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In the ever-evolving landscape of financial services, the integration of Artificial Intelligence (AI) and Large Language Models (LLMs) has emerged as a game-changer, especially in the realm of financial data generation such as:

The accuracy in which AI, particularly LLM’s can extract data points from unstructured sources, such as Exchange filings and Disclosures, is reshaping how FinTech’s and Data Vendors approach Data production and Operations, providing a potent catalyst for

Some key operational benefits that the integration of AI and LLM’s can deliver include:

1. Complementing Internal Operational Processes

Financial institutions rely heavily on data vendors for comprehensive and timely information. Integrating AI and LLMs into existing operational processes enhances the capabilities of these vendors. By automating data extraction and analysis, these models help eliminate manual errors, reduce processing times, and ensure a more consistent and reliable flow of information. This strategy can open the door for data vendors to extend the scope and simplify the integration of their data sets without additional large capital investment in data operations.

ESG factors have become integral to investment decision-making, reflecting a growing awareness of sustainability and responsible investing. AI and LLMs contribute significantly by automating the extraction and analysis of ESG-related information from diverse sources, including news articles, social media, and official company disclosures. This process not only improves the accuracy of ESG data but also significantly improves speed-to-market for updates data points within days of a company material ESG disclosures.

One of the primary advantages of leveraging AI and LLMs within data generation operations is the acceleration of speed to market for new product offerings. These models can quickly analyze, extract and maintain datasets, assuming the models can be exposed to the official underlying sources of data. It is worth noting that, in our view, there will always be a need for human involvement in the data generation process, especially for performing delta checks where business rules have been triggered. Overall the combination of AI and human within data operations can deliver the speed-to-market, which is critical in an industry where timely information can make the difference between seizing market opportunities and missing out.

Market trends affirm the growing importance of AI and LLMs in financial services. According to a report by EY in October 2023*, Wealth and Asset Manager senior executives rated ‘Data Ingestion to drive alpha generation’ as the top area in which GenAI could have the greatest impact on their organization. The report emphasizes the positive impact on data accuracy and the ability to uncover previously inaccessible insights, aligning with the themes discussed in this blog.

In conclusion, the integration of AI and Large Language Models highlights how positive an impact GenAI can have on data generation within financial services – detailed thought needs to go into how these GenAI models are deployed, as well as the controls and QC processes that are built around them. By enhancing accuracy, increasing scope, and accelerating speed to market, these technologies could empower services providers to stay ahead in a dynamic and competitive landscape.

As the industry continues to embrace this transformation, the power of GenAI models could prove to be a disruptive force within financial data generation.

Orbit Financial Technology focuses on harnessing AI models to make sense and structure from millions of unstructured data sources – Contact Us to see how our platform is creating positive disruption in data production and operations as well as investment research and ESG due diligence.

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Transforming the Data Landscape

All you need to source, process and transform unstructured data into predictive indicators.Alternative datasets of global regulatory filings, corporate websites, China and digital assets. Bespoke data

The Impact of AI and Large Language Models on ESG and Financial Data Generation

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Introducing our China A transcript app. We have over 5 years of historical data for all China A transcripts.

Use a range of parameters to provide accurate search results:

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Results are then displayed in list or block format:

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Open an individual result to see the full translated transcript with all relevant associated data and a dynamic event detector with stock position graph:

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Contact us and book a demo, if you’d like to know more about this or the wide range of problem-solving applications on offer through the Orbit Vision platform.

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China A Transcript App

All you need to source, process and transform unstructured data into predictive indicators. Alternative datasets of global regulatory filings, corporate websites, China and digital assets. Bespoke data

China A Transcript App – Now available

In the ever-evolving landscape of investment research, the integration of Generative AI (GenAI) and Large Language Models (LLMs) heralds a new era of transformative possibilities. The key areas that require thought and focus across the financial services landscape is to ensure that these models are utilized in the right way, are being fed with tokenized data (not just throwing a PDF to ChatGPT) to ensure accuracy and alleviate hallucinations, as well as having a clearly defined set of tasks and outcomes to be performed by GenAI.

