KNO

LoginStart Now >Get to know KNOKNO is live on Slack app marketplace. Introducing the new way to get answers to your curiosity.Precise and quick.Try KNO onDon't have a Slack workspace? Start here.Install KNO on Slack in three stepsLog into your Slack workspaceGo to the workspace of your choice. Create a Slack account if you don't have one.Open 'Browse Apps' screenClick on the small '+' symbol to the right of the Apps tab on your Slack sidebarSearch and select KNO. Get going.Choose KNO app and start asking right away. Embed KNO in different channels.See it in actionHave a use case in mind for your product? Let's talkYou can teach KNO how to answerPublicly available web dataKNO, by default, is trained on OpenAI's GPT3 model which includes 800GB of information.Custom prescribed sourcesKNO can prioritise knowledge from specific sources, say your own help resourcesSegues to customer supportRedirect users to key people in case the conversation does not end conclusivelySecurityWhat you ask KNO, stays with KNOThe questions you ask remain walled inside the algorithm and are never associated with your identity. Read our Privacy Policy9.4NPSAs rated by users who have tried KNO on messengers50%GROWTHMonth on month user growth on KNO86%+MAUReturning users asking at least one queryDon't just take our word for itSome of our pilot customers have nice things to say about KNO“KNO has become my go to place for asking technical questions.”ProfessorUC Irvine“It has the potential to become the next Gong. Better than Google.”CTOLeading analytics automation company“We were looking for something like this for quite some time now.”CEOHR-Tech software leaderGive it a try. Get surprised.Test it at will and get in touch with us if you wish to use it in your product.Try KNO onDon't have a Slack workspace? Start here.FAQsHere are answers to some questions we have already been asked. Feel free to write to us in case you have more.What does KNO know?KNO can answer general questions about technical issues fairly well and then some. Especially around AI and ML. However it surprises us in a pleasant way questions not very technical in nature.How do I use KNO?If your Slack workspace allows you to install apps, you can just use this link to add it to your Slack. Thereafter, you can either engage in a private conversation with KNO or do add it to a Slack channel.How much do I pay to use KNO?Few good things in life come free. KNO is one of them.Can I train KNO on specific topics?Yes, we are working on this feature. Please reach out to hello@alltius.ai or write to us to discuss further.Why about my privacy?We care a lot of our users’ privacy. What happens with KNO stays with KNO. Please refer to our privacy policy for more details.Who's behind making KNO?We are a team of engineers, data scientists and designers trying to create disrupt product enablement by changing how users exchange questions and answers on products.Still have unanswered questions? Get in touchBuild intelligent, secure and accurate GenAI assistants for your customers, support and sales teams ProductsPlatform OverviewSales Enablement AssistEnd User AssistSupport Agent AssistChannelsWidgetSlackAPIsCompanyAboutCareersBlogOur valuesContactPrivacy PolicyTerms of Use© 2025 Alltius Inc --- LoginStart Now >Customer StoriesAssurance IQAssurance IQ slashes agent ramp up time from 6 months to 1 month with Alltius' sales excellence assistantsAssurance IQ slashes agent ramp up time from 6 months to 1 month with Alltius' sales excellence assistantsAs millions of insurance buyers reach out with an intent to purchase a plan suited to their needs, Alltius' assistants coached on thousands of insurance plans each year, empower Assurance's sales agents to have personalized, well-informed and well pitched conversations with customersClientAssurance IQSeattle, WashingtonHQInsuranceProductsIndustryCX End User AssistCX Agent Assist67%Reduction in agent onboarding time100%Tested accuracy of responses96%+Agent questions answered6 → 2 monthsAgent ramp up time6000+Insurance plans comparedKnowledge6,000+ insurance plans across 100s of plan providersSkillsSearching plans based on user needs and narrowing search, comparing 2-6 plans on specific dimensions, pitching a plan, building information grid across thousands of plansChannelsPlayground, APIsAboutAssurance IQ provides personalised guidance and makes finding and using medicare, health, life, motor and home insurances for millions of North Americans each year. It enjoys patronage of over 17 million customers and uses data science and machine learning to narrow down options for coverage. SituationAssurance IQ receives millions of website visitors monthly, generating hundreds of thousands of leads. These leads are directed to the sales contact centre, where thousands of licensed insurance agents assist them over a phone call in making decisions tailored to their individual needs.However, this process has several challenges. With over 6,000 insurance plans to be familiar with, agents face uphill tasks in staying updated on plan details and specific terms and conditions that change every year in the insurance renewal season from September to December. The problem is more acute when the agents are relatively new or recently hired.  