DEV Community: Tanya qoulomb

How AI SEO Is Changing Digital Visibility for Businesses

Tanya qoulomb — Mon, 21 Sep 2026 06:56:36 +0000

The digital search landscape is undergoing a major transformation. People are increasingly using AI-powered platforms to find information, compare businesses, and research products and services. Tools such as ChatGPT, Gemini, Perplexity, and Google's AI search features can now provide summarized answers instead of requiring users to visit multiple websites.

For businesses, this creates a new challenge: being visible in AI-generated results alongside conventional search rankings.

Moving Beyond Traditional SEO

Search engine optimization has traditionally involved keyword targeting, technical improvements, link building, and publishing relevant content. These practices remain valuable, but AI-driven search introduces additional considerations.

AI SEO focuses on helping search systems understand a company's expertise, services, products, and overall online presence. Strategies may include Generative Engine Optimization (GEO), Answer Engine Optimization (AEO), entity optimization, structured data, and content designed around conversational search queries.

Rather than concentrating solely on where a page ranks, businesses are increasingly interested in whether their brand can be discovered, understood, and mentioned by AI-powered search systems.

What to Check Before Hiring an Agency

Reading AI SEO agency reviews can be useful when researching potential service providers, but reviews should not be the only deciding factor. Businesses should investigate what each agency actually offers and whether its methods fit their objectives.

One important factor is content strategy. Effective AI SEO involves more than producing articles with keywords. Content should answer relevant questions, provide reliable information, demonstrate subject expertise, and be organized clearly.

Technical capabilities are also worth examining. Depending on the business, an agency might work on schema markup, website architecture, internal linking, entity signals, digital PR, backlinks, and authoritative third party references.

Understanding Performance Metrics

Another difference between traditional and AI-focused SEO is measurement. Keyword positions and organic traffic are still useful indicators, but they don't capture every aspect of AI search visibility.

Businesses may also track how frequently their brand appears in AI-generated responses, whether external sources cite their content, referral traffic from emerging platforms, qualified leads, and conversions.

Before hiring an agency, companies should therefore ask which metrics will be reported and how progress will be evaluated.

Finding an Approach That Fits

Every business has different search requirements. A SaaS company, for example, may need thought leadership and industry-specific content, while a local service provider may focus more heavily on location-based visibility and business information.

Qoulomb's 2026 review looks at agencies such as iPullRank, Searchbloom, Qoulomb, Omniscient Digital, eSEOspace, Intero Digital, and First Page Sage, highlighting different approaches to AI-focused SEO.

The Next Stage of Search

AI SEO is not simply a replacement for traditional optimization. Instead, it adds another layer to an established digital strategy. Businesses still need technically sound websites, authoritative content, quality links, and a strong user experience.

The growing role of AI means companies must also ensure that their online information is consistent, trustworthy, and easy for intelligent search systems to interpret.

For organizations adapting to this changing environment, comparing an agency's experience, methodology, transparency, and reporting can provide a more practical basis for choosing an AI SEO partner.

AI SEO Strategies for Businesses Competing in Abu Dhabi

Tanya qoulomb — Fri, 18 Sep 2026 08:38:21 +0000

The way people discover businesses online is changing rapidly. Search is no longer limited to entering keywords into Google and choosing a website from a list of blue links. Consumers are increasingly turning to AI-powered search tools and conversational platforms to find information, compare services, and identify companies that can meet their needs.

For businesses in Abu Dhabi, this evolution creates a new digital marketing challenge. Having a well-optimized website is still important, but companies also need to ensure that their information can be understood and surfaced by modern AI-driven search systems.

Understanding the New Search Environment

Traditional SEO primarily concentrates on factors such as keywords, backlinks, technical performance, and organic rankings. AI SEO expands this approach by focusing on context, relevance, authority, and the relationship between different pieces of information.

AI powered search systems attempt to understand the meaning behind a query rather than simply matching individual words. As a result, businesses should create content that directly addresses customer questions and provides useful, comprehensive information.

This includes optimizing for conversational queries, answering specific questions, explaining complex subjects clearly, and developing content around related topics rather than relying on a single keyword.

The Importance of Abu Dhabi's Local Market

Local context is particularly important for companies targeting customers in Abu Dhabi. The city's economy includes sectors such as real estate, finance, tourism, healthcare, technology, construction, education, and professional services.

Each sector has its own audience, terminology, purchasing journey, and search requirements. An effective AI SEO strategy should therefore reflect the characteristics of the business and its target market.

Companies considering the best AI SEO agencies in Abu Dhabi should look beyond generic SEO services and examine whether an agency understands local search behavior, industry-specific requirements, and AI-driven discovery.

Creating Content AI Systems Can Understand

High-quality content remains one of the foundations of search visibility. However, simply producing large volumes of articles is unlikely to be enough.

Businesses should concentrate on information that demonstrates genuine expertise and answers real customer concerns. Useful formats include:

Industry-specific guides
Frequently asked questions
Original research
Detailed case studies
Service comparisons
Expert insights
Educational resources
Location-focused content

Content should also have a logical structure. Descriptive headings, concise paragraphs, lists, tables, internal links, and relevant structured data can make information easier for both users and search systems to process.

Building Digital Authority

A company's website is only one part of its online identity. Search and AI systems can encounter information about a brand across business directories, publications, industry websites, reviews, social platforms, and other third-party sources.

