Blog

Anurag Jain, Co-Founder Oriserve
 

Anurag is a tech strategist and entrepreneur passionate about turning cutting-edge ideas into impactful solutions. Driven by a mission to empower businesses through technology, over the past 12 years, he has worn many hats including product manager, AI advocate, and startup scaler.

When I first started working with banks and insurers, personalization meant a birthday offer in the mail or the occasional “preferred customer” badge. Today, customers expect something far more intimate: financial products and advice that feel like they were crafted for their life — not for a demographic bucket. As a technology leader who has spent years helping BFSI teams move from campaign-driven outreach to continuous customer relationships, I’ve come to believe that AI is the tool that makes that individuality possible. But it only works when it’s used with humility, purpose and human judgment.

Also Read: NHAI, Reliance Jio Join Hands for Highway Safety Alert System

Personalization in BFSI isn’t about flashy demos. It’s about small, meaningful moments: a timely nudge to start a retirement fund after a salary hike, a simplified claim process that senses frustration and routes the case to a human earlier, or an insurance offer that adjusts for the moments that actually matter in someone’s life—marriage, a new home, a parent’s illness. Those are the outcomes that build trust and long-term value.

Also Read: Amazon and Google Launch Multicloud Service

The promise is real. AI enables banks and insurers to stitch together transaction history, product interactions, customer service notes and external signals to create a living profile of a customer’s needs and intentions. Predictive analytics can surface the “next best action” — but only if the organization can act on it across channels. The technology’s value isn’t a recommendation engine on a shelf; it’s an orchestration layer that ensures the right human or algorithm meets the customer at the right time, on the right device, with the right tone.

Yet the path to this future is littered with practical challenges.
First: data. Many institutions have a wealth of information locked in silos — legacy core banking systems, separate insurance policy databases, CRM notes in another system. Personalization demands a unified view. That’s not just a technical integration problem; it’s an organizational one. Teams must agree on definitions, on the single source of truth, and on governance.

Second: trust and privacy. Customers rightly worry about how their data is used. Personalization that feels intrusive — a product suggestion that references a sensitive life event you didn’t explicitly share — erodes loyalty. Responsible personalization starts with consent, clear communication and giving customers control over what they share and how it’s used.

Organizations that combine thoughtful technology with real human care will win not by out-automating others, but by building more trusting, resilient relationships.

Third: fairness and explainability. When an AI model recommends credit limits or suggests premiums, the “why” matters. Regulators and customers expect decisions to be explainable and contestable. That means we must invest in models that are interpretable, maintain audit trails, and embed human oversight into the loop.

Fourth: culture and change management. Deploying a personalization engine is not a one-week project. It means reshaping go-to-market playbooks, reworking KPIs, and retraining relationship managers and call center teams to act on AI insights without losing empathy.

Also Read: Government Launches Cyber Security Innovation Challenge 1.0

Despite the challenges, breakthroughs are piling up. Conversational AI has matured: not only can voicebots answer FAQs, but they can hold context-rich conversations across channels and escalate to humans when nuance or emotion appears. Predictive models are being used to detect fraud early, reduce churn, and tailor retention offers that feel genuinely helpful rather than transactional. Generative AI — used carefully — is helping craft personalized, plain-English explanations of complex financial terms, making products more accessible.

But here’s the point I return to with every client: the most successful efforts pair AI with human judgment. AI surfaces likely needs and risks; people turn that into relationships. I remember a pilot where the model flagged a small business customer as at risk because of irregular cash flows. Instead of immediately reducing credit, the relationship manager reached out and discovered a one-off supplier dispute. The bank restructured timing, avoided a default, and the client’s loyalty strengthened. That human intervention, informed by AI, delivered outcomes a purely automated decision could not.

For BFSI leaders the roadmap is straightforward, if not easy: start with one high-impact journey — mortgage origination, small business lending, claims resolution — and personalize it end-to-end. Build data foundations and governance in parallel. Measure beyond revenue: track customer trust, time-to-resolution, and the reduction in unnecessary friction. Most importantly, codify human oversight and clear escalation paths so the model’s mistakes don’t become customer crises.

Finally, personalization at scale must be responsible. That means transparency to customers, robust privacy controls, continuous monitoring for bias, and cross-functional teams that include legal, compliance and front-line staff. When those safeguards are in place, AI becomes more than a recommendation engine — it becomes a partner that helps people make better financial choices.

Personalization isn’t an end in itself. It’s a promise: that financial services will feel more useful, less opaque, and more human. Organizations that combine thoughtful technology with real human care will win not by out-automating others, but by building more trusting, resilient relationships. That’s the quiet revolution AI can enable in banking and insurance — but only if we accept that technology must serve human judgement, not replace it.

Read the full article covered by CIO Insider.

Anurag Jain, Co-Founder Oriserve
 

Anurag is a tech strategist and entrepreneur passionate about turning cutting-edge ideas into impactful solutions. Driven by a mission to empower businesses through technology, over the past 12 years, he has worn many hats including product manager, AI advocate, and startup scaler.

