AI for

Financial Services

Bringing the Value of Self-Hosted LLMs to Financial Services


Use Cases for AI in Financial Services

Shakudo is proud to be working with leading Financial Services organziations on many AI use cases, including:

AI-Assisted Investment Research

Streamline the review of earnings transcripts, expert calls, sell-side research, news, and financial filings. Extract key data points, aggregate views, and create time-series insights.

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Seamless Integration with Research Management Systems

Utilize natural language interfaces to review historical notes, summarize views, and post commentary directly within your research management system (RMS).

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Efficient DDQ and RFP Support

Quickly draft responses to due diligence questionnaires (DDQs) and requests for proposals (RFPs) by leveraging prior responses and relevant fund and regulatory documents.

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Enhanced Investor Relations

Prepare for LP meetings by accessing contact biographies, internal communications, and CRM activity. Interact with your CRM using natural language prompts for efficient data management.

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Why industry models and fine-tuning is not the answer

As the landscape of alternative asset management continues to evolve, the intersection of technology and data stands as a cornerstone for innovation and maintaining a competitive edge. Many financial services are looking to external and internal data sources to capture trends, uncover information, and provide insight around operational opportunities. Large language models (LLMs) serve a growing use case around this data, as they provide an amazing ability to summarize unstructured data.

While existing models like BloombergGPT have demonstrated remarkable capabilities, they often fall short in addressing the nuances of organizational-specific use cases for LLMs. Fine-tuning improves model performance, but has its limits due to the specific training the fine-tuning is specialized for. Enter Retrieval Augmented Generation (RAG).

RAG fundamentally improves use case performance by enabling LLMs to access organizational context in real time. Whether it’s industry news, market data, recorded earnings calls, or internal emails, these data sources can provide needed context for the LLM. The Shakudo platform is a catalyst in the realization of RAG-based LLM architecture for financial services .

Why Industry Leaders in Financial Services Choose Shakudo

Seamless Data Source Integration

We streamline the process of connecting to diverse data sources through tools like Airbyte. Our platform ensures that accessing and integrating data is a straightforward process. Whether it is CapIQ, EDGAR, email, Excel, Word, or PDFs, the platform can handle it.

Robust ETL Pipelines

Leveraging tools such as Airflow, Dagster, and Prefect, we establish robust Extract, Transform, Load (ETL) pipelines. These pipelines serve as the backbone for reliable and efficient data processing.

Completely Self-Hosted

Everything on Shakudo is deployed inside a fund’s infrastructure. This means there is no data egress, and every aspect of the LLM can be wrapped with user-level data and privacy controls.

Access to Leading Technology

We provide immediate access to all tooling needed to set up a RAG-based LLM, including data ingestion, pipelines, vector databases, and LLMs.

Shakudo RAG LLM architecture

Leverage LLMs on Shakudo

At Shakudo, our mission is to be more than a platform. We aim to be a strategic partner, empowering technology and data leaders in financial services to navigate the complexities of AI and data with confidence and agility.

Get in touch with the Shakudo team to learn more about how our platform can enable your fund to stand up a RAG-based LLM!

Thank you.

We’ll be in touch shortly. In the meantime, feel free to check out our latest data insights, Shakudo success stories, and resources that help data teams win.
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