Maintaining accurate and consistent business information across these channels can help establish a clearer digital identity. Relevant mentions and credible references can also strengthen a company's broader online presence.

This makes digital PR, brand mentions, authoritative backlinks, and third-party content valuable components of a wider AI SEO strategy.

What to Look for in an AI SEO Agency

Selecting an agency requires more than comparing monthly packages. Businesses should investigate how prospective agencies approach AI search and how they define success.

Important considerations include their experience with technical SEO, content strategy, entity optimization, local search, and emerging AI search platforms. It is also useful to understand how they report results.

Instead of relying exclusively on keyword rankings, businesses can evaluate metrics such as organic traffic, qualified leads, conversions, brand visibility, AI-generated mentions, and citations where measurable.

Adapting to the Future of Search

AI SEO should complement not replace the fundamentals of search optimization. A technically sound website, authoritative content, strong internal linking, relevant keywords, and a trustworthy digital presence continue to provide the foundation.

The emerging opportunity is to make that foundation understandable across a wider range of search experiences.

For businesses in Abu Dhabi, adapting early can help them build a stronger digital presence as search becomes increasingly conversational, contextual, and AI-driven.

AI Feedback Loops: The Foundation of Self-Improving Systems

Tanya qoulomb — Tue, 15 Sep 2026 07:04:25 +0000

Artificial intelligence is increasingly moving from static models toward systems that can evaluate their own performance and make targeted changes. This has created significant interest in self-improving AI, but the term can mean very different things. Some systems improve their outputs, others modify code or training processes, while the most ambitious vision involves AI independently conducting research and continuously improving itself.

The evidence available today supports the first two categories far more strongly than the last.

What Does Self-Improving AI Mean?

A self-improving AI system changes some part of how it produces results, uses information gathered during operation, and checks whether the modification produced an improvement. The change might affect the model's outputs, software framework, training data, model weights, or the pipeline used to create another model.

The basic process can be represented as:

propose → test → evaluate → keep or reject → repeat.

This creates an AI feedback loop in which each successful experiment can influence subsequent attempts.

What Has Actually Been Demonstrated?

Several research projects provide concrete examples.

STaR demonstrated a loop in which an AI generates reasoning chains, retains examples that produce correct answers, and uses those examples for additional training. The process can improve performance, although it depends on having problems with checkable answers.

In software engineering, SICA demonstrated an AI system capable of modifying its own codebase and improving its performance on part of the SWE-bench Verified benchmark. AlphaEvolve uses AI-generated programs within an evolutionary search process and automated evaluation to discover improvements in algorithms and computational procedures.

The Darwin Gödel Machine represents another step. It can modify the logic involved in its own improvement process and evaluate proposed changes against a benchmark. These examples demonstrate measurable self-improvement, but within carefully bounded environments.

Verification Sets the Limit

One of the strongest conclusions from current research is that verification determines the reliability of improvement.

Formal mathematical verification provides a strong signal because a proposed result can be checked independently. Executable software tests can also provide useful evidence. However, learned AI judges are less reliable because they can drift or reinforce the assumptions of the system being evaluated.

This creates a fundamental problem: an AI may appear to improve simply because its evaluation mechanism has become easier to satisfy.

For this reason, successful self-improvement requires contact with signals outside the model itself.

Self Improvement Is Not Self Learning

Self-learning and self-improvement are often treated as synonyms, but they describe different concepts.

Self-learning generally refers to learning patterns from data without explicit labels. Self-improvement is an operational process in which a deployed system changes something about its own behavior or infrastructure and verifies the result.

A model can therefore use self-learning techniques without ever modifying itself after deployment.

What Is Still Missing?

The biggest gap is research direction. Current systems can optimize a problem they have been given, but they do not independently decide what problem is worth solving next.

Humans still define the objective, evaluation method, and operating environment. Removing those human-defined boundaries has not yet been demonstrated.

The current picture is therefore more practical than speculative: self-improving AI is real, but its progress consists largely of narrow, measurable improvements. The next major milestone will be creating systems that can reliably expand the scope of what they improve while maintaining trustworthy external verification.

How Does Recursive Self-Improvement Work in AI Systems?

Tanya qoulomb — Tue, 15 Sep 2026 06:09:02 +0000

Artificial intelligence is evolving beyond systems that simply follow instructions or generate responses. One of the more ambitious ideas in AI research is recursive self-improvement (RSI) - the possibility that an AI system could improve its own capabilities, strategies, or underlying processes over repeated cycles of learning and evaluation.

The concept has roots in the work of mathematician I. J. Good, who proposed the idea of an “intelligence explosion” in which a sufficiently intelligent machine could design improved versions of itself. While this scenario remains theoretical, modern AI research is exploring smaller and more controlled forms of self improvement.

How Does AI Improve Itself?

At a basic level, AI improvement can happen through a feedback loop. A system performs a task, evaluates its performance, identifies weaknesses, and changes its approach based on the results. The improved approach can then be tested again.

For example, an AI coding system could generate a program, run automated tests, detect errors, modify its code, and test the revised version. If the new solution performs better according to predefined measurements, the system can retain the successful strategy for future tasks.

Techniques such as reinforcement learning, automated evaluation, self-generated training data, and meta-learning can contribute to this process. However, these methods generally operate within specific objectives and constraints rather than allowing completely unrestricted self-modification.

