Today, San Francisco-based AI productivity startup Dashworks announced the launch of Dash AI, a natural language processing (NLP) search assistant designed to organize internal knowledge access and help enterprise employees be more efficient. The company also announced the expansion of its seed round with $5 million in additional funding from existing investor Point72 Ventures.
Dashworks said it plans to use the capital to further grow its footprint and expand the capabilities of the new AI assistant, which is already seeing strong demand from startups and large enterprises looking to bring order into their massive data ecosystems.
“Our mission at Dashworks is to empower teams to unlock the full potential of their internal knowledge, and Dash AI is a significant step forward in achieving that goal,” said Prasad Kawthekar, CEO and cofounder of the company. “With the support of Point72 Ventures and other investors like South Park Commons, we are well-positioned to enhance our platform and revolutionize how teams find, organize, and utilize information in the workplace.”
Today, internal business knowledge remains scattered across dozens of applications and systems. From Asana and Slack to Notion and Google, there’s a lot happening everywhere, leaving both structured (spreadsheets) and unstructured data (slack conversations, wikis) dispersed and teams struggling to keep up. Employees have to work just to get the information needed to get the job done, which leaves a measurable impact on productivity and the bottom line.
Kawthekar, who studied at Stanford, described a “frustrating” experience he had while working at software development company Blue River Technology.
“I was asked to start working on a new project related to credit scores for lending, and it took me seven email threads and eight weeks to find the right documents,” he told VentureBeat. “This experience made me realize the true cost of scattered information at a company.”
To address this problem, he teamed up with fellow Stanford batchmate Praty Sharma and launched Dashworks in 2020. Initially, the company offered an internal search platform — similar to Google web search — to make it easy for employees to find documents, messages, emails and tasks through natural language inputs. Now, this offering has evolved into the more comprehensive Dash AI.
“While we found good initial traction with this product, we realized that the advent of large language models (LLMs) meant that we could create a product with natural language conversational capabilities,” said Kawthekar. “Our core product today is Dash AI, a secure and cost-effective AI assistant designed to transform how teams access and leverage their internal information across multiple applications.”
Beyond finding the information needed, Dash AI can instantly answer any question that an employee might have by searching, understanding and summarizing a company’s tech stack. It can understand large codebases, debug code, draft emails and product collateral, provide historical context on projects, assist with complex technical and non-technical task execution, answer questions about company policies and continuously learn through user feedback and answer verification.
Whenever a query is made, Dash AI uses an API call to connect to the relevant applications and source the required information in real time. This means there’s no need to store or index proprietary information as is the case with legacy enterprise search solutions. Once the information is sourced, the company’s system performs several functions like query understanding, ranking and synthesis in real-time to create the final answer.
The AI assistant currently supports 17 integrations, with two to three new integrations being added every week. It went live six weeks ago (in beta) at a price point of $4.99 per user per month and is witnessing 100% week-over-week growth, the company said.
“We’ve seen rapid adoption from existing customers like Swiggy (India’s largest food delivery service), Invisible Technologies and Owner,” said Kawthekar. “We’ve also closed several new deals that we can’t talk about quite yet and have a couple more enterprise deals in the pipeline.”
With this funding, which takes the company’s total capital raised to $9 million, Dashworks plans to add new capabilities to strengthen Dash AI’s function as an internal Q&A chatbot, Kawthekar explained. This includes deeper and broader integrations with the source of truth work apps like databases and voice transcription as well as workflows for verifying and improving AI answers.
“In the medium term, we plan to add support for more use cases like personal assistant-type queries (‘What is my schedule for the day?’) and the ability to conduct more sophisticated analyses (‘How did our burn rate change last quarter?’),” the CEO added.
Other players targeting the knowledge management space are Glean and Guru. Data platform major Snowflake has also launched Document AI to help its customers glean information from their unstructured documents using natural language queries.
