Voice Operations Engineer - 24×7 Customer Support

How to choose an in-app AI agent platform for your banking app. An in-app AI agent platform embeds a journey-aware agent directly inside your banking app, unlike a conversational AI platform built for external chat and IVR. Ashutosh Prakash Singh Co-Founder & CEO at RevRag AI By 2026, 80% of enterprise applications shipped or updated embed at least one AI agent, up from 33% in 2024, according to Gartner's research on enterprise AI agent adoption. Banking and insurance lead every industry on this measure, with 47% of institutions already running at least one AI agent in production. The open question for most BFSI product leaders is no longer whether to add an agent. It is which kind of platform actually belongs inside a banking app, and which kind only looks like it does. What is an in-app AI agent platform for banking apps? An in-app AI agent platform is software that embeds an AI agent directly inside an existing banking, lending, or insurance app, so the agent can see the customer's live screen and journey state, take authorised actions inside that same app, and guide the customer through a task without redirecting them to a separate chat window, IVR, or website. This distinguishes it from a general-purpose conversational AI platform, which is typically built to power an external chatbot, voice IVR, or standalone messaging channel rather than to operate as a native layer inside a specific app's existing workflows. In-App AI agent platforms vs. Conversational AI Platforms: the real difference. Gartner's 2026 Magic Quadrant for Conversational AI Platforms names Google, Salesforce, SoundHound AI, and Kore.ai as Leaders, with Netomi and Boost.ai as Challengers. Every one of these is evaluated primarily on how well it powers chatbots, voice assistants, and contact-center automation, channels that sit outside the app itself. That is a different buying decision than choosing a platform to embed an agent inside a live banking app screen. The practical difference shows up in three places. First, context: a conversational AI platform typically starts a session from zero, working from what the customer types or says. An in-app agent platform starts with the customer's actual journey state, which screen they are on, what they already entered, and where they hesitated or failed. Second, action: conversational platforms are built to answer or route. In-app agent platforms are built to act, completing a KYC step, retrying a failed mandate, or submitting a document, inside the same session, using the app's own authenticated permissions. Third, surface: a conversational platform usually lives in a separate widget, tab, or number a customer has to go find. An in-app agent lives on the screen the customer already has open, at the exact moment they need it. Core capabilities to evaluate before you buy. A BFSI product or platform team evaluating vendors for this specific category should look past general chatbot benchmarks and score platforms on the capabilities that actually determine whether an agent works once it is live inside a regulated app. * Live journey-state awareness: can the agent see which screen the customer is on, what they have already attempted, and where they are stuck, rather than only responding to typed or spoken input. * In-session action execution: can the agent complete steps such as document re-upload, mandate retry, or eligibility checks itself, using the app's existing permissions, instead of only explaining what the customer should click next. * Multilingual coverage built for Indian BFSI: real support for the regional languages an institution's actual customer base uses, not just English and Hindi. * Compliance guardrails by design: built-in support for approval gates, escalation paths, and audit logging that map to RBI expectations, not guardrails bolted on after a pilot. * Data residency and security posture: where customer and transaction data is processed and stored, and whether that meets the institution's own data localisation requirements. * Latency inside the app UI: an agent that visibly lags while a customer is mid-task damages trust faster than a slow support call does, because the customer is already looking at the delay. * Integration depth with core banking, LOS, and policy admin systems: an agent that cannot read or write to the systems of record can only talk about a problem, not resolve it. Why BFSI product teams are moving past standalone chat widgets. A standalone FAQ chatbot answers a question the customer already knew how to type. It cannot see that the same customer just failed a mandate registration twice, and it cannot fix that failure itself, it can only tell the customer to try again or call support. That gap is why more BFSI product teams are now evaluating in-app agent platforms as a replacement for the chat widget entirely, not as an addition next to it. This is a specific, falsifiable claim: by the end of 2027, at least three of India's top ten private banks will have retired their in-app FAQ chatbot entirely, replacing it with a single embedded AI agent that handles full transactional journeys, document retries, mandate fixes, eligibility checks, rather than only answering questions and pointing customers elsewhere. Banking apps themselves are not going anywhere in this shift. The chat widget bolted onto the side of the app is what disappears, absorbed into an agent that lives on the screen instead of next to it. Compliance and governance: what RBI's FREE-AI framework means for platform selection. The Reserve Bank of India's Framework for Responsible and Ethical Enablement of AI (FREE-AI), released August 13, 2025, sets out seven guiding principles, trust as the foundation, people first, innovation over restraint, fairness and