What Makes Improvement Recursive?

The key difference between ordinary optimization and recursive self-improvement is that the system can improve not only its output but also the methods it uses to produce better outputs.

This creates a potential feedback loop. Better problem-solving methods can help the system discover even better methods, which may subsequently improve its performance further. In theory, repeated cycles could produce increasingly capable systems.

For this process to work reliably, three components are particularly important: autonomy, learning, and verification. An AI needs enough independence to experiment with different approaches, the ability to learn from results, and reliable evaluation mechanisms to determine whether an apparent improvement is genuine.

Current Progress and Limitations

Modern research has demonstrated limited examples of AI assisted improvement. Systems and research projects such as AlphaEvolve and the Darwin Gödel Machine explore ways AI can optimize algorithms, generate solutions, or modify code based on evaluation.

Nevertheless, today's AI systems are far from unrestricted self-improving intelligence. They usually depend on human designed objectives, testing environments, computational resources, and safety constraints.

Verification is one of the biggest challenges. If an AI incorrectly evaluates its own improvement, repeated optimization could reinforce poor strategies or errors instead of producing meaningful progress.

The Future of Recursive AI

Recursive self-improvement could eventually change how AI systems are developed. Instead of relying entirely on humans to design every improvement, future systems could participate more actively in optimizing their own learning processes.

For now, the most realistic direction is controlled self improvement within clearly defined environments. As AI becomes better at experimentation, evaluation, coding, and learning from feedback, recursive improvement may become an increasingly important part of AI research and development.

10 Leading AI SEO Agencies in India to Watch in 2026

Tanya qoulomb — Mon, 14 Sep 2026 10:01:25 +0000

Search is entering a new phase. People are increasingly turning to ChatGPT, Google AI Overviews, Gemini, Perplexity, and similar platforms to find information and make decisions. Instead of clicking through several websites, users can now receive summarized answers directly from AI systems.

For businesses, this means SEO is no longer only about achieving a position on Google's traditional results page. Brands also need to make their content understandable, authoritative, and useful to AI systems. Strategies such as Generative Engine Optimization (GEO) and Answer Engine Optimization (AEO) are becoming increasingly important for companies looking to rank in AI Overviews and gain visibility across AI powered search.

Here are 10 AI SEO agencies in India worth considering in 2026.

HavStrategy

HavStrategy works primarily with D2C and consumer-focused businesses. Its SEO approach combines traditional optimization with strategies designed around modern search behavior and AI-driven discovery.

Best for: Beauty, fashion, lifestyle, and D2C brands
Strength: Consumer focused SEO and GEO

AdLift

AdLift provides SEO solutions for startups, enterprises, and established companies. The agency uses data and AI assisted processes for areas such as keyword research, content optimization, search intent analysis, and performance measurement.

Best for: Enterprises and growing businesses
Strength: AI supported SEO execution

Qoulomb

Qoulomb is an AI focused B2B SEO agency that works across traditional search and emerging AI search platforms. Its services include AI SEO, GEO, AEO, technical SEO, and content strategy.

The agency works with startups, SaaS businesses, technology companies, and enterprises across sectors such as AI, fintech, healthcare, manufacturing, and edtech. Its methodology emphasizes entity optimization, topical authority, AI citations, and connecting organic visibility with qualified business outcomes.

Qoulomb has worked on more than 200 B2B engagements and lists companies including Samsung, Zetwerk, Slice Bank, CyberTech, and Titan Global Enterprises among its client portfolio.

Best for: B2B, SaaS, technology, and enterprise businesses
Strength: Integrated AI SEO, GEO, AEO, and technical SEO

Beyond ROAS

Beyond ROAS specializes in ecommerce SEO, with experience supporting Shopify and WooCommerce businesses. Its strategies focus on improving product discoverability, organic traffic, and conversions.

Best for: Ecommerce and D2C businesses
Strength: Ecommerce SEO and organic growth

Wildnet Technologies

Wildnet Technologies combines SEO with AI and predictive analytics. Its approach focuses on identifying changing search trends, automating optimization tasks, and improving technical SEO processes.

Best for: Large companies and global brands
Strength: Predictive and AI-assisted SEO

Webenza

Webenza takes a broader digital marketing approach, combining SEO, content, analytics, and digital strategy. Its use of AI-supported processes helps businesses respond to changing search behavior while improving their overall online visibility.

Best for: Brands seeking integrated digital marketing
Strength: SEO combined with content and digital marketing

SEO Discovery

SEO Discovery focuses on executing SEO campaigns at scale. AI assisted processes are used across areas such as keyword research, competitor analysis, optimization, and performance monitoring.

Best for: Mid sized and large businesses
Strength: Scalable SEO operations

PageTraffic

PageTraffic is an established SEO company with experience across multiple industries and markets. It combines conventional SEO practices with AI-supported research, content planning, and performance analysis.

Best for: SMBs, enterprises, and international businesses
Strength: Sustainable organic search growth

Growth Hackers Digital

Growth Hackers Digital combines SEO with content marketing and growth strategies. Rather than concentrating exclusively on traffic, its approach focuses on attracting users who are more likely to become leads or customers.

Best for: Startups, SaaS businesses, and growth stage companies
Strength: Performance oriented SEO

Infidigit

Infidigit provides technical and data-driven SEO services for businesses looking to strengthen their organic visibility. Its work includes technical optimization, keyword analysis, content improvement, and performance tracking, with AI increasingly supporting modern SEO workflows.