When I first started working with banks and insurers, personalization meant a birthday offer in the mail or the occasional “preferred customer” badge. Today, customers expect something far more intimate: financial products and advice that feel like they were crafted for their life — not for a demographic bucket. As a technology leader who has spent years helping BFSI teams move from campaign-driven outreach to continuous customer relationships, I’ve come to believe that AI is the tool that makes that individuality possible. But it only works when it’s used with humility, purpose and human judgment.

Also Read: NHAI, Reliance Jio Join Hands for Highway Safety Alert System

Personalization in BFSI isn’t about flashy demos. It’s about small, meaningful moments: a timely nudge to start a retirement fund after a salary hike, a simplified claim process that senses frustration and routes the case to a human earlier, or an insurance offer that adjusts for the moments that actually matter in someone’s life—marriage, a new home, a parent’s illness. Those are the outcomes that build trust and long-term value.

Also Read: Amazon and Google Launch Multicloud Service

The promise is real. AI enables banks and insurers to stitch together transaction history, product interactions, customer service notes and external signals to create a living profile of a customer’s needs and intentions. Predictive analytics can surface the “next best action” — but only if the organization can act on it across channels. The technology’s value isn’t a recommendation engine on a shelf; it’s an orchestration layer that ensures the right human or algorithm meets the customer at the right time, on the right device, with the right tone.

Yet the path to this future is littered with practical challenges.
First: data. Many institutions have a wealth of information locked in silos — legacy core banking systems, separate insurance policy databases, CRM notes in another system. Personalization demands a unified view. That’s not just a technical integration problem; it’s an organizational one. Teams must agree on definitions, on the single source of truth, and on governance.

Second: trust and privacy. Customers rightly worry about how their data is used. Personalization that feels intrusive — a product suggestion that references a sensitive life event you didn’t explicitly share — erodes loyalty. Responsible personalization starts with consent, clear communication and giving customers control over what they share and how it’s used.

Organizations that combine thoughtful technology with real human care will win not by out-automating others, but by building more trusting, resilient relationships.

Third: fairness and explainability. When an AI model recommends credit limits or suggests premiums, the “why” matters. Regulators and customers expect decisions to be explainable and contestable. That means we must invest in models that are interpretable, maintain audit trails, and embed human oversight into the loop.

Fourth: culture and change management. Deploying a personalization engine is not a one-week project. It means reshaping go-to-market playbooks, reworking KPIs, and retraining relationship managers and call center teams to act on AI insights without losing empathy.

Also Read: Government Launches Cyber Security Innovation Challenge 1.0

Despite the challenges, breakthroughs are piling up. Conversational AI has matured: not only can voicebots answer FAQs, but they can hold context-rich conversations across channels and escalate to humans when nuance or emotion appears. Predictive models are being used to detect fraud early, reduce churn, and tailor retention offers that feel genuinely helpful rather than transactional. Generative AI — used carefully — is helping craft personalized, plain-English explanations of complex financial terms, making products more accessible.

But here’s the point I return to with every client: the most successful efforts pair AI with human judgment. AI surfaces likely needs and risks; people turn that into relationships. I remember a pilot where the model flagged a small business customer as at risk because of irregular cash flows. Instead of immediately reducing credit, the relationship manager reached out and discovered a one-off supplier dispute. The bank restructured timing, avoided a default, and the client’s loyalty strengthened. That human intervention, informed by AI, delivered outcomes a purely automated decision could not.

For BFSI leaders the roadmap is straightforward, if not easy: start with one high-impact journey — mortgage origination, small business lending, claims resolution — and personalize it end-to-end. Build data foundations and governance in parallel. Measure beyond revenue: track customer trust, time-to-resolution, and the reduction in unnecessary friction. Most importantly, codify human oversight and clear escalation paths so the model’s mistakes don’t become customer crises.

Finally, personalization at scale must be responsible. That means transparency to customers, robust privacy controls, continuous monitoring for bias, and cross-functional teams that include legal, compliance and front-line staff. When those safeguards are in place, AI becomes more than a recommendation engine — it becomes a partner that helps people make better financial choices.

Personalization isn’t an end in itself. It’s a promise: that financial services will feel more useful, less opaque, and more human. Organizations that combine thoughtful technology with real human care will win not by out-automating others, but by building more trusting, resilient relationships. That’s the quiet revolution AI can enable in banking and insurance — but only if we accept that technology must serve human judgement, not replace it.

Read the full article covered by CIO Insider.

Oriserve has open-sourced Whisper–Hindi2Hinglish-Apex, a fine-tuned ASR model built for Hindi, Hinglish and Indian-accented English, improving accuracy on real-world, code-mixed and noisy audio.

The launch targets a critical gap in AI speech systems for India, where global ASR models typically show accuracy drops on code-mixed speech, strong regional accents and noisy telephonic audio

Oriserve has released Whisper–Hindi2Hinglish-Apex, an open-source automatic speech recognition (ASR) model fine-tuned on OpenAI’s Whisper and adapted for Hindi, Hinglish and Indian-accented English. The model is now available on Hugging Face.