New agents typically take around six months to reach the same level of effectiveness as experienced agents.Next comes the shortage of time and the multitasking involved. Agents need to efficiently engage with callers in under 30 minutes, quickly understand their needs from a vast array of plans, explain why a particular plan is recommended, highlight its benefits that align with the customer's needs, and ultimately close the sale convincingly. Delaying a decision often results in lost sales opportunities.CTO Nick Howard enlisted the help of Alltius to develop an AI tool for their agents - which would assist insurance agents in identifying plans with specific benefits as the customer conversation progresses, facilitate comparisons of these benefits across various plans, and ultimately provide valuable insights. Essentially, as agents engage with customers, the AI companion would offer talking points about the plans, highlighting not only how they cater to the customer's needs but also showcasing additional benefits that may not have been explicitly mentioned or recognized by the customer. SolutionA cross-functional team, comprising software engineers, product managers, designers, and data scientists from both Alltius and Assurance, was formed. The setup of Alltius assistant on the web app platform was swift and straightforward, with hundreds of medicare plans from various sources ingested and coached within minutes. In parallel, Alltius cleared a stringent info-sec review of the platform by the auditing team at Assurance.The team utilised Alltius' playground and APIs to assess the form and accuracy of the responses. The pilot project was divided into three phases that extended over a period of 10 weeks.Phase 1 : Customising skills (2-4 weeks): The goal was to assess and fine tune three specific assistant skills that would provide the maximum value to Assurance’s sales agents.Narrow the search : Distil top 5-10 relevant plans from thousands given the buyer is looking for addressing 3-5 specific needs and health issues. For example, the agent could ask "My client is a 60-year-old woman in Atlanta who utilises physical therapy, has glaucoma, and prioritises dental benefits. Which plan would best suit her?"Compare plans : Next, the assistant could give a comparison across benefits, inclusions, and exclusions across a narrowed choice of plans specific to the customer needs. For example, the agent could effectively ask, “Provide a concise explanation of how Plan X stacks up against Plan Y and Plan Z on the stated customer needs N1, N2, and N3.”Pitch a plan :  Finally, when the caller is now leaning towards a particular plan, help the agent give a 30-second comprehensive pitch highlighting how it addresses not only the stated needs but also the unstated needs of the caller. “Given that my customer has asked queries Q1, Q2, Q3 and has the needs N1, N2, and N3, provide me with a 30-second pitch that can help me close the sale.”Build the grid : Each season, a dedicated team of data entry operators and agents meticulously reviewed thousands of plans to extract and tally specific information in response to standard questions from each plan. For example, “What is the copay % or amount for out of hospital coverage?”  This process was time-consuming and required careful attention to detail. The goal was for the system to quickly generate a grid containing M pieces of information from N plans, which could then be validated by humans for accuracy and cross-referenced to the specific sections where the information was located. What used to take several months could now be completed in just an hour for generation and a day or two for validation.Alltius built these customised skills in a couple weeks and made it available for quality testing.Phase 2 : Quality testing (2 weeks): The teams focused on evaluating the quality and accuracy of responses, a crucial step in addressing trust issues given the vast amount of information in 6,000 plans, each with multiple pages. The question at hand was whether the AI assistant could provide accurate responses at scale with the specified skills. Assurance's team utilised Alltius' APIs to quickly generate thousands of responses for a sample set. Through collaborative fine-tuning, they achieved a precision rate of 100% and a recall rate of 96.2% (responses provided when relevant information is found in sources). This was more than acceptable to take this to the next level i.e. agent testing.Phase 3 : Prototype validate and POC (2 weeks): The task force took these assistants with a functional prototype to a varied mix of agent trainers, experienced agents and new agents. In all, over 10 individuals contributed to the validation process and the response was an overwhelming vote of trust on the utility of the assistants for the sales process and agent productivity. ResultsThe assistants returned valuable time to the agents, allowing them to focus on their core strength of listening to customers with empathy and fully comprehending their needs. This enabled the agents to provide undivided attention to customers, rather than spending time reviewing plan details individually and memorising offerings.Seasoned sales agents praised the assistants, stating, "This is excellent! It phrases things perfectly. It takes 6 months to get new agents up to mid-level of productivity. These tools would get them there in a third of the time."The estimated benefits of the assistant implementation include:Lift in conversion: By providing more personalised, needs-focused, and precise pitches during 30-minute phone calls, a 5-15% increase in call-to-sale conversion rates was projected, enhancing the overall buyer experience.New agent ramp-up time: With onboarding time reduced by 67%, new agents could reach mid-level productivity three times faster, resulting in a 20% improvement in overall productivity over an average two-year tenure.Tech man-month savings: The build-the-grid tooling would save 2-4 engineer months annually that would have otherwise been spent on information sifting tasks across thousands of plans.Way forwardWIth the promising results