Best for: Enterprises, ecommerce companies, and growing brands
Strength: Technical and data driven SEO

Final Thoughts

The evolution of AI powered search is changing what it means to be visible online. Traditional rankings still matter, but businesses increasingly need to consider how AI systems discover, understand, evaluate, and cite their content.

The strongest AI SEO agencies are therefore moving beyond conventional keyword strategies and incorporating GEO, AEO, entity optimization, topical authority, technical SEO, and AI visibility into their campaigns.

For businesses preparing for the next stage of search, partnering with an agency that understands both traditional SEO and AI powered discovery can help create a stronger long-term search strategy.

Top 10 Gnani AI Alternatives for Voice Automation in India in 2026

Tanya qoulomb — Wed, 09 Sep 2026 09:54:43 +0000

Voice AI is becoming an important technology for Indian businesses that handle large volumes of customer conversations. From sales and collections to customer support and lead qualification, AI voice agents are increasingly being used to automate repetitive calling workflows.

Gnani.ai is one of the recognised names in India's enterprise voice AI ecosystem, particularly among BFSI and telecom organisations. However, its enterprise focused approach may not suit every company. Businesses may instead be looking for transparent pricing, faster deployment, developer flexibility, or a platform designed for a specific industry.

Here are ten Gnani AI alternatives worth evaluating in 2026.

DialNexa

DialNexa is an India-focused voice AI platform built for high-volume business conversations. Its use cases include sales, collections, customer support, recruitment, and lead qualification.

The platform supports English, Hindi, and regional languages such as Tamil, Telugu, Kannada, Malayalam, Gujarati, and Marathi. It also provides CRM integrations, APIs, and MCP access.

Pricing starts at approximately ₹5 per minute, with high volume enterprise pricing reaching around ₹2.5 per minute, depending on commercial terms.

Best for: Businesses seeking multilingual voice AI with transparent pricing and flexible deployment.

Fluid AI

Fluid AI focuses on enterprise conversational AI, particularly for banks and financial institutions. Its solutions cover voice, chat, and WhatsApp, with applications including onboarding, support, and cross-selling.

Its integrations with financial platforms such as Temenos, Fiserv, Jack Henry, and Infosys Finacle make it suitable for organisations with complex banking infrastructure.

Best for: Financial institutions requiring deep system integration.

Uniphore

Uniphore provides a broader Business AI platform that extends beyond voice automation. Its technology combines conversational AI, generative AI, workflow automation, and analytics.

The company has an established enterprise presence and is better suited to large organisations looking to adopt AI across multiple customer service functions.

Best for: Large enterprises seeking a comprehensive AI ecosystem.

SquadStack

SquadStack uses a hybrid approach, combining AI voice agents with human telecalling support. This can be useful when automated conversations need to be transferred to people for complex or sensitive interactions.

Its applications include sales, lending, lead conversion, and customer acquisition, with support for several Indian languages.

Best for: Sales and lending teams requiring human backup.

Vomyra

Vomyra is designed around simpler, no-code voice AI deployment. Its platform targets startups and smaller teams that want to create and manage AI calling workflows without building extensive infrastructure. It supports Indian mobile numbers and multiple AI model options.

Best for: Startups and smaller businesses wanting a relatively quick setup.

Yellow.ai

Yellow.ai is an omnichannel customer-experience automation platform serving enterprises across multiple industries. Its capabilities span voice, chat, messaging, and other customer-service channels.

With extensive integrations and support for multiple Indian languages, it is positioned more as a complete CX automation platform than a dedicated voice-only solution.

Best for: Large organisations seeking voice AI alongside broader customer-experience automation.

Arrowhead.ai

Arrowhead.ai focuses specifically on voice automation for the BFSI sector. Its solutions are designed around sales conversations involving banks, financial institutions, and related organisations.

Its specialised approach can be valuable for businesses that prefer a vertical-focused platform rather than a general-purpose voice AI provider.

Best for: BFSI organisations with specialised sales requirements.

Greylabs.ai

Greylabs.ai is another India-based company focused on financial services. Its offering combines voice automation with speech analytics for banks and insurance businesses.

The sector-specific focus can help organisations looking for technology designed around financial-services conversations and compliance requirements.

Best for: Banks and insurers requiring voice automation and speech analytics.

Bolna.ai

Bolna.ai takes a more developer-oriented approach to voice AI. The platform is suited to technical teams that want greater control over how their voice applications are assembled.

Its model can be attractive to businesses that prefer building customised voice workflows instead of adopting a fully packaged enterprise platform.

Best for: Developers and technical teams building customised voice applications.

Vapi

Vapi is a developer focused voice AI infrastructure platform based in the United States. It provides tools for engineering teams building custom voice agents and integrating them into applications.

Unlike India first providers, Vapi is not specifically designed around Indian telephony, regional-language requirements, or local business workflows.

Best for: Global development teams building custom voice applications.

How to Pick a Gnani AI Alternative

The right alternative depends on the business's priorities. Enterprises may prioritise compliance, security, integrations, and scalability, while smaller companies may value transparent pricing and rapid deployment.

