Model ML & Perplexity Team Up!
Read our blog with Perplexity here:
https://sonar.perplexity.ai/case-studies/model-ml
In financial services, timely and reliable information isn’t just valuable—it’s fundamental to success. To help clients access the most up-to-date and trustworthy data, Model ML has integrated Perplexity’s Sonar API, bringing real-time, cited web intelligence directly into analysts’ workflows.
"Model ML enables bespoke analysis and tailored insights, freeing teams to focus on what truly matters: delivering exceptional outcomes for clients."
Saul Nathan, Chairman, Capital Markets, Morgan Stanley
Partnership Origin
Our partnership with Perplexity began nearly two years ago and was built on transparency from day one. During early conversations, Perplexity was clear about their technology’s capabilities and limitations, a refreshing approach in a landscape where many AI vendors overpromise. Both teams agreed that, in finance, factual accuracy, auditability, and trust must come first.
Technical Integration & Auditability
With Perplexity Sonar, Model ML enables users to pull in live web information directly within the platform. But what sets this apart isn’t just the speed—it’s the level of auditability and control. Every piece of information retrieved via Sonar is fully cited, allowing users (or compliance teams) to click through and verify the original source. This creates a transparent audit trail for any externally sourced insight used in decision-making or client deliverables.
Crucially, Sonar’s advanced filtering lets users define exactly which sources to trust. If a firm wants to restrict research inputs to only Bloomberg, Thomson Reuters, or the Financial Times, these preferences can be set at the platform level. This not only reduces information risk but also aligns with compliance and regulatory standards. For Model ML and its clients, the ability to control data provenance was a major factor in choosing Perplexity’s API. In practice, this means analysts can confidently rely on information, knowing it comes only from sources they—and their clients—trust.
Use Cases & Impact
Market Sentiment Analysis: Automatically scan and summarize breaking news and opinion from pre-approved sources, with all data points linked back to the original publication.
Company Profiles & Investment Research: Create dynamic, up-to-date company profiles that combine internal data with filtered, cited external information.
Automated Deliverables: Produce reports, presentations, and models that are always current and fully auditable—every external fact is sourced, and every workflow is transparent.
These features save significant time, increase accuracy, and support regulatory compliance, all while empowering teams to focus on higher-value analysis and client engagement.
Industry Implications & Vision
This collaboration reflects a broader evolution in financial AI: from generic “black box” solutions to configurable, transparent tools designed for professional accountability. The ability to filter by source and maintain a clear audit trail isn’t just a technical detail—it’s now an industry expectation, and partnerships like this are leading the way.
Book a Demo
Interested in seeing how this works in practice? Book a demo with us.
Model ML & Perplexity Team Up!
Read our blog with Perplexity here:
https://sonar.perplexity.ai/case-studies/model-ml
In financial services, timely and reliable information isn’t just valuable—it’s fundamental to success. To help clients access the most up-to-date and trustworthy data, Model ML has integrated Perplexity’s Sonar API, bringing real-time, cited web intelligence directly into analysts’ workflows.
"Model ML enables bespoke analysis and tailored insights, freeing teams to focus on what truly matters: delivering exceptional outcomes for clients."
Saul Nathan, Chairman, Capital Markets, Morgan Stanley
Partnership Origin
Our partnership with Perplexity began nearly two years ago and was built on transparency from day one. During early conversations, Perplexity was clear about their technology’s capabilities and limitations, a refreshing approach in a landscape where many AI vendors overpromise. Both teams agreed that, in finance, factual accuracy, auditability, and trust must come first.
Technical Integration & Auditability
With Perplexity Sonar, Model ML enables users to pull in live web information directly within the platform. But what sets this apart isn’t just the speed—it’s the level of auditability and control. Every piece of information retrieved via Sonar is fully cited, allowing users (or compliance teams) to click through and verify the original source. This creates a transparent audit trail for any externally sourced insight used in decision-making or client deliverables.