The launch targets a critical gap in AI speech systems for India, where global ASR models typically show accuracy drops on code-mixed speech, strong regional accents and noisy telephonic audio. While Whisper remains a widely used multilingual ASR model, its performance declines on non-standardised and hybrid Indian datasets.

Whisper–Hindi2Hinglish-Apex retains Whisper’s architecture but is trained on more than 1,000 hours of conversational Indian audio, including call-centre recordings and mixed Hindi–English speech. The fine-tuning is intended to improve accuracy in enterprise conditions, where accent diversity and low-quality audio are common.

The model contains about 800 million parameters. Oriserve says it offers:

•faster inference than larger Whisper variants,

•a 42% improvement over Whisper’s baseline on internal benchmarks, and

•stronger handling of accented, hybrid and noisy audio.

“India needs speech models trained on its own linguistic data, not just adapted global datasets,” said Anurag Jain, co-founder, Oriserve. “Open-sourcing this model enables developers to build AI systems aligned with real Indian audio environments.”

Co-founder Maaz Ansari said the effort is aimed at reducing dependence on proprietary cloud ASR systems and enabling on-premise or hybrid deployments across sectors such as BFSI, telecom, healthcare and education.

This is Oriserve’s third release in its open-source AI series. The company plans to extend fine-tuned Whisper variants to Marathi, Gujarati, Tamil, Telugu, Kannada, Malayalam, Bengali and Punjabi as part of its larger multilingual AI roadmap.

Read the full article covered by Financial Express.

Oriserve has open-sourced Whisper–Hindi2Hinglish-Apex, a fine-tuned ASR model built for Hindi, Hinglish and Indian-accented English, improving accuracy on real-world, code-mixed and noisy audio.

The launch targets a critical gap in AI speech systems for India, where global ASR models typically show accuracy drops on code-mixed speech, strong regional accents and noisy telephonic audio

Oriserve has released Whisper–Hindi2Hinglish-Apex, an open-source automatic speech recognition (ASR) model fine-tuned on OpenAI’s Whisper and adapted for Hindi, Hinglish and Indian-accented English. The model is now available on Hugging Face.

The launch targets a critical gap in AI speech systems for India, where global ASR models typically show accuracy drops on code-mixed speech, strong regional accents and noisy telephonic audio. While Whisper remains a widely used multilingual ASR model, its performance declines on non-standardised and hybrid Indian datasets.

Whisper–Hindi2Hinglish-Apex retains Whisper’s architecture but is trained on more than 1,000 hours of conversational Indian audio, including call-centre recordings and mixed Hindi–English speech. The fine-tuning is intended to improve accuracy in enterprise conditions, where accent diversity and low-quality audio are common.

The model contains about 800 million parameters. Oriserve says it offers:

•faster inference than larger Whisper variants,

•a 42% improvement over Whisper’s baseline on internal benchmarks, and

•stronger handling of accented, hybrid and noisy audio.

“India needs speech models trained on its own linguistic data, not just adapted global datasets,” said Anurag Jain, co-founder, Oriserve. “Open-sourcing this model enables developers to build AI systems aligned with real Indian audio environments.”

Co-founder Maaz Ansari said the effort is aimed at reducing dependence on proprietary cloud ASR systems and enabling on-premise or hybrid deployments across sectors such as BFSI, telecom, healthcare and education.

This is Oriserve’s third release in its open-source AI series. The company plans to extend fine-tuned Whisper variants to Marathi, Gujarati, Tamil, Telugu, Kannada, Malayalam, Bengali and Punjabi as part of its larger multilingual AI roadmap.

Read the full article covered by Financial Express.

How Trust Is Redefining Enterprise AI Adoption, Oriserve’s Anurag Jain Shares Expert Insights

Artificial Intelligence is becoming a transformative force in the business world, with predictions suggesting it will add $15 trillion to global GDP by 2030. Many believe the country leading in AI will dominate the next generation. However, businesses face the challenge of balancing intelligence with trust as AI continues to evolve.

 In the latest episode of the Analytics Insight Podcast, host Priya Dialani speaks with Anurag Jain, Co-Founder of Oriserve, about how businesses are reconsidering their approach to AI, focusing on performance, security, and reliability rather than just innovation.

Why Trust Defines the Next Phase of Enterprise AI

Priya begins the conversation by questioning a popular concept: success is driven solely by the smartest algorithms. “In the enterprise world, intelligence only matters if it’s trusted,” she states. Companies today are not simply buying AI tools; they are also investing in systems that “run operations, shape customer experiences, and influence billion-dollar decisions.”

 Anurag agrees that while AI is a priority in the boardroom, it’s the trustworthiness of performance that separates the successful from the unsuccessful. “There’s a lot of hype around AI and everyone wants to adopt it,” he says. “But enterprise buyers look for real impact, proof that AI delivers ROI.”

 According to Anurag, the Oriserve team is so committed to scaling up trust, they will often deploy the AI solutions on a small base – usually anywhere from 5% – and continue to scale up as accuracy and results improve. “Trust comes by building that impact early on and creating continuous learning loops,” Jain adds.

