpNGulpa

[Samaya AI @samaya_AI]

👉 https://t.co/Ue8kBQIlyR

[Samaya AI @samaya_AI]

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 ↓

[Samaya AI @samaya_AI]

👉 https://t.co/CfomzKftPi

[Samaya AI @samaya_AI]

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.