Picture this: it's 2023, and Generative AI has emerged as a game-changer, akin to the transformative impact of smartphones in the past. Companies like NVIDIA, OpenAI, Meta, and Microsoft lead the charge, marking significant milestones in market valuation. The introduction of GenAI into the financial services industry, particularly investment research, represents a paradigm shift towards a data-centric approach and strategic utilization of unstructured data.

The key to unlocking the power of GenAI and LLMs lies in their ability to streamline tasks, leverage unstructured data sources, and enhance the speed of delivering investment opportunities to market. While GenAI isn't a standalone solution, it acts as a facilitator, providing structured information for more efficient analysis. Analysts, once mired in manual data gathering, now have the opportunity to become architects of sophisticated strategies, refining models, and curating data sources for valuable insights.

But before the power of GenAI and LLM’s can be realized, significant thought needs to go into the underlying data that is feeding these models. Before fully harnessing GenAI and LLMs, two critical considerations come into play: the accuracy and reliability of underlying data and defining specific outcomes. The quality of input data is paramount, requiring meticulous attention to ensure reliability and accuracy. Converting unstructured data into a semi-structured, tagged state is essential for enhancing accuracy and extracting valuable insights efficiently.

The integration of LLMs into the investment research landscape has further expanded possibilities, particularly in analyzing unstructured data sources like news sentiment, corporate filings, and regulatory documents. Imagine automating the analysis of vast amounts of textual data in real-time, providing accurate and up-to-the-minute insights into market sentiment. LLMs excel in navigating regulatory documents, extracting pertinent information, and accelerating project timelines, empowering financial professionals with actionable insights.

Personalization is another hallmark of the GenAI and LLMs revolution. Analysts can now configure models to execute multi-stage screening tasks autonomously, tailored to their specific objectives. This level of personalization not only enhances efficiency but also elevates the quality of decision-making, ensuring no potential opportunity goes unnoticed.

Looking ahead, the journey towards fully automated investment research is just beginning. While the industry may currently be around the test phase in research automation, the roadmap laid out by GenAI promises a future marked by innovation, customization, and scalability.

As analysts embrace their roles as architects of sophisticated strategies, the landscape of investment research is set to undergo a significant overhaul, unlocking the full potential of GenAI and LLMs to create a future where financial analysis is accurate, personalized, and efficiently navigated.

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Unlocking the Potential

Unlocking the Potential: How GenAI and LLMs Can Revolutionize Investment Research

Unlocking the potential: how GenAI and LLMs can revolutionise investment research

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Harnessing GenAI

Orbit - Whitepaper 

In the dynamic landscape of sustainable investing, where environmental, social, and governance (ESG) factors are gaining paramount importance, the need for data transparency has never been more critical. As asset managers strive to align their portfolios with ethical and sustainable practices, the demand for reliable, traceable data becomes the bedrock of responsible investment decisions.

One key challenge in the realm of sustainable investing is the diversity and complexity of data points involved—emissions data, water usage, social impact metrics—the list goes on. To navigate this maze successfully, asset managers will need to rely on data vendors to provide a clear audit trail and data lineage. This ensures that every reported data point can be meticulously traced back to its official underlying source. Such transparency is not merely a bureaucratic formality; it is the linchpin for robust due diligence.

Investment Management & Company Interactions

Looking at this from a company perspective, in a 2023 Willis Tower Watson Risk Manager Survey*, 74% of respondents stated that improving their ‘ESG Score’ is a core focus for their business, however, the report indicates that the magnitude of frameworks and ESG data relevance to their industry is making this task ever more complex.

From a stewardship standpoint, transparency is paramount for monitoring how companies are progressing towards their sustainable goals. It's not enough for companies to claim adherence to ESG principles; investors, regulators, and the public demand tangible evidence. Transparent data empowers asset managers to scrutinize the journey of each company in their portfolio, fostering accountability and trust.

Thus, there is a specific need for closer engagement between investment managers and underlying companies, however, this engagement needs to be supported with a scalable, AI supported process to drill into ESG disclosures in order to clearly discuss progress towards defined ESG goals.

For asset managers to truly meet regulations and deliver sustainable products to the market, data must be not only accurate but also cost-effective and consumption-based. This means having the flexibility to procure only the data that is pertinent to their investment strategies. In an era where resource efficiency is synonymous with sustainability, it is crucial that asset managers can access the information they need without unnecessary data license fees for a universe of data they may not actually need.