How Oriserve Is Enabling Reliable AI Transformation

Oriserve is a deep-tech conversational AI company that drives Automation of 70-80% of enterprise conversations in the eight multicultural response areas. “We drive human-like conversations at scale that are aware of goals – whether it’s lead qualification or collection recovery,” he explains.

 The Oriserve Co-Founder’s AI journey began in its early days. After graduating from IIT Kharagpur in 2011, Jain joined Fractal Analytics, focusing on predictive models, before the concept of generative AI was available. About that experience, he says, “it built my passion for how AI can impact real-life scenarios.”

Building Trust Through Compliance and Partnership

In addition to performance, compliance and security continue to be leading concerns of enterprise adoption. Anurag highlights, “We build AI observability stacks that give enterprise buyers confidence that the solution is compliant and secure.”

Trust, as he explains, is not created in a single transaction; it is built by working together over time. “It’s not just a one-time sale; it’s a continuous partnership where we keep building further so that the AI becomes better over time.”

Read the full article published by Analytics Insight and also listen to the full interview here.

How Trust Is Redefining Enterprise AI Adoption, Oriserve’s Anurag Jain Shares Expert Insights

Artificial Intelligence is becoming a transformative force in the business world, with predictions suggesting it will add $15 trillion to global GDP by 2030. Many believe the country leading in AI will dominate the next generation. However, businesses face the challenge of balancing intelligence with trust as AI continues to evolve.

 In the latest episode of the Analytics Insight Podcast, host Priya Dialani speaks with Anurag Jain, Co-Founder of Oriserve, about how businesses are reconsidering their approach to AI, focusing on performance, security, and reliability rather than just innovation.

Why Trust Defines the Next Phase of Enterprise AI

Priya begins the conversation by questioning a popular concept: success is driven solely by the smartest algorithms. “In the enterprise world, intelligence only matters if it’s trusted,” she states. Companies today are not simply buying AI tools; they are also investing in systems that “run operations, shape customer experiences, and influence billion-dollar decisions.”

 Anurag agrees that while AI is a priority in the boardroom, it’s the trustworthiness of performance that separates the successful from the unsuccessful. “There’s a lot of hype around AI and everyone wants to adopt it,” he says. “But enterprise buyers look for real impact, proof that AI delivers ROI.”

 According to Anurag, the Oriserve team is so committed to scaling up trust, they will often deploy the AI solutions on a small base – usually anywhere from 5% – and continue to scale up as accuracy and results improve. “Trust comes by building that impact early on and creating continuous learning loops,” Jain adds.

How Oriserve Is Enabling Reliable AI Transformation

Oriserve is a deep-tech conversational AI company that drives Automation of 70-80% of enterprise conversations in the eight multicultural response areas. “We drive human-like conversations at scale that are aware of goals – whether it’s lead qualification or collection recovery,” he explains.

 The Oriserve Co-Founder’s AI journey began in its early days. After graduating from IIT Kharagpur in 2011, Jain joined Fractal Analytics, focusing on predictive models, before the concept of generative AI was available. About that experience, he says, “it built my passion for how AI can impact real-life scenarios.”

Building Trust Through Compliance and Partnership

In addition to performance, compliance and security continue to be leading concerns of enterprise adoption. Anurag highlights, “We build AI observability stacks that give enterprise buyers confidence that the solution is compliant and secure.”

Trust, as he explains, is not created in a single transaction; it is built by working together over time. “It’s not just a one-time sale; it’s a continuous partnership where we keep building further so that the AI becomes better over time.”

Read the full article published by Analytics Insight and also listen to the full interview here.

An Exclusive Interview with Maaz Ansari, Co-Founder of Oriserve (Ori), a next-gen Generative AI platform

Maaz Ansari, Co-Founder of Oriserve (Ori), is at the forefront of redefining enterprise communication with a next-gen Generative AI platform.

In this interview, he shares Ori’s journey, its mission to transform customer engagement, and how AI-driven innovation is bridging the gap between technology and human-like interactions to empower businesses across industries.

Can you tell us about your journey and what inspired you to co-found Oriserve (Ori)?

Maaz Ansari: My journey has always revolved around building things that genuinely matter, products that make businesses more customer-centric, resilient, and future-ready.

Coming from a background in data science and product leadership, I saw first-hand how fragmented, impersonal, and inefficient most customer engagement still was, especially in highly regulated, competitive sectors like BFSI and telecom.

It wasn’t just a technology gap; it was an empathy gap. We founded ORI to close that gap: to reinvent how enterprises drive revenue and retention with AI that feels as natural, insightful, and trustworthy as your best human agent, but operates with the precision and scale modern business demands.

The spark was realizing just how much potential was untapped in every customer conversation, and how the right AI could turn those moments into measurable business impact.

Could you explain what Ori does and how its conversational AI solutions are transforming customer engagement?

Maaz Ansari: ORI is more than a platform, it’s a revenue engine for leading BFSI and telecom businesses. Our purpose-built GenAI and Voice AI tech automate revenue operations across lead qualification, collections, renewals, and upsell, at the scale and compliance required by banks, insurers, NBFCs, and telcos.