equity, accountability, understandable by design, and safety, resilience, and sustainability, for how banks, NBFCs, and payment players should build and deploy AI. A parallel RBI Model Risk Management guidance for AI systems moved to public consultation in 2026, and while it is not yet binding, several BFSI compliance teams are already treating its direction as the near-term bar for any AI system with customer-facing decision authority. For platform selection, this means a vendor evaluation cannot stop at demo quality. It has to answer whether the platform can produce an audit trail for every action the agent took inside the app, whether it supports a hard approval gate before financially material actions, and whether its escalation path hands off to a human agent with full context, not a customer repeating themselves from zero. A platform that cannot answer these questions concretely is not ready for a regulated in-app deployment, regardless of how capable its underlying model is. How RevRag AI approaches in-app agent platform selection. RevRag AI builds in-app AI agents purpose-built for Indian BFSI apps, designed around journey-state awareness and in-session action execution rather than a general-purpose chat layer retrofitted into a banking app. The starting design question is never "how do we add a chatbot to this app." It is "what is the customer stuck on right now, on this exact screen, and what can the agent do about it without sending them somewhere else." Frequently asked questions about in-app AI agent platforms for banking apps. What is the difference between an in-app AI agent and a chatbot? A chatbot answers questions a customer types or asks, usually in a separate widget disconnected from the customer's actual journey state. An in-app AI agent sees which screen the customer is on, what they have already tried, and can take authorised actions inside that same session, such as retrying a failed step, rather than only explaining what to do next. Do banks need a separate platform for in-app AI agents versus chatbots? Not necessarily a separate vendor relationship, but a separate evaluation. Gartner's Conversational AI Platform Leaders (Google, Salesforce, SoundHound AI, Kore.ai) are scored on chatbot and IVR capability. Evaluating the same or a different vendor specifically for in-app journey awareness, in-session action execution, and embedded compliance guardrails is a distinct exercise BFSI teams should run before buying. Is RBI's FREE-AI framework mandatory for in-app AI agent platforms? The FREE-AI framework itself, released in August 2025, sets principles rather than binding rules, but several of its provisions are expected to be incorporated into RBI Master Directions, and the related 2026 Model Risk Management guidance has already moved to public consultation. BFSI institutions choosing an in-app agent platform today should treat its direction as the practical compliance bar, not wait for it to become mandatory. Will in-app AI agents replace banking apps entirely? No. In-app AI agents operate inside the app the customer already has open, using its existing authenticated session and permissions. The shift is in what apps' interface layers become, an agent guiding and acting on the customer's behalf, not in whether apps themselves continue to exist. Core banking systems and the banking apps that front them are not going away. What should a BFSI product team ask a vendor before buying an in-app AI agent platform? Ask for a concrete audit trail example of an action the agent took inside a live app, a description of its approval-gate design for financially material actions, and evidence of multilingual coverage for the institution's actual customer languages, not a generic capability slide. A vendor that cannot answer with a specific, working example is not ready for a regulated deployment. See RevRag AI in Action. Book a demo and see how agentic AI can transform your BFSI customer journeys.

AI voice agents for customer support: what they do and when to use them. Persistence Team · August 26, 2026 On this page Key takeaways * AI voice agents can answer calls 24/7, resolve common issues, and hand off complex cases to humans. * Most vendor comparisons in 2026 name Retell AI, PolyAI, Cognigy, Sierra, Decagon, and Ada among the leading options. * Cost and setup vary a lot by vendor, call volume, and how deeply the agent integrates with existing support tools. * The right fit depends on your call types: high-volume, repetitive requests are better candidates than complex or emotionally sensitive calls. * Evaluate by testing real call scripts, not demos, and by checking how the agent handles interruptions and edge cases. What is an AI voice agent for customer support? An AI voice agent is software that answers phone calls, understands what a caller wants, and responds in natural speech. It can look up account details, answer common questions, take actions like rescheduling or refunding, and route the call to a human when needed. Vendors like Salesforce describe this as giving customers 24/7 access to quick, automated resolution over the phone (salesforce.com). ElevenLabs frames the same idea as agents that qualify leads, book meetings, and resolve support issues in real time with human-like conversation (elevenlabs.io). Should you use AI voice agents for customer support? The honest answer: it depends on your call mix. If a large share of your calls are repetitive - order status, password resets, appointment changes, basic troubleshooting - a voice agent can resolve many of them without a human ever picking up. NiCE describes this pattern as voice agents independently resolving common issues or collaborating with human agents on harder ones (nice.com). If your calls are mostly complex, high-stakes, or emotionally charged, a voice agent