Consumption-based data models allow asset managers to be discerning in their choices, ensuring that they only pay and extract what they need, in line with their investment universe. This not only aligns with the principles of sustainability but also makes economic sense. It empowers asset managers to tailor their data procurement to the specific needs of their portfolios, avoiding unnecessary costs associated with surplus or irrelevant information.

In conclusion, the journey toward sustainable investing is intrinsically linked to the transparency and reliability of the data that guides it. From emissions data to social impact metrics, every piece of information must be traceable and accountable. With the rise of LLMs, the importance of cost-effective, consumption-based data models cannot be overstated.

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Navigating the Data Maze

In the dynamic landscape of sustainable investing, where environmental, social, and
governance (ESG) factors are gaining paramount importance, the need for data transparency has never been more critical.

Navigating the Data Maze: The Imperative of Transparency within ESG Data

Leverage the insightful power of unstructured data when selecting, monitoring and re-weighting your holdings for active ETFs. Orbit Insight is the go to tool for company filings, news and press releases covering 15+ global exchanges with results updated throughout the day.Attempting to digest the volume of data required to make informed and insightful decisions when selecting the stock holdings for an ETF or any fund for that matter is impossible without an advanced tool of some sort. Orbit Insight covers all public company filings, news and press releases and enables you to monitor for all listed companies ongoing with up to 3 months historical data as standard and up to 5 years on request.The tool allows you to save and manage themes and their associated concepts as well as creating and managing portfolios and setting up alerts to highlight filings, news and press releases for companies that are directly relevant to you.

View these and a raft of additional features in action here:

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Calling all active ETF Issuers

Calling all active ETF Issuers, fund managers and analyst

There is no doubt that financial industry is more and more aware of the value of alternative data. The number of alternative data platforms and data vendors is rapidly increasing. More and more companies are setting up a ‘Chief of Data’ role and data science teams to experience use cases on alternative data. Investment also increases, when for example Nasdaq acquired Quandl to advance the use of alternative data.

Complexities of Converting Alternative Data

There is a rising focus on alternative data, however the industry is not yet fully aware of the nature and opportunities of alternative data, nor appreciate the complexities on processing it.

Alternative data is diversified. By definition, any data apart from static or market data is classed as alternative data. It can be structured or unstructured, in the format of text or speech or image, multi-lingual, and sometimes even in hard copy. Most alternative data vendors specialise on a niche domain, e.g. credit card transactions, property transactions, satellite images, news sentiment etc. It’s difficult for end users to know what the available data is. Luckily the gap is getting filled with rising number of intermediate platforms that match between vendors and users.

Requirements are diversified. Let’s take sentiment as an example, which is not a new concept for financial industry anymore. However it’s still the most well-known way of utilising text data, e.g. news and social media. Purchasing sentiment from a data vendor has its limitations: Sentiment score is calculated in a black box algorithm. Too much information is lost when converting text into a numerical value. Alpha can be diluted if too many users consume the same data. Text data – news, filings, research reports, contains vast information that is proved to be forward looking and predictive. It can be used not only by quant funds, but broadly buy and sell side for stock selection, thematic trading, portfolio management, risk management, investment research, etc. The diversified requirements and applications bring huge challenges to vendors as it is difficult to pre-process everything and sell as off-the-shelf products.

Too much focus on data, with little focus on processing. Alternative data is the source of alpha, but the data itself is not alpha unless being processed properly. For example, there are vendors sourcing and distributing research reports, which is a valuable source of alternative data, especially when under MIFID 2 regulations. With a reasonable price, it’s not a sizeable challenge for a fund to purchase huge volume of reports. What to do with that data is what’s key. However due to lack of understanding of available technologies and long development cycles, most users only extract shallow information or do key word search.

Lack of solution vendor. There are large solution vendors on static and market data who provide off-the-shelf tools and frameworks for data cleansing, storage, and calculation. But it is a different story in the alternative data domain. Funds and investment banks are very protective when it comes to processing alternative data and tend to spend too much resource on building technical capabilities before starting to build business cases. The growth of solution vendors will be a sign of maturity for utilising alternative data, because they focus on building standard processing functionalities. It will be cost effective and accelerate clients’ development cycle. By purchasing a technical solution and support, funds and investment banks can be more focused on business related calculation logics, which is the true business secret to be protected from competitors.