Unlike generic voice AI tools, ORI’s multilingual voice agents and speech analytics are tailored for regulated workflows and real-time insights.

For example, our solution drives up to 30% cost reduction and up to 15% better collections, while improving NPS and customer loyalty by making every interaction fast, hyper-personal, and regulatory-compliant.

It’s not about replacing humans; it’s about empowering teams to focus on high-value work while AI handles the rest, 24/7, across every touchpoint.

How does Ori differentiate itself from other AI-based engagement platforms?

Maaz Ansari: At ORI, we aren’t just another automation vendor, we’re trusted by BFSI leaders precisely because our conversational AI is built for human-centric, high-stakes CX.

While competitors promise “AI automation,” we deliver measurable ROI by aligning every touchpoint with regulatory needs, multilingual support, and domain-tuned empathy.

Our proprietary algorithm stack goes beyond intent-matching to infuse every conversation with context awareness and emotional intelligence.

We also stand out through our unmatched production-readiness: 90% pilot-to-deployment success, whereas most AI pilots fail to scale, thanks to continuous learning, flexible integrations (web, mobile, IoT, contact center, CRM), and full stack observability.

Our agents handle 1.2b+ conversations, support 50+ languages, and refine their performance in real time, turning compliance, accuracy, and user trust into a growth engine.

What has been the biggest technological challenge Ori has faced, and how did you overcome it?

Maaz Ansari: Our greatest challenge, and biggest win, has been making truly human-like, compliant voice AI for India’s rich regional language landscape. Handling layered expressions, code-switching, and emotion nuances in regulated BFSI calls wasn’t solved by off-the-shelf LLMs.

We built proprietary models and custom data pipelines to ensure our voice AI not only understands but responds naturally, accurately, and in full compliance with evolving regulations.

This required relentless iteration, partnership with clients, and pushing the limits of speech analytics and continuous learning in live production.

How have partnerships and collaborations shaped Ori’s growth journey?

Maaz Ansari: Openness to collaboration is at the heart of ORI. We grew by listening closely to BFSI, telecom, and enterprise leaders, and co-creating solutions that tackle industry-specific pain points.

Our partnerships with marquee brands like Bajaj, Vi, and Maruti Suzuki did more than expand our reach; they kept us honest to the pace, rigor, and outcomes these sectors demand.

For instance, through these collaborations, clients have seen more than 18% increase in collections efficiency and crores in operational savings. Our partner ecosystem extends to BPOs, system integrators, and tech alliances, ensuring we stay ahead on compliance, feature roadmaps, and go-to-market innovation.

Where do you see Ori in the next five years, both in India and globally?

Maaz Ansari: In five years, the ORI platform won’t just be present across India’s BFSI, telecom, and emerging enterprise sectors, it’ll be synonymous with production-grade AI customer engagement globally. With teams already on the ground in several locations, we’re scaling to deliver localized compliance, language, and cultural empathy everywhere.

Our aim is to make ORI the go-to copilot for growth, cost-efficiency, and regulatory trust in every high-stakes industry worldwide, not just BFSI.

What advice would you give to young entrepreneurs seeking to build impact-driven tech startups?

Maaz Ansari: Start deep, not wide. Find the one industry pain point where your technology can make an unmissable difference, and prove it through results, not hype. Don’t fear regulated or “tough” sectors; that’s where trust, learning, and real business value are built.

Listen fiercely to your users, iterate relentlessly, and build for outcomes, not vanity metrics. Collaboration and resilience matter more than lone genius, embrace both. And above all, know that genuine impact takes patience, grit, and the humility to keep learning as your startup grows.

From vision to execution, Maaz Ansari’s story reflects Ori’s commitment to shaping the future of generative AI.

His insights reveal not only the potential of AI in driving business transformation but also the determination behind Ori’s growth.

As enterprises embrace intelligent automation, Ori stands as a pioneering force, led by Maaz’s forward-looking leadership.

Read the full article here.

An Exclusive Interview with Maaz Ansari, Co-Founder of Oriserve (Ori), a next-gen Generative AI platform

Maaz Ansari, Co-Founder of Oriserve (Ori), is at the forefront of redefining enterprise communication with a next-gen Generative AI platform.

In this interview, he shares Ori’s journey, its mission to transform customer engagement, and how AI-driven innovation is bridging the gap between technology and human-like interactions to empower businesses across industries.

Can you tell us about your journey and what inspired you to co-found Oriserve (Ori)?

Maaz Ansari: My journey has always revolved around building things that genuinely matter, products that make businesses more customer-centric, resilient, and future-ready.

Coming from a background in data science and product leadership, I saw first-hand how fragmented, impersonal, and inefficient most customer engagement still was, especially in highly regulated, competitive sectors like BFSI and telecom.

It wasn’t just a technology gap; it was an empathy gap. We founded ORI to close that gap: to reinvent how enterprises drive revenue and retention with AI that feels as natural, insightful, and trustworthy as your best human agent, but operates with the precision and scale modern business demands.