Crucially, Sonar’s advanced filtering lets users define exactly which sources to trust. If a firm wants to restrict research inputs to only Bloomberg, Thomson Reuters, or the Financial Times, these preferences can be set at the platform level. This not only reduces information risk but also aligns with compliance and regulatory standards. For Model ML and its clients, the ability to control data provenance was a major factor in choosing Perplexity’s API. In practice, this means analysts can confidently rely on information, knowing it comes only from sources they—and their clients—trust.
Use Cases & Impact
Market Sentiment Analysis: Automatically scan and summarize breaking news and opinion from pre-approved sources, with all data points linked back to the original publication.
Company Profiles & Investment Research: Create dynamic, up-to-date company profiles that combine internal data with filtered, cited external information.
Automated Deliverables: Produce reports, presentations, and models that are always current and fully auditable—every external fact is sourced, and every workflow is transparent.
These features save significant time, increase accuracy, and support regulatory compliance, all while empowering teams to focus on higher-value analysis and client engagement.
Industry Implications & Vision
This collaboration reflects a broader evolution in financial AI: from generic “black box” solutions to configurable, transparent tools designed for professional accountability. The ability to filter by source and maintain a clear audit trail isn’t just a technical detail—it’s now an industry expectation, and partnerships like this are leading the way.
Book a Demo
Interested in seeing how this works in practice? Book a demo with us.
Model ML & Perplexity Team Up!
Read our blog with Perplexity here:
https://sonar.perplexity.ai/case-studies/model-ml
In financial services, timely and reliable information isn’t just valuable—it’s fundamental to success. To help clients access the most up-to-date and trustworthy data, Model ML has integrated Perplexity’s Sonar API, bringing real-time, cited web intelligence directly into analysts’ workflows.
"Model ML enables bespoke analysis and tailored insights, freeing teams to focus on what truly matters: delivering exceptional outcomes for clients."
Saul Nathan, Chairman, Capital Markets, Morgan Stanley
Partnership Origin
Our partnership with Perplexity began nearly two years ago and was built on transparency from day one. During early conversations, Perplexity was clear about their technology’s capabilities and limitations, a refreshing approach in a landscape where many AI vendors overpromise. Both teams agreed that, in finance, factual accuracy, auditability, and trust must come first.
Technical Integration & Auditability
With Perplexity Sonar, Model ML enables users to pull in live web information directly within the platform. But what sets this apart isn’t just the speed—it’s the level of auditability and control. Every piece of information retrieved via Sonar is fully cited, allowing users (or compliance teams) to click through and verify the original source. This creates a transparent audit trail for any externally sourced insight used in decision-making or client deliverables.
Crucially, Sonar’s advanced filtering lets users define exactly which sources to trust. If a firm wants to restrict research inputs to only Bloomberg, Thomson Reuters, or the Financial Times, these preferences can be set at the platform level. This not only reduces information risk but also aligns with compliance and regulatory standards. For Model ML and its clients, the ability to control data provenance was a major factor in choosing Perplexity’s API. In practice, this means analysts can confidently rely on information, knowing it comes only from sources they—and their clients—trust.
Use Cases & Impact
Market Sentiment Analysis: Automatically scan and summarize breaking news and opinion from pre-approved sources, with all data points linked back to the original publication.
Company Profiles & Investment Research: Create dynamic, up-to-date company profiles that combine internal data with filtered, cited external information.
Automated Deliverables: Produce reports, presentations, and models that are always current and fully auditable—every external fact is sourced, and every workflow is transparent.
These features save significant time, increase accuracy, and support regulatory compliance, all while empowering teams to focus on higher-value analysis and client engagement.
Industry Implications & Vision
This collaboration reflects a broader evolution in financial AI: from generic “black box” solutions to configurable, transparent tools designed for professional accountability. The ability to filter by source and maintain a clear audit trail isn’t just a technical detail—it’s now an industry expectation, and partnerships like this are leading the way.
Book a Demo
Interested in seeing how this works in practice? Book a demo with us.
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