should route to a human quickly rather than try to handle everything. The technology works best as a first responder and escalation filter, not a full replacement for support staff on every call type. How are AI agents used in customer service today? Independent comparisons from 2026 group vendors by what they're best at. Retell AI's own review lists Retell AI, PolyAI, Cognigy, CloudTalk, Lindy AI, and Synthflow AI as top picks for voice-specific support use cases (retellai.com). Kore.ai's buyer's guide names Kore.ai, Zendesk, Cognigy, Omilia, SoundHound, Sierra AI, and Yellow.ai as leaders across the broader customer service AI agent category (kore.ai). Assembled's review focuses specifically on voice and lists Assembled, Cresta, Sierra, PolyAI, Decagon, Regal, Ada, and Forethought (assembled.com). Botpress and Fin AI both cover text-first and omnichannel agents including Ada, Crescendo.ai, Fin, Zendesk, and Tidio (botpress.com, fin.ai). The overlap across these lists - PolyAI, Cognigy, Sierra, Ada, Fin - is a reasonable starting shortlist if you're comparing options. How to create an AI agent for customer service. Across the vendor guides, the setup pattern is similar: define the specific call types the agent should handle, connect it to your existing knowledge base or support system so it has accurate answers, write escalation rules for when it should transfer to a human, and test with real call transcripts before going live. A Reddit discussion among practitioners on r/AI_Agents notes that voice quality (tools like ElevenLabs) and reasoning quality (models like Gemini) are now both strong, but getting reliable end-to-end behavior in production still takes real testing, not just a demo (reddit.com). Treat the first few weeks after launch as a tuning period - listen to real call recordings and adjust prompts, routing, and fallback behavior based on what actually happens on the phone. How much does an AI voice calling agent cost? None of the sources in this research provide a single standard price, because cost depends heavily on vendor, call volume, and integration depth. What's consistent across the guides is that pricing is usually usage-based (per minute or per call) rather than a flat fee, and that costs scale with call volume and the complexity of integrations required. If cost matters most to your decision, ask each vendor directly for a quote based on your expected call volume and required integrations rather than relying on published list prices, since these guides do not publish comparable numbers. What to check before choosing a vendor. The comparison guides suggest a few consistent evaluation points: how the agent handles interruptions and mid-sentence changes of mind, how accurately it retrieves information from your existing systems, how clearly it knows when to escalate to a human, and how it performs on your actual call scripts rather than a generic demo script. Testing with real, messy calls - not scripted happy-path examples - is the theme that recurs across the Retell AI, Kore.ai, and Assembled reviews (retellai.com, kore.ai, assembled.com). Related resources.

Kore.ai named Leader across five Enterprise AI evaluations by Gartner, Forrester, Everest Group. Kore.ai has been named a Leader across five Gartner, Forrester and Everest Group evaluations covering agentic AI, conversational AI, customer service, employee services and enterprise search. 21 Aug 2026 15:55 IST Enterprise AI platform provider Kore.ai has been named a Leader across five recent evaluations by Gartner, Forrester and Everest Group, covering agentic AI, conversational AI, customer service, employee services and enterprise search. The recognitions include a Leader position in the Gartner Magic Quadrant for Conversational AI Platforms, the Forrester Wave for Conversational AI Platforms for Employee Services, the Forrester Wave for Conversational AI Platforms for Customer Service, the Forrester Wave for Cognitive Search Platforms, and the Everest Group Agentic AI Products PEAK Matrix Assessment 2026. The Gartner recognition marks Kore.ai's fourth consecutive Leader placement in its conversational AI platforms Magic Quadrant, covering every edition since the report was launched in 2022. The company said the latest evaluations reflect the broader shift in enterprise AI from scripted chatbots to generative AI and increasingly autonomous agents. The analyst assessments also highlighted different aspects of Kore.ai's platform. Gartner cited its research and development staffing and identified Arch and Agent Blueprint Language (ABL) as distinctive tools. Forrester noted ABL's role in agent predictability, guardrail enforcement and measuring agent impact, while Everest Group highlighted agent lifecycle management, model-agnostic architecture and audit trails covering prompts, outputs and decisions. "Enterprise AI is entering its third wave, where governance, observability, and trust define success at scale," said Raj Koneru, Founder and CEO, Kore.ai. He said enterprises scaling AI will need platforms that can build, govern and improve AI systems on a common layer. Kore.ai also said enterprise AI purchasing is increasingly moving from individual departments to the CIO level, with buyers looking for platforms that cover the broader lifecycle of AI agents rather than individual AI tools. "Three independent firms scored five different categories and reached one conclusion," said Peter Mullen, Chief Marketing Officer, Kore.ai, adding that enterprises are increasingly selecting the platform layer that will govern and operate their AI systems. The recognitions come as Kore.ai expands its newer Artemis edition of the Kore.ai Agent Platform. The company said its platform supports more than 500 large enterprises and handles over 20 billion interactions annually.