Before making a decision, compare:

Indian language and accent support
Pricing and additional usage charges
CRM and telephony integrations
Deployment time
Scalability
Human escalation options
Security and compliance requirements
Availability of a pilot

A practical trial using your own scripts and call scenarios is usually more useful than relying only on a product demonstration.

Conclusion

Gnani.ai remains a strong option for enterprises that need sophisticated voice AI and extensive deployment capabilities. However, it is not the only choice.

DialNexa, Fluid AI, Uniphore, SquadStack, Vomyra, Yellow.ai, Arrowhead.ai, Greylabs.ai, Bolna.ai, and Vapi offer different approaches to voice automation, ranging from India-focused multilingual calling to enterprise CX platforms and developer infrastructure.

The best alternative ultimately depends on your industry, call volume, language requirements, technical environment, budget, and deployment timeline.

7 Leading Voice AI Platforms in India to Watch in 2026

Tanya qoulomb — Tue, 08 Sep 2026 12:36:02 +0000

Top 7 Voice AI Companies in India: A 2026 Guide for Businesses

Voice AI is rapidly becoming part of the way Indian businesses communicate with customers. Instead of relying entirely on traditional call centres, companies are using AI agents to qualify leads, answer customer queries, follow up with prospects, collect payments, schedule appointments, and handle repetitive conversations.

The market has also become more competitive. Some providers focus on enterprise deployments, while others target startups and businesses looking for faster, more flexible implementations. Here are seven voice AI companies worth considering in India in 2026.

1. DialNexa

DialNexa focuses on AI-powered business calling for the Indian market. Its platform can be used for sales, customer support, collections, recruitment, lead qualification, and other outbound and inbound workflows.

A key strength is its focus on Indian languages. Along with English and Hindi, the platform supports languages such as Tamil, Telugu, Kannada, Malayalam, Gujarati, and Marathi. It also provides CRM integrations, APIs, and MCP access for businesses that want to connect voice workflows with existing systems.

Pricing starts at around ₹5 per minute, with committed enterprise volumes available at approximately ₹2.5 per minute, subject to commercial terms.

Ideal for: Businesses needing multilingual voice automation across multiple industries.

2. SquadStack

SquadStack combines AI-powered calling with human telecalling capabilities. This hybrid approach is particularly useful for sales and lending operations where some conversations require human intervention.

The platform supports Indian languages including Hindi, English, Hinglish, Tamil, and Telugu. Its focus on high-volume calling makes it relevant for businesses managing large lead-generation or customer-acquisition campaigns.

Ideal for: Sales, lending, and businesses that want an AI-plus-human operating model.

3. Fluid AI

Fluid AI is primarily focused on enterprise conversational AI, with significant relevance to banking and financial services.

Its solutions cover voice, chat, and WhatsApp and can integrate with financial technology environments such as Temenos, Fiserv, Jack Henry, and Infosys Finacle.

For financial institutions, the ability to connect conversational AI with existing systems can be more important than simply having a sophisticated voice interface.

Ideal for: Banks and financial institutions requiring deep enterprise integration.

4. Uniphore

Uniphore is an established conversational AI company with a significant enterprise presence. Its offering extends beyond voice agents into generative AI, automation, analytics, and broader Business AI capabilities.

This makes it particularly relevant for large organisations looking to adopt AI across several customer-service and business functions rather than deploying an isolated voice solution.

Ideal for: Large enterprises seeking a broader AI technology platform.

5. Vomyra

Vomyra targets startups and smaller businesses that want to create AI voice agents without building extensive infrastructure themselves.

Its no-code approach can make experimentation easier for teams without dedicated engineering resources. The platform also supports Indian mobile numbers and multiple underlying AI models.

For companies testing their first automated calling campaign, a simpler setup can be more valuable than a highly complex enterprise platform.

Ideal for: Startups and small teams looking for faster deployment.

6. MyOperator

MyOperator brings voice AI into an established cloud-telephony environment. Its platform combines business calling, IVR, AI voice capabilities, WhatsApp, and human-agent workflows.

Because telephony is central to the platform, it can be an attractive option for companies that already manage customer communications through cloud-based phone systems.

Ideal for: Businesses wanting telephony, AI voice, and messaging in one ecosystem.

7. Bolti

Bolti focuses on speech and voice automation with an emphasis on Indian languages. Its platform supports languages such as Hindi, Tamil, and Telugu, along with wider multilingual capabilities.

It can work with telephony infrastructure through providers including Twilio, Plivo, and Exotel. Its pricing starts at approximately ₹6 per minute, with a trial option for businesses evaluating the platform.

Ideal for: Teams looking for Indian-language voice AI and flexible telephony integration.

What Should Businesses Compare?

Choosing a voice AI provider should involve more than comparing advertised prices. Businesses should test speech quality, response latency, language switching, CRM integrations, call reliability, scalability, and human escalation.

Pricing also needs to be evaluated against the complete workflow. Telephony charges, implementation, integrations, AI model usage, and call duration can all influence the final cost.

The strongest evaluation is usually a real-world pilot using your own scripts, customer scenarios, and expected call volumes.

Final Thoughts

India's voice AI ecosystem now includes solutions for startups, growing companies, large enterprises, banks, sales organisations, and customer-service teams.

DialNexa, SquadStack, Fluid AI, Uniphore, Vomyra, MyOperator, and Bolti each approach the market differently. The right choice ultimately depends on the complexity of your workflows, languages, integrations, budget, and deployment requirements.

