Ashish Nagar is the CEO and founding father of Stage AI, taking his expertise at Amazon on the Alexa staff to make use of synthetic intelligence to rework contact heart operations. With a robust background in expertise and entrepreneurship, Ashish has been instrumental in driving the corporate’s mission to boost the effectivity and effectiveness of customer support interactions by superior AI options. Below his management, Stage AI has change into a key participant within the AI-driven contact heart area, identified for its cutting-edge merchandise and superior implementation of synthetic intelligence.
What impressed you to go away Amazon and begin Stage AI? Are you able to share the precise ache factors in customer support that you just aimed to deal with together with your expertise?
My background is constructing merchandise on the intersection of expertise and enterprise. Though I’ve an undergrad diploma in Utilized Physics, my work has constantly targeted on product roles and establishing, launching, and constructing new companies. My ardour for expertise and enterprise led me to AI.
I began working in AI in 2014, after we have been constructing a next-generation cellular search firm known as Rel C, which was much like what Perplexity AI is at present. That have sparked my journey into AI software program, and finally, that firm was acquired by Amazon. At Amazon, I used to be a product chief on the Alexa staff, constantly in search of alternatives to sort out extra advanced AI issues.
In my final yr at Amazon, in 2018,I labored on a challenge we known as the “Star Trek computer,” impressed by the well-known sci-fi franchise. The purpose was to develop a pc that might perceive and reply to any query you requested it. This challenge turned generally known as the Alexa Prize, aiming to allow anybody to carry a 20-minute dialog with Alexa on any social matter. I led a staff of about 10 scientists, and we launched this as a worldwide AI problem. I labored carefully with main minds from establishments like MIT, CMU, Stanford, and Oxford. One factor turned clear: at the moment, nobody may absolutely remedy the issue.
Even then, I may sense a wave of innovation coming that will make this doable. Quick ahead to 2024, and applied sciences like ChatGPT are actually doing a lot of what we envisioned. There have been speedy developments in pure language processing with corporations like Amazon, Google, OpenAI, and Microsoft constructing massive fashions and the underlying infrastructure. However they weren’t essentially tackling end-to-end workflows. We acknowledged this hole and needed to deal with it.
Our first product wasn’t a customer support resolution; it was a voice assistant for frontline staff, reminiscent of technicians and retail retailer staff. We raised $2 million in seed funding and confirmed the product to potential prospects. They overwhelmingly requested that we adapt the expertise for contact facilities, the place they already had voice and knowledge streams however lacked the trendy generative AI structure. This led us to appreciate that current corporations on this area have been caught up to now, grappling with the traditional innovator’s dilemma of whether or not to overtake their legacy methods or construct one thing new. We began from a clean slate and constructed the primary native massive language mannequin (LLM) buyer expertise intelligence and repair automation platform.
My deep curiosity within the complexities of human language and the way difficult it’s to resolve these issues from a pc engineering perspective, performed a major function in our method. AI’s potential to grasp human speech is essential, notably for the contact heart {industry}. For instance, utilizing Siri usually reveals how troublesome it’s for AI to grasp intent and context in human language. Even easy queries can journey up AI, which struggles to interpret what you’re asking.
AI struggles with understanding intent, sustaining context over lengthy conversations, and possessing related data of the world. Even ChatGPT has limitations in these areas. As an illustration, it may not know the newest information or perceive shifting subjects inside a dialog. These challenges are immediately related to customer support, the place conversations usually contain a number of subjects and require the AI to grasp particular, domain-related data. We’re addressing these challenges in our platform, which is designed to deal with the complexities of human language in a customer support atmosphere.
Stage AI’s NLU expertise goes past fundamental key phrase matching. Are you able to clarify how your AI understands deeper buyer intent and the advantages this brings to customer support? How does Stage AI make sure the accuracy and reliability of its AI methods, particularly in understanding nuanced buyer interactions?
Now we have six or seven completely different AI pipelines tailor-made to particular duties, relying on the job at hand. For instance, one workflow would possibly contain figuring out name drivers and understanding the problems prospects have with a services or products, which we name the “voice of the customer.” One other might be the automated scoring of high quality scorecards to judge agent efficiency. Every workflow or service has its personal AI pipeline, however the underlying expertise stays the identical.