Best practices for utilising alternative data

Business takes ownership. Instead of looking at an alternative data vendor and extracting available market data, the business should define its requirements, potential use cases and then select the appropriate data and solution vendors.

Focus/invest more on processing data. Traditionally, end users rely on data vendors to process alternative data to a level that can be directly used as a signal. They should have the control on how raw data is processed. The more end users are involved in the process, the better they can control what information is to be extracted.

A systematic approach. In many cases, alternative data is big data, which is 3V – high volume, high velocity, high variety. Generating historical data and run an offline POC is acceptable for the first step of validating idea and requirements, however a robust IT system and support is required to support use cases in production.

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Challenges and trends

News Article - Orbit

Challenges and trends of utilising alternative data

Data Production: Simplified, Personalised, and Efficient

When it comes to managing and leveraging data, businesses often face a choice between complexity and cost-efficiency. With Orbit, you no longer have to choose. We’re here to help you increase revenue, improve accuracy, and streamline productivity—all while reducing costs and time spent. Sound impossible? Not anymore.

Orbit isn’t here to replace your existing processes—we’re here to enhance them. Our platform is designed to seamlessly integrate with your workflows, complementing your team’s efforts and amplifying efficiency. From automating routine tasks to offering insights in record time, Orbit helps you get more done with less hassle.

No two businesses are the same, and neither are their data requirements. That’s why Orbit offers customisable solutions that adapt to your unique needs. Whether it’s providing machine-readable exchange filings or delivering bespoke data feeds, we ensure you get the precise information you need, when you need it—all at a fraction of the cost and time you’re used to.

Data can be messy, overwhelming, and downright confusing. At Orbit, we embrace this complexity so you don’t have to. Our platform is built to handle even the most intricate data challenges, doing the heavy lifting for you. This allows you to focus on what matters: using the insights to make informed decisions.

Get instant access to a global database of machine-readable exchange filings, available before the next market open. With PDFs, machine-readable formats, and vector files available through our APIs or as daily managed feeds, Orbit delivers the data you need without delay.

Using the latest in Large Language Models (LLMs), Orbit enables you to generate financial, ESG, and research data tailored to your needs. Whether it’s the format, frequency, or scope, our data production solutions are as flexible as your requirements—paying only for what you use.

Streamline your workflows with the Orbit Bot Marketplace, where automation meets personalisation. Generate company-level data for financial, ESG, and research purposes as often as you need, freeing up your team for higher-value tasks.

Data is more than numbers—it’s a story waiting to be told. Orbit’s advanced analytics tools help you turn raw data into actionable insights. With AI-driven models, including LLMs, you can create reports, forecasts, and visualisations that inform smarter strategies and decisions.

Whether you’re working with structured datasets or unstructured documents, Orbit scales effortlessly to meet your growing needs. Customise your data management processes to ensure they evolve alongside your organisation.

In today’s regulatory landscape, traceability is essential. Orbit’s platform lets you track every data point back to its original source, ensuring full transparency for audits, compliance, and reporting. It’s peace of mind, built into your workflow.

With Orbit, data production is no longer a burden—it’s an advantage. From enhancing efficiency to delivering personalised, scalable solutions, we help you unlock the full potential of your data while saving time and resources.

Ready to transform how you manage and use data? Let Orbit guide the way.

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Data Production Overview.png

Discover Orbit's innovative data production platform. Enhance efficiency, personalise data workflows, and tackle complex data challenges with scalable, cost-effective solutions designed to streamline your operations.

Data Production Simplified: Enhance Efficiency with Orbit’s Customised Solutions

Data Production with Orbit

This 5-minute read provides a comprehensive breakdown of the costs involved in building an enterprise-level RetrievalAugmented Generation (RAG) system.

I ll explain the necessity of such a system to overcome the limitations of Large Language Models (LLMs) in handling complex tasks that can be a perpetual headache.

The aim of this paper is to consider and measure the full cost, and technical considerations, of implementing such a system from scratch.