early on, the sponsors green-lit other projects to reconsider after the end-of-year insurance selling season. The first use case was auto-detection of buyer needs as the assistants make sense of the telephonic conversations. Next, would be to summarise the buyer needs and log across conversations so that different agents would instantly get context of past and present discussions. Additionally, the assistants could evolve into a knowledge navigator to get the best information, instantly and accurately across product, legal and operations manuals and policy wordings. ‍“One of the best [platforms] we have seen in the market. Also, they are one of the best teams we have worked with among our vendors after trying this ourselves for 2 years."Nick Howard, CTO, Assurance IQHappy customers. Quickest time to value.Matchbook AIMatchbook's data intelligence platform now offers Alltius powered AI assistant to its users from marquee Fortune 500 brands, where they can not only get instant help from across their documents but also raise tickets from within the bot.Read StoryStreamline Developer Workflows with Alltius Hiro Smart ToolHiro System helps thousands of crypto users by helping them locate precise help and code snippets in seconds from an abundant overwhelming pool of information and resources.Read StoryLeading Asian BankThe large multinational bank can now give instant recommendations to its 12 million customer base on its card products and near instant insights to its large equity analyst team with high quality interpretation of chart and table filled research reportsRead StoryYour journey starts hereLet us personally show you how Alltius can help your product.Request a personal demoBuild intelligent, secure and accurate GenAI assistants for your customers, support and sales teams ProductsPlatform OverviewSales Enablement AssistEnd User AssistSupport Agent AssistChannelsWidgetSlackAPIsCompanyAboutCareersBlogOur valuesContactPrivacy PolicyTerms of Use© 2025 Alltius Inc --- LoginStart Now >Customer StoriesAngelOneAngelOne achieves 65% ticket deflection and 15%+ contact ratio reduction within 2 months of going liveAngelOne achieves 65% ticket deflection and 15%+ contact ratio reduction within 2 months of going liveIndia's leading stock and mutual funds brokerage firm with over 18M users and $400B in average daily turnover, effortlessly deploys Alltius powered widgets on its trading product, coached across 20K+ documents, pages and articles and integrated with custom API workflows.ClientAngelOneMumbai, IndiaHQFinancial ServicesProductsIndustryCX End User AssistCX Agent Assist65%+Self-serve ticket deflection15%+Reduction in contact ratio100,000Interactions per week< 2 monthsTime to value breakeven300,000+Users assisted each monthKnowledge20,000+ multilingual webpages, user account APIs, canned responses, Google DriveSkillsMulti-tier 2-step QnA, menu workflows, intent recognition, separate logged in user journey ChannelsWeb widget, mobile widgetAboutAngelOne is a listed stock and mutual funds brokerage house in India. At the time of writing, AngelOne has a user base of about 18 million users growing over 30-50% annually and an average daily turnover of almost $400 billion.In 2019, only 8% of Indian households held an active demat account, essential for trading in equities and mutual funds. The share more than doubled to 17% in 2023. The surge in retail investor base in India is attributed by experts to increased internet and smartphone penetration, attractive returns riding on the Indian growth story and digital first approach of most of the brokerage houses. SituationAngelOne is experiencing rapid growth in its user base compared to the overall brokerage market in India. This has led to a significant increase in the number of incoming tickets for onboarding and support queries. With over 1,000 support agents, the contact centre now handles more than 500,000 tickets per month.Previously, retail traders had to wait between 30 minutes to a few hours to receive assistance through email or phone. The resolution time was further impacted during events like IPOs, buybacks, and exchange glitches. Additionally, there was a disparity in knowledge among agents, with some being more informed about product features and troubleshooting than others.Even the more experienced agents were not fully leveraging their collective knowledge, which ranged from publicly available resources like product documentation to user-specific sources such as user dashboard APIs and past ticket resolutions. As with any other product documentation, keeping all documentation up to date was a big challenge.SolutionTo achieve their goals, AngelOne and Alltius collaborated to reduce the contact ratio by at least 20% and decrease the median customer query resolution time from 2-4 hours to just a few minutes. They aimed to cover all possible information sources within the organisation, including static and dynamic sources, synthesised and unsynthesized sources, and structured and unstructured sources. They also wanted to reduce the new agent settling time from months to just a few days and decrease the information access time for agents from hours to seconds.To start the process, a team of AngelOne support agents and product managers created a test Assistant with over 20,000 multilingual web pages that could process thousands of pages in less than an hour. They also created a test set of several hundred conversations that were monitored in Alltius testing Playground. After conducting a quality check of over 1000 data points and gathering feedback from over 50 agents, it was determined that Alltius was the best choice in terms of quality and capabilities. The