Samaya
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About
Samaya AI is an advanced artificial intelligence platform specifically engineered for the high-stakes environment of global financial markets. It functions as a suite of Expert AI Agents designed to help investment professionals navigate the overwhelming volume of market data. Rather than relying on generic large language models, the platform uses a proprietary tech stack composed of multiple specialized models that retrieve, contextualize, and reason in tandem. This architecture allows the tool to process qualitative and quantitative data—ranging from SEC filings and broker research to social media—into actionable investment conviction with high precision and financial nuance. The platform operates through several core capabilities: automated multi-step workflows, instant Q&A, and real-time data monitoring. Users can deploy agents to continually monitor specific market segments or theses, providing alerts when relevant data points surface. The tool is highly instructable, allowing analysts to upload custom templates and set specific output formats for research notes, earnings call summaries, or financial models. It integrates internal proprietary notes with external market data, creating a unified information environment that maintains strict security through sandboxed virtual machines and granular user access controls. Samaya is built for institutional use across the entire financial services spectrum, including sell-side research, hedge funds, mutual funds, investment banking, private equity, and wealth management. It addresses the common zoom in versus zoom out dilemma, enabling users to maintain a broad view of market themes while simultaneously surfacing the needle in a haystack details necessary for specific trades. The tool is particularly valuable for roles that require rapid synthesis of earnings reports, tracking data points across coverage areas, or creating custom primers for new industries. What distinguishes Samaya from standard AI tools is its verified accuracy and specialized financial training. Independent benchmarks cited by the company show a 96% accuracy rate, significantly higher than the 57% typically found in general-purpose GPT-based systems. It emphasizes a lack of hallucinations and rigorous sourcing, ensuring every insight is attributed to a specific data point. Backed by industry heavyweights like NVIDIA and NEA, and led by a team from top AI labs like Google DeepMind and Meta, the platform is designed to scale within large organizations, already serving thousands of seats at bulge-bracket banks.
Pros & Cons
Achieves a verified 96% accuracy rate on financial tasks, significantly outperforming standard GPT-based systems.
Provides rigorous sourcing and citations for all generated insights to prevent hallucinations.
Enterprise-grade security includes sandboxed virtual machines and granular user access controls.
Supports custom user templates to ensure outputs match specific internal reporting formats.
Proven at scale with 10,000+ seats deployed at major institutions like Morgan Stanley.
Lack of transparent public pricing requires potential users to contact the team for a demo.
Highly specialized focus on finance makes it less suitable for general-purpose business administration.
Complexity of multi-step agents may require initial training for analysts to master.
Use Cases
Hedge fund analysts can automate the tracking of specific thesis evaluators across thousands of filings to find unique alpha.
Sell-side research teams can generate formatted notes immediately after earnings calls, significantly reducing manual drafting time.
Investment bankers can rapidly create comprehensive company or industry primers for deal initiations using external and internal data.
Wealth managers can monitor broad market trends and specific news alerts across multiple sources to provide more timely advice.
Platform
Features
• soc 2 security compliance
• custom template support
• real-time market monitoring
• sandboxed execution environments
• internal data unification
• instant cited q&a
• multi-step workflow automation
• expert ai agents
FAQs
How accurate is Samaya compared to general AI tools?
Independent benchmarks confirm that Samaya delivers 96% accuracy for financial tasks, whereas leading GPT-based systems typically score around 57%. This high performance is due to its proprietary system of specialized models that retrieve and reason in tandem.
Can Samaya be customized for my firm's specific reporting style?
Yes, the platform allows users to upload their own templates and set specific formats. Analysts can co-create outlines with the AI before the agents autonomously execute the work.
What security measures does Samaya use to protect data?
Samaya is SOC 2 compliant and utilizes sandboxed virtual machines to isolate execution environments. It also features granular user-level access controls and a complete audit log for every action taken within the platform.
What types of financial institutions use this platform?
The platform is used by bulge-bracket banks like Morgan Stanley, as well as top hedge funds, mutual funds, and private equity firms. It is designed to scale across research, sales, trading, and banking divisions.
Does Samaya provide source citations for its answers?
Yes, every answer generated by the platform is carefully sourced and attributed. This design helps analysts verify information quickly and ensures the platform does not produce hallucinations.
Pricing Plans
Enterprise
Unknown Price• Expert AI Agents
• Multi-step workflow automation
• Instant cited Q&A
• Real-time market monitoring
• Internal data integration
• SOC 2 security compliance
• Sandboxed execution environments
• Custom output templates
Job Opportunities
Applied Scientist (ML)
Transform real-time financial data into investment conviction with expert AI agents designed for hedge funds, banks, and asset managers to generate alpha.
Benefits:
Comprehensive health insurance
401(k) and pre-tax benefits
Unlimited PTO
Travel budget
Office Equipment allowance
Education Requirements:
PhD or Master’s degree in Computer Science, Machine Learning, NLP, or a related field
Experience Requirements:
Strong background in deep learning, large language models, and NLP techniques
Track record of first-author publications in top AI/NLP conferences
Proficiency in Python and deep learning frameworks (PyTorch, Transformers)
Other Requirements:
2+ years of industry applied ML research experience preferred
Familiarity with retrieval-augmented generation and RL
Responsibilities:
Formulate ML problems from product requirements
Analyze existing ML systems and propose novel methodologies
Create and productionize research prototypes
Build novel ML evaluation datasets
Mentor ML interns and publish research findings
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Backend Engineer
Transform real-time financial data into investment conviction with expert AI agents designed for hedge funds, banks, and asset managers to generate alpha.
Benefits:
Comprehensive health insurance
401(k) and pre-tax benefits
Unlimited PTO
Travel budget
Office Equipment allowance
Education Requirements:
Bachelors in Computer Science, Engineering or related qualification
Experience Requirements:
5+ years experience in tech industry
Experience in backend or distributed systems
Experience in data/indexing/search systems at scale
Expertise in building for scale and performance
Other Requirements:
Proactive and eager to take ownership
Fast iteration based on user engagement
Excited about mentoring
Responsibilities:
Develop and own core system components
Design document ingestion systems at scale
Provide engineering best practices
Build extensible systems for business problems
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Machine Learning Engineer
Transform real-time financial data into investment conviction with expert AI agents designed for hedge funds, banks, and asset managers to generate alpha.
Benefits:
Comprehensive health insurance
401(k) and pre-tax benefits
Unlimited PTO
Travel budget
Office Equipment allowance
Education Requirements:
Bachelors in Computer Science, Engineering or related qualification
Experience Requirements:
3+ years experience in industry applied ML
Experience in LLM model training, inference and eval
Experience in serving ML models to a significant number of users
Other Requirements:
Proactive and eager to take ownership
Fast iteration based on user engagement
Responsibilities:
Design scalable ML inference/training systems
Automate ML pipelines at scale
Productionize research prototypes
Develop evaluations/models to improve core systems
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Ratings & Reviews
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