While LLMs demonstrate remarkable capabilities in general knowledge, reasoning, and coding, they are not directly applicable for enterprise-level professional tasks for several key reasons:

To bridge these gaps, a Retrieval-Augmented Generation (RAG) system is necessary. This system connects data with LLMs, enhancing their utility.

While basic RAG systems can be created by simply dropping a PDF into ChatGPT and asking questions, scaling to handle large volumes of data is exponentially more complex and costly.

A common misconception is that LLM costs dominate the expenses of a RAG system. This paper aims to break down the essential components of a RAG system and elucidate the costs involved in building it, even before the first interaction occurs.

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Efficiently import and process data from various sources, crucial for handling large volumes of documents. Ensures data is ingested promptly and accurately, impacting the quality and timeliness of responses.

Centralised repository for all entities, ensuring consistency and accuracy across the system. This helps in precise entity recognition, improving the system s understanding and contextual relevance.

Handles document and data metadata to enhance search and retrieval. Efficient metadata management facilitates faster and more accurate information retrieval, improving overall response quality.

Prepares data by cleaning, normalising, and transforming it, which is essential for handling large data volumes. Proper pre-processing ensures that the data is in a usable format, enhancing the system's performance and accuracy.

Secure storage for PDFs and flat files ensures that all documents are easily accessible and protected. Proper storage solutions enhance data retrieval speeds and ensure data integrity.

Advanced search capabilities for quick and accurate data retrieval, crucial for managing large document volumes. A robust search engine improves the system's ability to find and deliver relevant information swiftly.

Manages and coordinates workflows and processing logic, ensuring efficient operation. This component is vital for maintaining system performance and reliability.

Intuitive interface for users to interact with the system, ensuring ease of use and accessibility. A user-friendly interface improves user engagement and satisfaction.

Let's firstly look at how much it cost to run the system without considering development cost yet. To simplify the calculations, the costs are based on 1 million pages worth of PDFs (10,000 PDFs, each with 100 pages). Some costs are one off calculations, some costs are for storage, so let's calculate how much every year the cost will be.

For simplicity, I have used standard cloud provider's pricing for comparison purposes.

PDFs should be parsed into granular blocks for later retrieval which is much more accurate. We will use Microsoft's document AI parsing service, which is the best quality commercial APIs based on our experience.

The cost of PDF parsing is about $35 per 1000 pages,

so in total, $35*(1,000,000/1000) = $35,000

OpenAI s Ada v2 is a mainstream embedding model, and its price is $0.10 / 1M tokens

Assuming about 1000 tokens per page, so in total

1,000,000 pages / 1,000 tokens per page * $0.1 = $100

The file size grows significantly when converted to vector format, especially at block level. If we assume each page is 1MB and AWS storage starts from $25 per month. To store data for 1 year will cost 1T*$25*12 = $300

To keep it robust, we use Elastic 1.7 TB storage | 58 GB RAM | 8 vCPU and 2 zone configurations, which is $5.6246 per hour, so total cost in one year is:

Let's add up all the general costs for application deployment, databases, etc, every year it costs $10,000.

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$10,000+$300+$49,272+$100+$35,000 = $94,672

In total to process 1 million pages of PDFs, it costs

data+licensing+processing = $94,672+$10,000+$20,000 = $124,672 And Development cost of $50,000

*This can be considered an on-going cost, however, the actual figure will vary on the number of pages PDF you run through the RAG system.

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Developing a robust Retrieval-Augmented Generation (RAG) system is far more complex and expensive than it initially appears. While a simplistic estimation might suggest 100 development days and $50,000, this overlooks several hidden costs that can escalate unpredictably:

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From Challenges to Solutions

Re-introducing Orbit – Rethink the Impossible 

We’re excited to announce the evolution of our brand from Orbit Financial Technology to simply Orbit, with a fresh perspective encapsulated in our new tagline: Rethink the Impossible. This evolution reflects our commitment to pushing the boundaries of what’s achievable in financial intelligence by delivering unparalleled accuracy, transparency, and efficiency through AI-driven solutions.