Rather than selecting a vendor solely because of its brand or advertised features, businesses should run a practical pilot and measure the metrics that matter: connectivity, conversation completion, response time, escalation rate, and cost per successful interaction.

The Voice AI Revolution in 2026: Key Statistics and Trends

Tanya qoulomb — Tue, 01 Sep 2026 08:25:11 +0000

Voice AI is moving from experimental demos into real-world production systems. For developers, this creates a more complex challenge: building Voice agents that can perform reliably across thousands of conversations involving different languages, response patterns, customer behaviors, and calling conditions.

To understand these challenges, DialNexa’s State of Voice AI in India Report analyzed more than 1 million AI-assisted business calls across India. The report examines real world patterns in connectivity, retry strategies, response latency, customer engagement, language handling, and call outcomes.

The findings offer several practical lessons for developers building production grade Voice AI systems.

First Attempt Pickup Is Not the Whole Story

Outbound connectivity is one of the biggest challenges in business calling. According to DialNexa’s report, new calling numbers achieved an average 48% first attempt pickup rate across their first 1,000 leads. With increased usage, pickup performance declined in some categories to approximately 20%.

However, campaigns using structured retry strategies achieved more than 70% cumulative connectivity in some cases. For developers, this highlights an important architectural principle: an unanswered call should not necessarily be treated as a permanent failure.Retry logic can consider factors such as previous attempts, time of day, customer segments, and calling history. The goal is to optimize cumulative connectivity, not simply first-attempt pickup.

Latency Is a Voice UX Metric

Latency becomes especially noticeable in a voice interface. DialNexa’s State of Voice AI Report found that median AI response latency remained below one second, while P95 latency was approximately 2.1 seconds.

A voice response involves multiple components:

Speech → Speech-to-Text → AI processing → API/tool calls → Text-to-Speech → Audio delivery

A delay anywhere in this pipeline can affect conversational flow.For production systems, developers should therefore monitor end-to-end latency rather than focusing only on LLM inference time. P95 and other tail latency metrics are particularly useful because average response times can hide slower user experiences.

Conversation Retention Depends on Design

The report found that less than 3% of calls ended because the customer hung up first. This suggests that customers are not automatically rejecting AI conversations. However, retention depends on how the system behaves. Good conversation design requires effective turn taking, context management, interruption handling, relevant responses, and minimal repetition. The first few seconds are particularly important. If an agent sounds unnatural or responds slowly, users may disengage before the conversation delivers any value.

Voice UX should therefore be treated as part of the core system architecture.

Multilingual AI Must Handle Hinglish

India also presents a significant language challenge. DialNexa’s analysis highlights English, Hindi, and Hinglish across business conversations. Users can switch between Hindi and English within the same interaction, often using English terms for products, technology, or pricing. This means multilingual Voice AI requires more than translation.

A robust system needs language identification, multilingual speech recognition, context preservation, appropriate pronunciation, and intent recognition across code switched conversations.

For developers targeting India, natural Hinglish handling can be a stronger benchmark of conversational robustness than simply counting supported languages.

Inbound and Outbound Systems Need Different Strategies

The report found that approximately 84% of analyzed calls were outbound, while around 16% were inbound. Inbound conversations achieved approximately 89% goal completion, averaged around 13 minutes, and recorded a user initiated disconnect rate of approximately 0.01%.The difference is largely driven by intent.

Inbound callers have already chosen to engage, so the system can focus on understanding and completing the request. Outbound systems must first solve the connection problem. This means inbound and outbound Voice AI should not necessarily be optimized using identical architectures or metrics.

The Engineering Lesson

DialNexa’s State of Voice AI in India Report shows that production Voice AI is not just an LLM problem. It is a systems engineering problem.

Developers need to optimize:

1. Connectivity and retry strategies
2. End-to-end latency
3. Conversation management
4. Multilingual and Hinglish understanding
5. Goal completion
6. Inbound and outbound workflows
7. Calling-time optimization

The next generation of Voice AI will not be defined only by more realistic voices or larger models. The real challenge is building systems that can reliably turn conversations into outcomes at scale.

Why Voice AI Is Becoming a Business Infrastructure in India

Tanya qoulomb — Thu, 27 Aug 2026 12:47:29 +0000

Voice AI is entering a more mature stage in India. The conversation is no longer limited to whether an artificial voice can sound convincing. For businesses, the more consequential question is whether AI can manage customer conversations reliably, efficiently, and at scale.

DialNexa’s analysis of more than one million AI-assisted business calls across India provides a detailed view of the state of AI voice calling in India and how these systems perform in real world operating environments. The findings reveal that successful voice automation is shaped by several interconnected factors, from calling strategies and response latency to language flexibility, customer intent, and overall engagement patterns.

Scale Changes the Rules of Outbound Calling

Outbound calling performance cannot be understood through a single metric or a single attempt.

New calling numbers achieved approximately 48% pickup across their first 1,000 leads. With increased usage, however, pickup rates declined, reaching approximately 20% in some categories. This indicates that number reputation can become an operational constraint as calling activity grows.

The encouraging finding is that structured retries can substantially improve reach. In some campaigns, appropriately timed retry sequences pushed cumulative connectivity beyond 70%.

For businesses, this means that retry architecture should be incorporated into campaign design from the outset rather than introduced only after initial performance deteriorates.

