GoodGist transforms workplace productivity with its innovative autonomous AI agents, designed to tackle repetitive and time-consuming tasks. Pulse 2.0 interviewed GoodGist co-founder and CEO Ruban Phukan to learn more about the company.
Ruban Phukan’s Background
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What is Ruban Phukan’s background? Phukan said:
“I have been working in the field of AI and Machine Learning for 20+ years, focusing on solving human-scale problems in both consumer and business technology space. I started my career at Yahoo as a member of the company’s first data science team and had the amazing opportunity to work closely with co-founder David Filo. I started Bixee, a machine-learning-powered vertical search company that helped consumers get relevant results on jobs, vacation-travel deals, and shopping recommendations based on their interest profile. Bixee Travel evolved into Goibibo, which became the number #2 travel destination in India and was later acquired by the #1 travel destination – MakeMyTrip. I then started DataRPM, one of the pioneers in Enterprise AI for Industrial IoT, working with customers like GE, Samsung, Siemens, Jaguar Landrover, and many others.”
“My current startup is GoodGist, where we use Agentic AI to provide an autonomous knowledge management platform for software companies for their partner, customer, and employee enablement. This delivers personalized learning paths, use-case-centric research, and instant answers to software-related questions to empower the right stakeholder, with the right knowledge, at the right time. “
Formation Of GoodGist
How did the idea for GoodGist come together? Phukan shared:
“The idea of GoodGist came from the personal experiences of all the founders. We all have encountered the need to constantly upskill ourselves on software tools, platforms, and methodologies in the ever-changing technology world. The traditional approach of just doing a web search for just-in-time information is no longer scalable, as professionals are hard-pressed for time and attention span today. The rate at which technology evolves today needs a completely different approach to managing knowledge to keep pace.”
“We have seen these challenges in employees, customers, and partners of software companies. Inefficiency in knowledge management costs businesses significantly in unrealized revenue, support costs, and productivity losses. We wanted to solve this problem as it is a human-scale issue, and applying AI is the only way to solve it at scale while keeping the costs in check.”
Favorite Memory
What has been Phukan’s favorite memory working for the company so far? Phukan reflected:
“The best memories are always when customers and teams come together to evolve the solution. One of our offerings is partner enablement, which came around as we were discussing the employee enablement approach with a prospective customer. They shared how painful their partner enablement process is. This is one of the Fortune 100 software companies known for its excellence in evolved partner processes. So, we were pleasantly surprised about their human-scale challenges.”
“We have an amazing team at GoodGist that could immediately identify the problem and put together an effective solution on our platform in a matter of days that simply amazed the customer.”
“It is moments like these which gives make us extremely proud of what we have built, and the team that we have that is super committed to the success of our customers and also the joy of seeing the satisfaction of the customer.”
Core Products
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What are the company’s core products and features? Phukan explained:
“The core product of GoodGist is an autonomous knowledge management platform.”
“It enables software companies to connect to their content repositories, be it their product documentation, support ticket systems, online forums, document repositories, videos, knowledge bases and LMS, and CRM/PRM/HRMS system in addition to the open web.”
“The platform then takes the requirements of the companies’ end customers or their employees and partners to specify their learning objectives and creates personalized learning paths and content, taking into account the role, use case, and proficiency level of the individual learner.”
“The platform also enables users to research any topic, whether for technology understanding, market and competitive research, sales proposals, marketing content, RFP responses, reference architectures, solution architectures, or many more. The research is done in an automated way and doesn’t require manual look-up and aggregation of information, saving time. Also, it identifies highly relevant and authoritative content down to the version of the software.”
“Finally the platform enables users to ask any ad-hoc questions, whether it is related to the implementation of the software and technology or troubleshooting questions. It removes the burden of manual support as the AI generates the best response referring to all available content, documentation, and support ticket repositories.”
Challenges Faced
What challenges have Phukan and the team faced in building the company? Phukan acknowledged:
“Just like using cars had to go through a mindset shift from fast horses, every technology innovation faces the challenge of helping organizations overcome the mindset shift from the status quo to what is needed to embrace the new approach.”
“We face a similar challenge with the status quo as the traditional knowledge management for customer / partner / employee enablement has always been manually created one-size-fits-all content or manual support. Now, businesses have to bring in the change to take an AI-first approach to enable personalized, use-case-centric, and instant enablement approaches. This is a significant change management task.”
“To overcome the challenges, we take a three-pronged approach:
i) Education and thought leadership. We share a lot of information on why businesses should look at this approach and how it works.
ii) Proof of concept and value. We do PoC and PoV experiments to show the quick value of the business.
iii) Help with change management. We work closely with businesses to enable the change management and organization-level adoption of the approach to get them future ready.”
