Boosted.ai
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About
Boosted.ai is an AI-driven investment platform designed to assist financial professionals in cutting through market noise. It functions as an intelligent engine that supports idea generation and portfolio management by processing vast amounts of financial data. The platform aims to make sophisticated, adaptive AI accessible to various stakeholders, including institutional investors, wealth management firms, and individual advisors. By integrating AI into the core research process, it helps users improve the quality and speed of their decision-making through high-fidelity data analysis. The tool provides a suite of capabilities including a specialized financial chat interface and agentic AI systems. These investment agents are designed to be intelligent and connected, simulating or supporting a human-centered research process. Users can interact with the AI to get specific answers about market trends or individual stocks, which the company claims are more accurate and trustworthy than general-purpose LLMs like ChatGPT. Additionally, an API is available for firms looking to integrate Boosted.ai’s intelligence directly into their own existing investing or wealth management platforms. The platform is primarily built for the professional financial sector, specifically targeting institutional investors, wealth platforms, and professional money managers. It is currently utilized by major institutions such as BMO, HSBC, and Raymond James. Because it focuses on high-stakes financial data and professional workflows, it is best suited for those who require rigorous, verifiable insights rather than general market sentiment. It serves roles ranging from portfolio managers needing to justify trades to software developers building bespoke financial applications via the provided API. What sets Boosted.ai apart is its finance-first approach to artificial intelligence. Unlike horizontal AI models, this platform is specifically tuned for the nuances of the global financial markets, with institutional investors ranking its accuracy and relevance higher than popular alternatives like Perplexity or Claude. The shift towards agentic investing—where the AI acts as a proactive assistant rather than just a passive search tool—positions it as a specialized solution for complex research tasks that generic AI often struggles to handle with precision.
Pros & Cons
Ranked more accurate and trustworthy for financial queries than general-purpose AI like ChatGPT and Claude.
Used and trusted by top-tier global institutions including BMO, RBC, and HSBC.
Offers a dedicated API for seamless integration into proprietary wealth management software.
Utilizes a specialized agentic AI approach specifically tailored for institutional research workflows.
Institutional pricing is not transparent and requires booking a custom consultation.
The platform's professional focus may present a steep learning curve for casual retail investors.
Full access to advanced investment agents may require custom account setup for larger firms.
Use Cases
Portfolio managers can deploy investment agents to cut through market noise and identify high-conviction trade ideas more efficiently.
Wealth management platforms can use the API to provide their clients with personalized, AI-driven financial insights and interactive chat support.
Financial analysts can leverage the specialized chat interface to obtain more accurate and relevant data compared to general LLM outputs.
Platform
Features
• api integration
• idea generation
• institutional data processing
• adaptive ai modeling
• market noise reduction
• workflow acceleration
• investment agents
• financial chat
FAQs
How does Boosted.ai compare to ChatGPT for financial tasks?
In comparative testing, 8 out of 10 professional respondents preferred Boosted.ai over general models like ChatGPT. Investors specifically ranked its answers as more accurate, relevant, and trustworthy for market research.
Can firms integrate Boosted.ai into their own applications?
Yes, Boosted.ai provides a specialized API designed for investing and wealth management platforms. This allows firms to incorporate intelligent agentic workflows and financial chat directly into their existing software ecosystems.
What is meant by agentic investing on this platform?
Agentic investing refers to the use of intelligent, connected AI agents that proactively assist in the research and decision-making process. These agents are designed to be human-centered, helping advisors and analysts automate complex data analysis.
Who are the primary users of the platform?
The platform is built for institutional investors, professional wealth managers, and large financial platforms. It is trusted by major global firms including HSBC, BMO, and Wells Fargo Advisors.
Pricing Plans
Institutional
Unknown Price• Investment agents
• API access
• Institutional-grade accuracy
• Dedicated support
• Custom workflows
Free Account
Free Plan• Financial chat access
• Basic idea generation
• Account creation
• Web-based platform
Job Opportunities
Senior Software Engineer
Optimize investment research and accelerate workflows with an institutional-grade AI engine featuring financial chat and agents for more accurate market insights.
Benefits:
Flexible (Unlimited) Paid Time Off
Medical, Dental, and Vision benefits
Life Insurance and Disability Benefits
Access to a 401(k) retirement savings plan
Paid Parental Leave
Education Requirements:
Bachelor's, Master's, or PhD in Computer Science, Software Engineering, or a related field
Experience Requirements:
3+ years of professional programming experience
Strong focus on Python
Experience building and optimizing distributed, scalable systems
Proficiency in working with large-scale datasets
Experience with machine learning, data analytics, or financial services (bonus)
Other Requirements:
Legally authorized to work in Canada or the United States
Solid understanding of data structures, algorithms, and their applications
Strong problem-solving skills
Excellent communication skills
Responsibilities:
Take ownership of the full software development lifecycle
Collaborate with cross-functional teams (product managers, data scientists)
Partner with clients and internal stakeholders to design solutions
Work with large datasets for efficient processing and analysis
Contribute to architectural decisions and mentor junior team members
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Ratings & Reviews
No ratings available yet. Be the first to rate this tool!
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