Clearly, we are in a rapidly evolving financial landscape, therefore, the need for sophisticated, adaptable tools has never been greater. Many of our clients require more than just data—they need insights delivered with precision and speed, especially as the complexity of global markets and regulatory expectations grow. With this rebrand, we are doubling down on our mission to enable financial professionals to think differently, transforming workflows and uncovering opportunities that may have seemed out of reach.

Our rebrand is not just cosmetic; it’s a promise to empower you, financial professionals, to achieve what was once thought impossible. Orbit is a platform built for those who demand advanced solutions, offering:

Transforming Financial Services Use Cases

Orbit is designed to simplify complex tasks and introduce new efficiencies across key financial services use cases, including:


Navigate the immense volume of global exchange filings and earnings call transcripts effortlessly. With Orbit, users receive real-time alerts and insights from official filings worldwide, helping you stay ahead with crucial regulatory and market information.

2. Portfolio Monitoring & Thematic Research


Research focused on themes like AI, fintech, or sustainable energy is in high demand. Orbit provides automated tools to track these themes across our knowledge base, enabling you to efficiently gather insights that align with your strategic focus areas.


Environmental, social, and governance (ESG) concerns are now central to investment strategies. Orbit empowers stewardship teams with tailored alerts and insights, tracking ESG developments across industries and geographies to support responsible investing.


As China’s influence in global markets grows, so does the need for timely, accurate intelligence. Our system captures and translates transcripts directly from Chinese sources, providing critical information for global investors looking to make data-informed decisions.

These use cases highlight our mission to rethink and redefine what’s possible for financial services professionals. By combining extensive, high-quality data with advanced AI capabilities, Orbit doesn’t just meet industry standards—it challenges them, offering new paths to clarity and confidence in decision-making.

Orbit’s rebrand marks an exciting new chapter, and we’re thrilled to help our clients achieve the extraordinary. With the ongoing expansion of our data resources and the evolution of AI-powered capabilities, Orbit remains committed to driving financial innovation for a future where nothing is impossible.

Explore how Orbit can help you rethink the impossible

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Orbit2.0

We’re excited to announce the evolution of our brand from Orbit Financial Technology to simply Orbit, with a fresh perspective encapsulated in our new tagline: Rethink the Impossible.

Orbit 2.0

Compliance Due Diligence: Smarter, Faster, and More Reliable

Compliance due diligence has often been seen as a tedious, slow-moving process prone to human error and blind spots. Orbit changes that narrative entirely. Powered by cutting-edge AI, our platform delivers compliance analysis with an unprecedented combination of speed, accuracy, and personalisation—making the impossible, possible.

Effective compliance starts with trust in your data. Orbit leverages advanced AI to provide transparent and accurate insights into regulatory and compliance information. Whether you’re navigating complex regulations or evaluating partnerships, our platform ensures every analysis is clear, reliable, and actionable.

Orbit isn’t a one-size-fits-all solution—it’s a platform designed with your specific needs in mind. By tailoring the data and workflows to your organisation’s requirements, we allow you to focus on the areas that matter most. With Orbit, you’re in control, backed by tools that adapt to your unique challenges.

Why spend time on repetitive, routine tasks when automation can handle them for you? From compliance checks to regulatory monitoring, Orbit’s automation capabilities streamline the tedious processes, so your team can dedicate their time to high-value decision-making.

Orbit dives deep into the world of unstructured data, using advanced AI and Natural Language Processing (NLP) to uncover risks traditional methods often miss. By analysing regulatory filings, legal documents, market reports, and more, our platform provides a thorough view of your risk landscape—helping you anticipate vulnerabilities before they become problems.

Orbit simplifies and enhances due diligence processes by giving you direct access to detailed information from original documents.

Compliance and risk reporting shouldn’t be a chore. With Orbit, you can create reports tailored to your organisation’s specific parameters.

Orbit simplifies compliance due diligence with AI-powered tools for risk identification, automated monitoring, and customisable reporting. Enhance transparency, streamline audits, and stay ahead of regulatory changes with ease.

Orbit Data

Our knowledge base is essentially a dataset that we maintain. Each dataset has a clear scope of document types, coverage, and history.

For example, Global Regulatory Filings are made up from over 30 stock exchanges that contain all disclosures and history since 2017.

Our Data Universe

Seamless Data Integration

Web Scraping for Real-time Insights

Rethink the impossible