Evolution Of GoodGist’s Technology
How has the company’s technology evolved since launching? Phukan noted:
“We have built a robust Agentic AI framework that leverages and builds Knowledge Graphs on various software and technology categories to be able to serve the most relevant content to users on-demand and in real-time via web, mobile, embeds and APIs. We have a patent-pending as well on our approach.”
“We evolve our technology every day on the following:
i) Solving hard problems for customers—We started with employee enablement and then expanded to partner and customer enablement. We let customers and demand drive the roadmap.
ii) Best UI/UX—We constantly strive to innovate in UI and UX to deliver the most simple and intuitive user experience that masks the complexity of the underlying technology.
iii) Guardrails – since we are an AI-native platform, one of the highest priorities for us is ensuring enterprise-grade guardrails that ensure authority, accuracy, relevance, and currency. We are constantly working on the guardrails.
- iv) Performance – Ensuring fast setup and fast response time is another key area that we constantly work on. We already have one of the fastest go-live times in the industry, where we have done setup in the range of 3 days to 3 weeks for most customers.”
Significant Milestones
What have been some of the company’s most significant milestones? Phukan cited:
“At GoodGist, the only milestone that matters to us is every customer for whom we have delivered value. We are a value-driven company. We have been fortunate to work with customers as large as Fortune 100 companies and as small as 20 people startups. We take pride in each of these customers, and they are all equally important to us in delivering significant value to their employees, customers, or partners.”
Customer Success Stories
When asking Phukan about customer success stories, he highlighted:
“We take pride in being able to make a significant impact on companies as large as Fortune 100 companies. While this company has a mature partner success team, they are only able to support a tiny fraction of partners in their ecosystem. These top partners receive white-glove support, and it costs the company almost as much as $1000 per support ticket raised by partners. But most of their long tail partners (1000s of them) are pretty much left on their own to enable themselves, causing a lot of frustration, revenue loss, and end customer satisfaction issues because the partners couldn’t deliver the solution properly to them.”
“With the autonomous knowledge management platform from GoodGist, they were able to scale their partner enablement to their long tail, where all of them received personalized onboarding, use-case-centric guidance for implementation, and instant answers to questions and problems, all powered by AI.”
“In the initial results, they saw a 45% decrease in onboarding time, a 35% reduction in support costs, and a 55% increase in long-tail partner engagement and effectiveness.”
Funding
When asking Phukan about the company’s funding details, he revealed:
“We were incorporated in November 2023. Earlier this year, we raised a pre-seed of $1 million from Fortytwo VC, Cedar Ridge Ventures, DX Partners, and angel investors from companies like IBM, Amazon, Microsoft, Arista Networks, and others.”
Total Addressable Market
What total addressable market (TAM) size is the company pursuing? Phukan assessed:
“The global knowledge management software space is projected to reach $40 – $50 billion by the end of this decade, growing at a 13-15% CAGR. But most of these estimates don’t quite factor in the AI-driven aspect, which could accelerate this significantlyly and create a bigger market as autonomous knowledge management increases the reach to organizations of all sizes.”
Differentiation From The Competition
What differentiates the company from its competition? Phukan affirmed:
“The traditional knowledge management software systems are a manual-first approach. While they are trying to implement AI as an add-on, the effectiveness of AI in a manual-first core is very limited. But GoodGist is designed ground up as an AI-native platform. We are AI-first in everything that we do, and this gives our customers a significant edge in the fastest go-live, significant scaling in a very cost-effective way, and a truly autonomous platform.”
Future Company Goals
What are some of the company’s future goals? Phukan pointed out:
“Our goal is to become as ubiquitous as Google is for web search for Enterprise Knowledge Management for all stakeholders – employees, customers, and partners. We want to create an even playing field for organizations of any size where knowledge is no longer a barrier, and the right knowledge is available to the right stakeholder at the right time.”
Additional Thoughts
Any other topics to discuss? Phukan concluded:
“We often get asked why organizations can’t use something like ChatGPT or build their own using LLMs. This is the same reason that one would not want to use a database with a SQL query UI as their CRM software replacement.”
“Today, there is a lot of excitement around Large Language Model (LLM) technology, as was the case when databases first came out. However, there is a significant path to transforming a platform into an effective business solution. We have spent many years in research on knowledge systems, learning systems, agent-based systems, and cognitive science, and we have worked in the field of AI / ML and large-scale enterprise software for many decades. We know how many challenges we have to overcome on a daily basis and continuously make our solution enterprise-ready and keep iterating and improving it.”
“Currently, LLMs may seem like a silver bullet for everything. But like any foundational technology layer, it is easy to create experiments, but the nuances get deeper once you implement them into a business-ready solution. For companies who are not in the business of building knowledge management systems, the technical debt of doing it in-house is too huge to make it ineffective for practical use or becomes cost prohibitive soon. We have customers who came to us after failed experiments of trying to build it in-house.”