assistants were then ready to be deployed across different user-facing channels via on-brand fully customizable web widgets.During this time, an audit of Alltius' platform architecture was conducted by the infosec team to ensure robust security and business continuity measures were in place, especially for a partner of Alltius’ vintage.Within three months, the assistants went live across web and mobile app platforms, increasing coverage from 5% to over 80% of the user base. The assistants were able to handle varied traffic patterns and scale up quickly when needed. They could handle peak capacity up to 10 times the median hourly conversation volume during critical events such as market opening hours, IPOs, and buybacks. AngelOne and Alltius held weekly meetings to assess performance and identify areas for improvement. The in-product audit trail and insights module helped the product team quickly understand documentation gaps and product issues. The custom labelling feature allowed for pattern recognition and insights in minutes instead of weeks. Through continuous testing after going live, AngelOne added several skills to their assistants, such as hierarchical search, answering date-time sensitive questions, detecting escalation intents, using option-based menu workflows, providing unique experiences for authenticated users, securely interacting with user dashboard APIs from the widget, and more.ResultsIn just a few months, Alltius has already powered over 1 million conversations and recorded over 100,000 sessions per week. As a result, the contact centre has seen a 20% decrease in ticket volumes. Median wait time for resolving issues is now slashed to a few seconds from a few hours earlier. More than 65% of users are able to resolve their queries within 1 minute by interacting with the Alltius assistant, instead of waiting for hours for a resolution. This number is expected to increase even further as Alltius provides insights on recurring product issues and documentation gaps that are being fixed. This has allowed agents to free up more than 20% of their bandwidth to handle more complex and high-priority queries. Additionally, the product team now receives product-focused issue discovery, custom analytics, and insights within a day, instead of waiting weeks. This helps them quickly understand the distribution and recurrence of specific product issues, fix them and witness material progress subsequently in a matter of a few hours.Way forwardAs a result of the positive results in key areas, Angel One plans to expand the use of Alltius assistants in various roles. These assistants will be trained and coached with different skill sets to enhance onboarding workflows, assist support agents, facilitate conversational order placement, and support algorithmic trading. Furthermore, the user-facing assistants will be integrated with numerous additional APIs to provide instant support and improve self-serve assistance beyond the current 65% level.Alltius is grateful to the AngelOne team for being a key design partner and early believer in shaping the platform.‍"Alltius’ work has been very promising. Within months they have deflected a substantial portion of our incoming tickets. We see them as our extended team."Jyotiswarup R, CTO, AngelOneHappy customers. Quickest time to value.Matchbook AIMatchbook's data intelligence platform now offers Alltius powered AI assistant to its users from marquee Fortune 500 brands, where they can not only get instant help from across their documents but also raise tickets from within the bot.Read StoryStreamline Developer Workflows with Alltius Hiro Smart ToolHiro System helps thousands of crypto users by helping them locate precise help and code snippets in seconds from an abundant overwhelming pool of information and resources.Read StoryLeading Asian BankThe large multinational bank can now give instant recommendations to its 12 million customer base on its card products and near instant insights to its large equity analyst team with high quality interpretation of chart and table filled research reportsRead StoryYour journey starts hereLet us personally show you how Alltius can help your product.Request a personal demoBuild intelligent, secure and accurate GenAI assistants for your customers, support and sales teams ProductsPlatform OverviewSales Enablement AssistEnd User AssistSupport Agent AssistChannelsWidgetSlackAPIsCompanyAboutCareersBlogOur valuesContactPrivacy PolicyTerms of Use© 2025 Alltius Inc --- LoginStart Now >Customer StoriesLeading Digital LenderA leading digital lender with $1B+ AUM slashes median customer support wait time from 4 hours to 5 minutes. A leading digital lender with $1B+ AUM slashes median customer support wait time from 4 hours to 5 minutes. Frontrunner lending platform with over 1M customers responds to over 3K emails each day with AI powered responses using previous customer interactions, company's knowledge base and a secure API integration over FreshDesk.ClientLeading Digital LenderBangalore, IndiaHQFinTechProductsIndustryCX Agent Assist75%Lower average wait time for email queries2XContact centre productivity improvement3000+Customers addressed daily over emails5 hr → 2 minDraft response generation time2500+Agents hours saved per monthKnowledge20,000+ previous conversations converted to knowledge, website content pages, user profile from CRMSkillsDraft ready-to-send email response with placeholders for customer specific variablesChannelsAPIsAboutThe client is a Series D funded financial services platform that offers retail and business loans with over $1B annual disbursements, $40M in annualised profit target and over 7 million customers.SituationThe