AI Conversations Can Extend Beyond Scripts

A common perception is that voice agents are primarily useful for brief, highly scripted interactions. The data presents a more nuanced picture.

Most calls were short and aligned with their intended purpose. Webinar booking conversations averaged approximately 90 seconds, while pre sales qualification calls were closer to two minutes.

However, more than 120 calls exceeded 20 minutes. These extended interactions were not necessarily failures or system loops. They represented conversations in which the AI maintained engagement and continued working toward the desired outcome.

This suggests that call duration should be interpreted in context. A longer conversation is not inherently inefficient if it reflects meaningful engagement and produces a valuable result.

Responsiveness Has Commercial Consequences

In voice communication, delays are immediately noticeable. The dataset recorded a median response latency below one second, while p95 latency reached approximately 2.1 seconds.

This distinction is particularly important because the average experience can conceal slower interactions at the tail. A few extended pauses can disrupt conversational continuity and reduce the sense of immediacy.

Consequently, businesses deploying voice AI should monitor p95 latency alongside conventional averages. Product teams can also reduce delays through techniques such as response caching and parallelizing speech recognition and generation.

India Requires Conversational, Not Merely Multilingual, AI

Supporting Hindi and English independently is not enough for the Indian market.

Real conversations frequently involve code-switching, with speakers moving between Hindi and English naturally. The report identifies English, Hindi, and Hinglish among the prominent language patterns in the dataset and recommends speech to speech approaches where mixed language interactions are common.

This distinction matters because customers communicate organically rather than according to predefined language boundaries. A capable voice agent must therefore preserve context while adapting to the customer's linguistic behaviour.

Intent Is a Major Performance Advantage

The strongest applications for voice AI tend to have a clearly defined purpose.

Pre-sales qualification emerged as the leading use case, while webinar and event reminders also performed strongly. These workflows are structured around specific outcomes, making them easier to automate and measure.

Inbound calling presents an even stronger example. Although inbound conversations accounted for approximately 16% of the total dataset, they achieved 89% goal completion. The reason is straightforward: an inbound caller has already demonstrated intent, allowing the AI to focus on resolving the customer's objective rather than first establishing relevance.

Timing Remains an Underrated Variable

The report also identifies three particularly effective calling periods for the analyzed audience: 10 AM–12 PM, 4 PM–6 PM, and 8 PM–9 PM.

These windows should not be treated as universal benchmarks. Instead, they demonstrate that customer availability follows behavioural patterns, and businesses can improve efficiency by aligning outbound activity with those patterns.

The Shift From Voice Technology to Conversation Infrastructure

The central insight from the million call dataset is that voice quality alone does not determine business performance.

Number reputation influences reach. Retry logic influences cumulative connectivity. Latency influences conversational continuity. Language handling influences accessibility. Call design influences engagement. Timing influences pickup rates. And use case selection determines whether automation can generate measurable value.

Taken together, these variables represent a broader shift in how businesses should think about voice AI. It is no longer simply a tool for automating phone calls. It is becoming conversation infrastructure capable of extending business capacity without requiring every interaction to scale through additional human headcount.

The companies that extract the greatest value from this technology will therefore be those that optimize the complete interaction not just the voice at the other end of the line.

Voice AI at Scale: What India’s Business Calls Reveal

Tanya qoulomb — Thu, 27 Aug 2026 12:20:06 +0000

The conversation around artificial intelligence in customer communication has moved beyond theoretical possibilities. In India, voice AI is increasingly being integrated into operational workflows where responsiveness, scalability, and consistency directly influence commercial outcomes.

To understand the state of AI voice calling in India and examine how these systems perform under real-world conditions, DialNexa analyzed more than one million AI-assisted business calls made and received across the country. Rather than evaluating voice AI solely through the lens of speech quality, the analysis examined the broader determinants of conversational performance, including connectivity, retention, latency, language adaptability, use-case suitability, and calling behaviour.

The findings suggest that effective voice automation is less about a single technological capability and more about the orchestration of multiple operational variables. Together, these insights provide a clearer picture of how AI voice systems are performing today and what businesses need to consider as voice AI moves from experimentation into large-scale deployment.

Connectivity Requires a Lifecycle Approach

The first challenge in outbound calling is establishing contact. Newly deployed calling numbers achieved an approximately 48% first attempt pickup rate. However, connectivity weakened as calling volumes increased, with certain categories declining towards 20%.

Yet the decline was not necessarily permanent. Campaigns incorporating appropriately timed retry sequences achieved more than 70% cumulative connectivity in some cases.

This distinction has important implications for performance measurement. A first attempt pickup rate provides only a partial representation of campaign effectiveness. Evaluating connectivity across the complete retry lifecycle offers a more meaningful assessment of whether a lead was ultimately reached.

The Quality of Interaction Determines Retention

Once a call is answered, the objective shifts from connectivity to engagement.

Across the analysed dataset, fewer than 3% of calls ended through user initiated drop off. The research associated stronger retention with natural sounding voices, conversational rather than rigid openings, and transparency when callers questioned whether they were interacting with an AI.

This reinforces a fundamental principle of conversational AI: synthetic speech may initiate credibility, but interaction design determines whether credibility is sustained.

The opening moments of a call therefore deserve the same degree of attention as the underlying voice technology.