To attract an analogy, the expertise we use is predicated on LLMs much like the expertise behind ChatGPT and different generative AI instruments. Nonetheless, we use buyer service-specific LLMs that we’ve skilled in-house for these specialised workflows. This enables us to attain over 85% accuracy inside just some days of onboarding new prospects, leading to quicker time to worth, minimal skilled providers, and unmatched accuracy, safety, and belief.
Our fashions have deep, particular experience in customer support. The outdated paradigm concerned analyzing conversations by choosing out key phrases or phrases like “cancel my account” or “I’m not happy.” However our resolution doesn’t depend on capturing all doable variations of phrases. As an alternative, it applies AI to grasp the intent behind the query, making it a lot faster and extra environment friendly.
For instance, if somebody says, “I want to cancel my account,” there are numerous methods they could specific that, like “I’m done with you guys” or “I’m moving on to someone else.” Our AI understands the query’s intent and ties it again to the context, which is why our software program is quicker and extra correct.
A useful analogy is that outdated AI was like a rule ebook—you’d construct these inflexible rule books, with if-then-else statements, which have been rigid and continuously wanted upkeep. The brand new AI, then again, is sort of a dynamic mind or a studying system. With just some pointers, it dynamically learns context and intent, frequently bettering on the fly. A rule ebook has a restricted scope and breaks simply when one thing doesn’t match the predefined guidelines, whereas a dynamic studying system retains increasing, rising, and has a much wider affect.
An incredible instance from a buyer perspective is a big ecommerce model. They’ve hundreds of merchandise, and it’s unimaginable to maintain up with fixed updates. Our AI, nevertheless, can perceive the context, like whether or not you’re speaking a few particular sofa, without having to continuously replace a scorecard or rubric with each new product.
What are the important thing challenges in integrating Stage AI’s expertise with current customer support methods, and the way do you handle them?
Stage AI is a buyer expertise intelligence and repair automation platform. As such, we combine with most CX software program within the {industry}, whether or not it’s a CRM, CCaaS, survey, or tooling resolution. This makes us the central hub, gathering knowledge from all these sources and serving because the intelligence layer on prime.
Nonetheless, the problem is that a few of these methods are based mostly on non-cloud, on-premise expertise, and even cloud expertise that lacks APIs or clear knowledge integrations. We work carefully with our prospects to deal with this, although 80% of our integrations are actually cloud-based or API-native, permitting us to combine rapidly.
How does Stage AI present real-time intelligence and actionable insights for customer support brokers? Are you able to share some examples of how this has improved buyer interactions?
There are three sorts of real-time intelligence and actionable insights we offer our prospects:
- Automation of Guide Workflows: Service reps usually have restricted time (6 to 9 minutes) and a number of guide duties. Stage AI automates tedious duties like note-taking throughout and after conversations, producing custom-made summaries for every buyer. This has saved our prospects 10 to 25% in name dealing with time, resulting in extra effectivity.
- CX Copilot for Service Reps: Service reps face excessive churn and onboarding challenges. Think about being dropped right into a contact heart with out realizing the corporate’s insurance policies. Stage AI acts as an skilled AI sitting beside the rep, listening to conversations, and providing real-time steerage. This consists of dealing with objections, offering data, and providing good transcription. This functionality has helped our prospects onboard and prepare service reps 30 to 50% quicker.
- Supervisor Copilot: This distinctive characteristic offers managers real-time visibility into how their staff is performing. Stage AI supplies second-by-second insights into conversations, permitting managers to intervene, detect sentiment and intent, and assist reps in real-time. This has improved agent productiveness by 10 to fifteen% and elevated agent satisfaction, which is essential for lowering prices. For instance, if a buyer begins cursing at a rep, the system flags it, and the supervisor can both take over the decision or whisper steerage to the rep. This type of real-time intervention could be unimaginable with out this expertise.
Are you able to elaborate on how Stage AI’s sentiment evaluation works and the way it helps brokers reply extra successfully to prospects?