The spark was realizing just how much potential was untapped in every customer conversation, and how the right AI could turn those moments into measurable business impact.

Could you explain what Ori does and how its conversational AI solutions are transforming customer engagement?

Maaz Ansari: ORI is more than a platform, it’s a revenue engine for leading BFSI and telecom businesses. Our purpose-built GenAI and Voice AI tech automate revenue operations across lead qualification, collections, renewals, and upsell, at the scale and compliance required by banks, insurers, NBFCs, and telcos.

Unlike generic voice AI tools, ORI’s multilingual voice agents and speech analytics are tailored for regulated workflows and real-time insights.

For example, our solution drives up to 30% cost reduction and up to 15% better collections, while improving NPS and customer loyalty by making every interaction fast, hyper-personal, and regulatory-compliant.

It’s not about replacing humans; it’s about empowering teams to focus on high-value work while AI handles the rest, 24/7, across every touchpoint.

How does Ori differentiate itself from other AI-based engagement platforms?

Maaz Ansari: At ORI, we aren’t just another automation vendor, we’re trusted by BFSI leaders precisely because our conversational AI is built for human-centric, high-stakes CX.

While competitors promise “AI automation,” we deliver measurable ROI by aligning every touchpoint with regulatory needs, multilingual support, and domain-tuned empathy.

Our proprietary algorithm stack goes beyond intent-matching to infuse every conversation with context awareness and emotional intelligence.

We also stand out through our unmatched production-readiness: 90% pilot-to-deployment success, whereas most AI pilots fail to scale, thanks to continuous learning, flexible integrations (web, mobile, IoT, contact center, CRM), and full stack observability.

Our agents handle 1.2b+ conversations, support 50+ languages, and refine their performance in real time, turning compliance, accuracy, and user trust into a growth engine.

What has been the biggest technological challenge Ori has faced, and how did you overcome it?

Maaz Ansari: Our greatest challenge, and biggest win, has been making truly human-like, compliant voice AI for India’s rich regional language landscape. Handling layered expressions, code-switching, and emotion nuances in regulated BFSI calls wasn’t solved by off-the-shelf LLMs.

We built proprietary models and custom data pipelines to ensure our voice AI not only understands but responds naturally, accurately, and in full compliance with evolving regulations.

This required relentless iteration, partnership with clients, and pushing the limits of speech analytics and continuous learning in live production.

How have partnerships and collaborations shaped Ori’s growth journey?

Maaz Ansari: Openness to collaboration is at the heart of ORI. We grew by listening closely to BFSI, telecom, and enterprise leaders, and co-creating solutions that tackle industry-specific pain points.

Our partnerships with marquee brands like Bajaj, Vi, and Maruti Suzuki did more than expand our reach; they kept us honest to the pace, rigor, and outcomes these sectors demand.

For instance, through these collaborations, clients have seen more than 18% increase in collections efficiency and crores in operational savings. Our partner ecosystem extends to BPOs, system integrators, and tech alliances, ensuring we stay ahead on compliance, feature roadmaps, and go-to-market innovation.

Where do you see Ori in the next five years, both in India and globally?

Maaz Ansari: In five years, the ORI platform won’t just be present across India’s BFSI, telecom, and emerging enterprise sectors, it’ll be synonymous with production-grade AI customer engagement globally. With teams already on the ground in several locations, we’re scaling to deliver localized compliance, language, and cultural empathy everywhere.

Our aim is to make ORI the go-to copilot for growth, cost-efficiency, and regulatory trust in every high-stakes industry worldwide, not just BFSI.

What advice would you give to young entrepreneurs seeking to build impact-driven tech startups?

Maaz Ansari: Start deep, not wide. Find the one industry pain point where your technology can make an unmissable difference, and prove it through results, not hype. Don’t fear regulated or “tough” sectors; that’s where trust, learning, and real business value are built.

Listen fiercely to your users, iterate relentlessly, and build for outcomes, not vanity metrics. Collaboration and resilience matter more than lone genius, embrace both. And above all, know that genuine impact takes patience, grit, and the humility to keep learning as your startup grows.

From vision to execution, Maaz Ansari’s story reflects Ori’s commitment to shaping the future of generative AI.

His insights reveal not only the potential of AI in driving business transformation but also the determination behind Ori’s growth.

As enterprises embrace intelligent automation, Ori stands as a pioneering force, led by Maaz’s forward-looking leadership.

Read the full article here.

By Maaz Ansari, Co-Founder, Oriserve

The way organisations approach debt collections and customer retention is undergoing a fundamental transformation. What was once a rigid, script-driven process handled largely through call centres is now giving way to more nuanced, technology-enabled interactions. Customers, even in sensitive financial situations, increasingly expect speed, empathy, and personalised engagement — a reality that traditional models struggle to deliver.

Adding to this shift are regulatory and compliance pressures. One-size-fits-all collection strategies are becoming less effective and, in some cases, risk running afoul of evolving frameworks that demand fairness, transparency, and customer-first communication. Against this backdrop, the industry is beginning to recognise the power of Human+AI collaboration.

