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Financial firm saves over 30 employee hours monthly after adopting Needl.ai

Financial firm saves over 30 employee hours monthly after adopting Needl.ai

February 1, 2024

Objectives

A five-member financial services firm specializing in investment management wanted a single tool to compile and process data scattered across platform silos and devices.

The firm handled large volumes of:

Research reports from email alerts, broker updates, group messages via WhatsApp or Telegram, and paid subscriptions

Company news and announcements from popular PR channels, blogs, regulatory websites and exchanges

Investment-related issues in real-time from emails, chats, Google Drive files, Notion, and other communication platforms

Critical decision-making information from conference calls, annual general meetings, and more.

Leveraging all that data to get valuable insights meant setting up a knowledge repository that can:

Gather data from various sources

Organize it into the right categories or folders

Analyze it to provide intelligent recommendations

Discuss and share the right data with the right people instantly

The firm used a motley set of tools to keep track of its data. However, they lacked a centralized repository and a mechanism to quickly search, retrieve, and share data, which led to critical productivity and collaboration issues.

Financial firm saves over 30 employee hours monthly after adopting Needl.ai

Industry
Financial Services
Team size
4+
Established in
2015
Headquarters
Mumbai

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Objectives

A five-member financial services firm specializing in investment management wanted a single tool to compile and process data scattered across platform silos and devices.

The firm handled large volumes of:

Research reports from email alerts, broker updates, group messages via WhatsApp or Telegram, and paid subscriptions

Company news and announcements from popular PR channels, blogs, regulatory websites and exchanges

Investment-related issues in real-time from emails, chats, Google Drive files, Notion, and other communication platforms

Critical decision-making information from conference calls, annual general meetings, and more.

Leveraging all that data to get valuable insights meant setting up a knowledge repository that can:

Gather data from various sources

Organize it into the right categories or folders

Analyze it to provide intelligent recommendations

Discuss and share the right data with the right people instantly

The firm used a motley set of tools to keep track of its data. However, they lacked a centralized repository and a mechanism to quickly search, retrieve, and share data, which led to critical productivity and collaboration issues.

Challenges

Research analysts spent most of their time – 20 to 30 hours each month – looking for data and figuring out how to share it. This translated to thousands of dollars lost in merely searching for and organizing data for the firm.

Moreover, the data found and shared could have multiple versions and lack the essential context needed to make informed decisions. As a result, the firm faced challenges such as: 

1. Information overload

Keeping track of critical information pouring into the firm’s various communication and data management platforms in real time was challenging. The data deluge led to an information overload, with the team struggling to stay on top of everything.

2. A loss of valuable information

In other cases, the data shared would get lost or become unavailable if an analyst wasn’t online. For example, file attachments shared over chat groups weren’t available for download at a later stage. 

3. Siloed data

Even when the right data gets downloaded, it’s stored in an analyst’s personal device rather than a centralized cloud-based repository. Over time, such practices lead to data silos and make data sharing more challenging.

4. An inability to analyze data in real-time

Processing thousands of research reports and real-time data from stock exchanges to create a knowledge graph was a herculean task. Moreover, relying on numerous scattered tools and manual intervention made data processing and analysis inefficient and error-prone.

How Needl.ai solved the productivity problem

The firm took Need for a test drive and the results were immediate. After setting up Needl.ai – within an hour, the firm had:

1. A centralized knowledge repository for historical and real-time data

Needl.ai integrated data from emails, chat groups, social media channels, storage platforms, and even meta searches on popular search engines in a single cloud-based repository.

All existing documents, media, and links were now on Needl.ai, rather than residing in the personal devices of analysts. Needl.ai also enabled the firm to set up crawlers that continuously collect news, updates, and announcements from exchanges, regulators, company blogs, news sites, and more. 

As a result, the firm removed data silos and brought all of its data under a single roof. Users with the right credentials could instantaneously view, download, and share any data. 

Fig: Consolidated Information for Efficient Insights

 

2. A Google-like search engine for data

Using Needl.ai’s powerful, ad-free search engine, the firm could search for data across apps, platforms, websites, and multiple document types (audio, video, or images). Users could quickly pull up granular pieces of information on any company, industry, or topic.

Fig: Search across company, industry or topic within your information universe.

The algorithm ranks search results in the order of contextual relevance. 

So, users could quickly sift through search results to find the data they need, without opening each file, link, message, or PDF. 

Lastly, users can fine-tune their search by filtering the results by date, source, sender, chat groups, creation date, and more to cut the time spent searching for data substantially.

Fig: Find what you are looking for in seconds with Needl.ai's one search.

3. An intelligent data platform to quickly see, understand and share data

Once set up, Needl.ai classifies and labels data using ML-based tagging algorithms. It also intelligently groups real-time data into the relevant feeds. However, if users required additional tags to further organize their work, they could create new tags. 

They could also bookmark content and review it later under the Bookmarks tab.

As mentioned earlier, Needl.ai clips and summarizes large data sets to help users understand and assess data quickly. Needl.ai lets users clip and convert paragraphs, tables, and images from PDFs to word documents or Excel spreadsheets. 

The clipped content is available in the Clipped Reports tab.

Fig: AI-powered extraction and conversion of text/table from PDF/images

 

As a result, the firm’s analysts didn’t have to leave Needl.ai to open multiple documents, copy data from each file, and paste it into another document. Instead, Needl.ai automated such time-consuming tasks, making it easier for the firm to consume, interpret and use data.  

Thanks to needl.ai, we are now able to utilize the team’s time and efforts much more scientifically, leading to increased operational efficiency.

Chief investment officer

Thanks to needl.ai, we are now able to utilize the team’s time and efforts much more scientifically, leading to increased operational efficiency.

Chief investment officer