Our sentiment evaluation detects seven completely different feelings, starting from excessive frustration to elation, permitting us to measure various levels of feelings that contribute to our total sentiment rating. This evaluation considers each the spoken phrases and the tonality of the dialog. Nonetheless, we have discovered by our experiments that the spoken phrase performs a way more vital function than tone. You may say the meanest issues in a flat tone or very good issues in a wierd tone.
We offer a sentiment rating on a scale from 1 to 10, with 1 indicating very detrimental sentiment and 10 indicating a extremely optimistic sentiment. We analyze 100% of our prospects’ conversations, providing a deep perception into buyer interactions.
Contextual understanding can also be essential. For instance, if a name begins with very detrimental sentiment however ends positively, even when 80% of the decision was detrimental, the general interplay is taken into account optimistic. It is because the client began upset, the agent resolved the problem, and the client left happy. Alternatively, if the decision begins positively however ends negatively, that is a distinct story, even if 80% of the decision might need been optimistic.
This evaluation helps each the rep and the supervisor determine areas for coaching, specializing in actions that correlate with optimistic sentiment, reminiscent of greeting the client, acknowledging their considerations, and displaying empathy—components which can be essential to profitable interactions.
How does Stage AI handle knowledge privateness and safety considerations, particularly given the delicate nature of buyer interactions?
From day one, we’ve prioritized safety and privateness. We have constructed our system with enterprise-level safety and privateness as core ideas. We do not outsource any of our generative AI capabilities to third-party distributors. Every part is developed in-house, permitting us to coach customer-specific AI fashions with out sharing knowledge exterior our surroundings. We additionally provide intensive customization, enabling prospects to have their very own AI fashions with none knowledge sharing throughout completely different components of our knowledge pipeline.
To handle a present {industry} concern, our knowledge isn’t utilized by exterior fashions for coaching. We do not enable our fashions to be influenced by AI-generated knowledge from different sources. This method prevents the problems some AI fashions are going through, the place being skilled on AI-generated knowledge causes them to lose accuracy. At Stage AI, every part is first-party, and we do not share or pull knowledge externally.
With the latest $39.4 million Collection C funding, what are your plans for increasing Stage AI’s platform and reaching new buyer segments?
The Collection C funding will gasoline our strategic progress and innovation initiatives in essential areas, together with advancing product growth, engineering enhancements, and rigorous analysis and growth efforts. We purpose to recruit top-tier expertise throughout all ranges of the group, enabling us to proceed pioneering industry-leading applied sciences that surpass shopper expectations and meet dynamic market calls for.
How do you see the function of AI in reworking customer support over the following decade?
Whereas the overall focus is commonly on the automation side—predicting a future the place bots deal with all customer support—our view is extra nuanced. The extent of automation varies by vertical. For instance, in banking or finance, automation is likely to be decrease, whereas in different sectors, it might be greater. On common, we imagine that attaining greater than 40% automation throughout all verticals is difficult. It is because service reps do extra than simply reply questions—they act as troubleshooters, gross sales advisors, and extra, roles that may’t be absolutely replicated by AI.
There may be additionally vital potential in workflow automation, which Stage AI focuses on. This consists of back-office duties like high quality assurance, ticket triaging, and display screen monitoring. Right here, automation can exceed 80% utilizing generative AI. Intelligence and knowledge insights are essential. We’re distinctive in utilizing generative AI to realize insights from unstructured knowledge. This method can vastly enhance the standard of insights, lowering the necessity for skilled providers by 90% and accelerating time to worth by 90%.
One other essential consideration is whether or not the face of your group must be a bot or an individual. Past the fundamental features they carry out, a human connection together with your prospects is essential. Our method is to take away the surplus duties from an individual’s workload, permitting them to concentrate on significant interactions.
We imagine that people are greatest fitted to direct communication and will proceed to be in that function. Nonetheless, they’re not preferrred for duties like note-taking, transcribing interactions, or display screen recording. By dealing with these duties for them, we unencumber their time to interact with prospects extra successfully.
Thanks for the good interview, readers who want to be taught extra ought to go to Stage AI.