client, a regulated fintech company, was seeking to reduce its operational costs by improving its support contact centre. They receive over 10,000 tickets per day, with half coming through emails and the rest through phone calls and in-app chat. However, customers have to wait an average of 4-5 hours for a satisfactory response. This is mainly due to the pre-opening hours ticket queuing and ticket assignment delays. A relatively smaller time is taken by support agents to draft email responses.One of the challenges faced by the client was the lack of utilisation of company wide tribal knowledge and the company's knowledge base when drafting email responses. With thousands of tickets flowing each hour, the manner in which the human agents responded to customers with different account profiles, default statuses, credit scores, etc. was a treasure trove. Moreover, there were inconsistencies in the quality of responses as it depended on the support agent's tenure and knowledge. New agents also took a significant amount of time (3-6 months) to become as effective as more experienced agents.The goal for the client is to reduce customer wait time by 50% while improving the quality and personalization of email responses. They also aim to decrease dependency on agent tenure and training levels while bringing more standardisation to the responses. Additionally, they hoped to increase agent productivity by 30-40% and reduce ramp-up time from 6 months to just 1 month.SolutionThe client and Alltius joined hands to slash the overall customer wait time for email responses. The team from Alltius, consisting of engineers and product managers, visited the Contact Centre to observe support agents as they responded to emails. They also interviewed the contact centre managers to gather their hypotheses on how to decrease customer wait time.The proposed solution involves implementing a highly secure AI assistant powered by the Alltius platform. This assistant would be coached using a vast  dataset consisting of resolved customer conversations, web documentation, and support agent handbooks. Through this training, the assistant would not only acquire knowledge but also familiarise itself with the company's typical response style and objection handling strategies. Leveraging this training, the assistant would generate a personalised initial draft email response that includes relevant placeholders for customer-specific details. These placeholders would then be replaced with specific values from within the organisation, ensuring that no sensitive information is shared or exposed to public LLMs. The agents could then accept or editorialise the suggested email response and finally shoot an email response off to the customer.The client successfully integrated this assistant with their FreshDesk agent workbench using Alltius’ asynchronous and polled API channels. The payload included customer details and their email query, while the response consisted of a well-crafted email generated from a synthesis of previous email exchanges, knowledge base information, and placeholder variables. This integration was tested on over a hundred sample tickets that were reviewed and approved by the business and contact centre operations. Upon clearing the quality thresholds, the agent was cleared for use by agents in real time.ResultsThe assistant has been upgraded and is now generating 3,000-5,000 email responses per day. This has led to a 50% decrease in the average waiting time for customers, which now stands at around 2-3 hours. Instead of spending a lot of time crafting responses, agents now focus on validating them. The majority of the necessary information for personalization is already filled in and drafted beforehand. Thanks to Alltius' asynchronous responses, the wait time before the contact centre opening hours and the time drafting an email is significantly reduced to only a few seconds. Consequently, the assignment queue is much shorter, resulting in a decrease in overall customer wait time.Way forwardThe main objective of Team Alltius is to enhance the quality of draft emails to a level where the additional effort put in does not justify the marginal improvement in quality. Next, our aim is to expand our coverage to handle all 5000+ customer emails received daily. Additionally, we will be conducting pilots to address in-app chat scenarios. Furthermore, we will be implementing ticket assignment and labelling use cases to reduce customer wait times.Happy customers. Quickest time to value.Matchbook AIMatchbook's data intelligence platform now offers Alltius powered AI assistant to its users from marquee Fortune 500 brands, where they can not only get instant help from across their documents but also raise tickets from within the bot.Read StoryStreamline Developer Workflows with Alltius Hiro Smart ToolHiro System helps thousands of crypto users by helping them locate precise help and code snippets in seconds from an abundant overwhelming pool of information and resources.Read StoryLeading Asian BankThe large multinational bank can now give instant recommendations to its 12 million customer base on its card products and near instant insights to its large equity analyst team with high quality interpretation of chart and table filled research reportsRead StoryYour journey starts hereLet us personally show you how Alltius can help your product.Request a personal demoBuild intelligent, secure and accurate GenAI assistants for your customers, support and sales teams ProductsPlatform OverviewSales Enablement AssistEnd User AssistSupport Agent AssistChannelsWidgetSlackAPIsCompanyAboutCareersBlogOur valuesContactPrivacy PolicyTerms of Use© 2025 Alltius Inc