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Today, San Francisco-based AI productivity startup Dashworks announced the launch of Dash AI, a natural language processing (NLP) search assistant designed to organize internal knowledge access and help enterprise employees be more efficient. The company also announced the expansion of its seed round with $5 million in additional funding from existing investor Point72 Ventures.
Dashworks said it plans to use the capital to further grow its footprint and expand the capabilities of the new AI assistant, which is already seeing strong demand from startups and large enterprises looking to bring order into their massive data ecosystems.
“Our mission at Dashworks is to empower teams to unlock the full potential of their internal knowledge, and Dash AI is a significant step forward in achieving that goal,” said Prasad Kawthekar, CEO and cofounder of the company. “With the support of Point72 Ventures and other investors like South Park Commons, we are well-positioned to enhance our platform and revolutionize how teams find, organize, and utilize information in the workplace.”
Why Dash AI for internal knowledge?
Today, internal business knowledge remains scattered across dozens of applications and systems. From Asana and Slack to Notion and Google, there’s a lot happening everywhere, leaving both structured (spreadsheets) and unstructured data (slack conversations, wikis) dispersed and teams struggling to keep up. Employees have to work just to get the information needed to get the job done, which leaves a measurable impact on productivity and the bottom line.
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Kawthekar, who studied at Stanford, described a “frustrating” experience he had while working at software development company Blue River Technology.
“I was asked to start working on a new project related to credit scores for lending, and it took me seven email threads and eight weeks to find the right documents,” he told VentureBeat. “This experience made me realize the true cost of scattered information at a company.”
To address this problem, he teamed up with fellow Stanford batchmate Praty Sharma and launched Dashworks in 2020. Initially, the company offered an internal search platform — similar to Google web search — to make it easy for employees to find documents, messages, emails and tasks through natural language inputs. Now, this offering has evolved into the more comprehensive Dash AI.
“While we found good initial traction with this product, we realized that the advent of large language models (LLMs) meant that we could create a product with natural language conversational capabilities,” said Kawthekar. “Our core product today is Dash AI, a secure and cost-effective AI assistant designed to transform how teams access and leverage their internal information across multiple applications.”
Instant question answering
Beyond finding the information needed, Dash AI can instantly answer any question that an employee might have by searching, understanding and summarizing a company’s tech stack. It can understand large codebases, debug code, draft emails and product collateral, provide historical context on projects, assist with complex technical and non-technical task execution, answer questions about company policies and continuously learn through user feedback and answer verification.
Whenever a query is made, Dash AI uses an API call to connect to the relevant applications and source the required information in real time. This means there’s no need to store or index proprietary information as is the case with legacy enterprise search solutions. Once the information is sourced, the company’s system performs several functions like query understanding, ranking and synthesis in real-time to create the final answer.
The AI assistant currently supports 17 integrations, with two to three new integrations being added every week. It went live six weeks ago (in beta) at a price point of $4.99 per user per month and is witnessing 100% week-over-week growth, the company said.
“We’ve seen rapid adoption from existing customers like Swiggy (India’s largest food delivery service), Invisible Technologies and Owner,” said Kawthekar. “We’ve also closed several new deals that we can’t talk about quite yet and have a couple more enterprise deals in the pipeline.”
Plan to add more features
With this funding, which takes the company’s total capital raised to $9 million, Dashworks plans to add new capabilities to strengthen Dash AI’s function as an internal Q&A chatbot, Kawthekar explained. This includes deeper and broader integrations with the source of truth work apps like databases and voice transcription as well as workflows for verifying and improving AI answers.
“In the medium term, we plan to add support for more use cases like personal assistant-type queries (‘What is my schedule for the day?’) and the ability to conduct more sophisticated analyses (‘How did our burn rate change last quarter?’),” the CEO added.
Other players targeting the knowledge management space are Glean and Guru. Data platform major Snowflake has also launched Document AI to help its customers glean information from their unstructured documents using natural language queries.
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Author: Shubham Sharma
Source: Venturebeat