Why Human+AI Collaboration Works

For context, research finds that 71% of consumers expect personalized interactions and 76% feel frustrated when they’re absent; companies that execute personalization well typically see a 5–15% revenue lift.

Neither humans nor machines, on their own, can address the complexities of modern collections. Automated tools are great at speed and consistency, but they often miss the human touch — the pauses, tone shifts, and emotional cues that shape genuine conversations. People, on the other hand, bring empathy and judgement, though they can quickly get overwhelmed when the workload is high.

The real breakthrough comes when the two work together. Routine, data-heavy work is where technology proves its worth. By taking on those tasks, it frees people to focus on the conversations that truly matter — the ones that demand empathy, careful listening, and negotiation. This isn’t about replacing human involvement; it’s about creating the right balance, where technology does the groundwork and people bring the depth of judgement and understanding that only they can offer.

In practice, large-scale field evidence shows complementarity: in a study of 5,179 customer‑support agents, gen‑AI assistance increased issues resolved per hour by ~14% on average (and by 34% for novices).

AI Voice Assistants in Action

A clear example of this collaboration is the rise of AI-powered voice assistants. Far beyond basic chatbots, these tools can now engage in natural, conversational dialogues that remain compliant and respectful. They work round the clock, take on high-volume outreach, and ease the workload on call centre teams.

Adoption and ROI are accelerating: venture funding into AI voice agents grew from about $315 million (2022) to $2.1 billion (2024), and analysts expect ~75% of new contact centers to incorporate generative AI by 2028; studies also estimate chatbot‑driven service savings of $7.3 billion in banking by 2023 and over $8 billion per year across sectors.

Crucially, they don’t work in silos. These tools are able to sense when a customer is uncertain, frustrated, or simply needs more attention, and at that point they can shift the conversation to a human agent. This smooth transition makes sure customers get the right support at the right time, without the impersonal experience of being trapped in a fully automated process.

Personalisation as the Key to Retention

Debt collection is no longer just about recovering dues; it is also about preserving customer relationships. AI systems can analyse payment histories, behavioural patterns, and sentiment signals to recommend personalised repayment plans or engagement strategies.

Personalization has shown tangible impact: typical revenue lift of 10–15%, with most customers expecting it; even small retention gains compound—e.g., a 5% increase in customer retention can raise profits by 25–95%.

When a customer hesitates, expresses frustration, or requires a tailored arrangement, human agents can step in to provide empathy and flexibility. When technology and human insight work together, the outcome goes beyond faster repayments. Customers are more likely to feel understood and supported, which in turn builds loyalty and strengthens long-term relationships. What could have been a difficult or negative encounter becomes an opportunity to earn trust.

Keeping Compliance and Ethics at the Core

In financial services, compliance and ethics cannot be treated as an afterthought. Technology can speed up the process, but it cannot be left unchecked. Human judgement is still needed to make sure communication follows the law, stays respectful, and feels transparent. Customers also deserve honesty — they should be told when they’re interacting with an AI system. Above all, the tone must protect dignity and fairness.

In addition to disclosure and fairness requirements in lenders’ Fair Practices Codes, supervisory guidance in India (RBI) and the U.S. (CFPB) codify respectful communication norms such as restricted call times and frequency presumptions.

Measuring Success

The results of Human+AI collaboration show up in two places. On the collections side, one can see faster repayments, shorter cycles, and leaner costs. On the retention side, the signs are different — fewer customers leaving, stronger repeat relationships, and more positive feedback.

These measures highlight not just stronger financial results, but also a better customer experience — an area that is fast becoming a decisive factor in competitive advantage.

Also Read: How Businesses Can Navigate Risks in the Digital Era

Looking Forward

The next chapter in this journey will move from reactive to predictive. As AI systems grow more capable of recognising risk signals early, organisations will be able to intervene before defaults occur, taking a more proactive approach to customer retention.

In this future, Human+AI collaboration won’t simply mean doing things faster. It will mean doing them smarter — anticipating needs, responding with empathy, and reshaping collections into a process that protects relationships as much as it recovers dues.

About the Author

Maaz Ansari, Co-Founder of Oriserve, is a tech enthusiast and AI evangelist with over 12 years of experience scaling startups. With a background as a data scientist, solution consultant, and product builder, he specializes in leveraging technology to deliver business impact. In 2017, Maaz co-founded Ori to empower SMBs and enterprises with conversational AI solutions, working with brands like VI, Bajaj Auto, Education First, Air Arabia, and Maruti Suzuki. He has spearheaded innovations such as VoiceGenie.ai and Orimon.ai, making AI adoption seamless and accessible. Passionate about disruptive technologies, Maaz champions AI-driven growth, customer engagement, and future-ready businesses.

Read the full article published on Finance Outlook.

By Maaz Ansari, Co-Founder, Oriserve

The way organisations approach debt collections and customer retention is undergoing a fundamental transformation. What was once a rigid, script-driven process handled largely through call centres is now giving way to more nuanced, technology-enabled interactions. Customers, even in sensitive financial situations, increasingly expect speed, empathy, and personalised engagement — a reality that traditional models struggle to deliver.

Adding to this shift are regulatory and compliance pressures. One-size-fits-all collection strategies are becoming less effective and, in some cases, risk running afoul of evolving frameworks that demand fairness, transparency, and customer-first communication. Against this backdrop, the industry is beginning to recognise the power of Human+AI collaboration.

Why Human+AI Collaboration Works

For context, research finds that 71% of consumers expect personalized interactions and 76% feel frustrated when they’re absent; companies that execute personalization well typically see a 5–15% revenue lift.

Neither humans nor machines, on their own, can address the complexities of modern collections. Automated tools are great at speed and consistency, but they often miss the human touch — the pauses, tone shifts, and emotional cues that shape genuine conversations. People, on the other hand, bring empathy and judgement, though they can quickly get overwhelmed when the workload is high.

The real breakthrough comes when the two work together. Routine, data-heavy work is where technology proves its worth. By taking on those tasks, it frees people to focus on the conversations that truly matter — the ones that demand empathy, careful listening, and negotiation. This isn’t about replacing human involvement; it’s about creating the right balance, where technology does the groundwork and people bring the depth of judgement and understanding that only they can offer.

In practice, large-scale field evidence shows complementarity: in a study of 5,179 customer‑support agents, gen‑AI assistance increased issues resolved per hour by ~14% on average (and by 34% for novices).

AI Voice Assistants in Action

A clear example of this collaboration is the rise of AI-powered voice assistants. Far beyond basic chatbots, these tools can now engage in natural, conversational dialogues that remain compliant and respectful. They work round the clock, take on high-volume outreach, and ease the workload on call centre teams.

Adoption and ROI are accelerating: venture funding into AI voice agents grew from about $315 million (2022) to $2.1 billion (2024), and analysts expect ~75% of new contact centers to incorporate generative AI by 2028; studies also estimate chatbot‑driven service savings of $7.3 billion in banking by 2023 and over $8 billion per year across sectors.

Crucially, they don’t work in silos. These tools are able to sense when a customer is uncertain, frustrated, or simply needs more attention, and at that point they can shift the conversation to a human agent. This smooth transition makes sure customers get the right support at the right time, without the impersonal experience of being trapped in a fully automated process.

Personalisation as the Key to Retention

Debt collection is no longer just about recovering dues; it is also about preserving customer relationships. AI systems can analyse payment histories, behavioural patterns, and sentiment signals to recommend personalised repayment plans or engagement strategies.

Personalization has shown tangible impact: typical revenue lift of 10–15%, with most customers expecting it; even small retention gains compound—e.g., a 5% increase in customer retention can raise profits by 25–95%.

When a customer hesitates, expresses frustration, or requires a tailored arrangement, human agents can step in to provide empathy and flexibility. When technology and human insight work together, the outcome goes beyond faster repayments. Customers are more likely to feel understood and supported, which in turn builds loyalty and strengthens long-term relationships. What could have been a difficult or negative encounter becomes an opportunity to earn trust.

Keeping Compliance and Ethics at the Core

In financial services, compliance and ethics cannot be treated as an afterthought. Technology can speed up the process, but it cannot be left unchecked. Human judgement is still needed to make sure communication follows the law, stays respectful, and feels transparent. Customers also deserve honesty — they should be told when they’re interacting with an AI system. Above all, the tone must protect dignity and fairness.

In addition to disclosure and fairness requirements in lenders’ Fair Practices Codes, supervisory guidance in India (RBI) and the U.S. (CFPB) codify respectful communication norms such as restricted call times and frequency presumptions.

Measuring Success

The results of Human+AI collaboration show up in two places. On the collections side, one can see faster repayments, shorter cycles, and leaner costs. On the retention side, the signs are different — fewer customers leaving, stronger repeat relationships, and more positive feedback.

These measures highlight not just stronger financial results, but also a better customer experience — an area that is fast becoming a decisive factor in competitive advantage.

Also Read: How Businesses Can Navigate Risks in the Digital Era

Looking Forward

The next chapter in this journey will move from reactive to predictive. As AI systems grow more capable of recognising risk signals early, organisations will be able to intervene before defaults occur, taking a more proactive approach to customer retention.

In this future, Human+AI collaboration won’t simply mean doing things faster. It will mean doing them smarter — anticipating needs, responding with empathy, and reshaping collections into a process that protects relationships as much as it recovers dues.

About the Author

Maaz Ansari, Co-Founder of Oriserve, is a tech enthusiast and AI evangelist with over 12 years of experience scaling startups. With a background as a data scientist, solution consultant, and product builder, he specializes in leveraging technology to deliver business impact. In 2017, Maaz co-founded Ori to empower SMBs and enterprises with conversational AI solutions, working with brands like VI, Bajaj Auto, Education First, Air Arabia, and Maruti Suzuki. He has spearheaded innovations such as VoiceGenie.ai and Orimon.ai, making AI adoption seamless and accessible. Passionate about disruptive technologies, Maaz champions AI-driven growth, customer engagement, and future-ready businesses.

Read the full article published on Finance Outlook.