Latency Is a Core Component of User Experience

In voice interfaces, technical latency becomes perceptual latency. Users experience delays directly, often without distinguishing between network, speech recognition, language model, or synthesis bottlenecks.

The dataset recorded a median response latency below one second, with p95 latency reaching approximately 2.1 seconds.

The disparity between these figures is particularly instructive. While typical interactions remained highly responsive, occasional delays had the potential to disrupt conversational continuity. Consequently, organizations deploying voice AI should monitor tail latency rather than relying exclusively on average performance.

Response caching, parallel processing, and predictive response generation can help reduce these interruptions.

India's Linguistic Complexity Demands Native Adaptability

The Indian market introduces another dimension that conventional voice AI evaluations can underestimate: code-switching.

English, Hindi, and Hinglish represented the most consistently observed language patterns within the dataset. In everyday conversations, speakers may transition between Hindi and English without consciously changing languages.

The analysis found that speech-to-speech systems handled such mixed-language interactions more consistently than certain cascade architectures, where multiple processing stages can introduce transcription inaccuracies, pronunciation inconsistencies, or contextual degradation.

For businesses operating in India, multilingual capability should therefore be assessed through authentic mixed language conversations rather than isolated language demonstrations.

Strategic Use Case Selection Matters

The performance of an AI voice system is also heavily influenced by the problem it is being asked to solve.

Pre sales lead qualification represented the largest and most successful use case in the dataset, followed by webinar and event attendance. Both applications share a defining characteristic: a clearly articulated objective.

The agent can determine what information needs to be gathered, what questions should be asked, and what action should follow.

This suggests a pragmatic adoption strategy for organizations entering voice AI: begin with high-volume, structured workflows with measurable outcomes, rather than immediately attempting complex, open-ended conversations.

Timing Remains a Significant Variable

Calling strategy is not simply a matter of volume. It is also a matter of timing.

For campaigns targeting working professionals, the analysis identified three periods associated with stronger connectivity: 10 AM–12 PM, 4 PM–6 PM, and 8 PM–9 PM.

These intervals should be regarded as directional rather than universal. Different audiences exhibit different availability patterns. Nevertheless, the underlying finding is clear: connectivity is concentrated within specific periods, making audience specific calling windows an important optimization lever.

*Inbound Conversations Demonstrate the Value of Intent *

The contrast between inbound and outbound calling provides another significant insight.

Inbound interactions constituted approximately 16% of total call volume, yet achieved approximately 89% goal completion. Average inbound call duration was around 13 minutes, while user initiated disconnects accounted for only 0.01%.

The underlying explanation is intent. An inbound caller has already initiated the interaction, allowing the AI system to focus on answering questions and progressing towards the caller's objective rather than first establishing relevance.

The Strategic Implication

The findings collectively point towards a broader conclusion: voice AI should be treated as an integrated operational system, not merely as a speech generation technology .

Connectivity is influenced by number reputation and retry architecture. Engagement depends on conversational design and transparency. Perceived intelligence is affected by latency. Accessibility depends on multilingual performance. Efficiency depends on timing. And commercial value depends heavily on selecting an appropriate use case.

The most effective implementation strategy is therefore likely to be iterative: identify a structured, high volume workflow; establish measurable performance criteria; deploy the technology; analyze real conversations; and continuously refine the system.

The future of voice AI in India will not be determined exclusively by how naturally an AI can speak. It will be determined by how reliably it can understand context, adapt to real world communication patterns, respond without friction, and convert conversations into measurable outcomes.

Understanding India’s Voice AI Growth

Tanya qoulomb — Thu, 27 Aug 2026 12:09:27 +0000

Voice AI is entering a new phase in India. What was once largely associated with experimental chatbots and scripted automated calls is increasingly being deployed in real business environments, where success is measured not by how sophisticated the underlying technology appears, but by whether a conversation produces a meaningful outcome.

To examine what actually drives that success, DialNexa analyzed more than one million AI-assisted business calls across India. The dataset provides a rare view into the operational realities of voice AI at scale, revealing how connectivity, conversational quality, latency, language, timing, and use-case design interact to influence performance.

Connectivity Is More Than a First Attempt

A successful voice strategy begins before the conversation itself. The research recorded a 48% first-attempt pickup rate for newly deployed calling numbers. However, as calling volumes increased, pickup performance deteriorated, with certain categories declining to approximately 20%.

The more revealing finding was what happened next. Appropriately timed retry sequences pushed cumulative connectivity beyond 70% in some campaigns.

This suggests that evaluating outbound calling on the basis of a single attempt can be misleading. A sophisticated calling strategy must account for the entire contact lifecycle, including when and how subsequent attempts are made.

Retention Depends on Conversational Intelligence

Getting someone to answer is only the beginning. Once a call is connected, the quality of the interaction determines whether that initial opportunity becomes a productive conversation.

Across the dataset, fewer than 3% of calls ended because the recipient disconnected first. Natural-sounding voices, less rigid introductions, and transparent responses to questions about AI identity were associated with stronger retention.

The implication is significant: a convincing synthetic voice may establish the initial credibility of an interaction, but conversation design sustains engagement. Voice AI must therefore be engineered around the behavior of the conversation, rather than speech generation alone.

Latency Is a Human-Facing Metric

In conventional software, latency is often treated as an infrastructure concern. In voice AI, it becomes immediately perceptible to the user.

The analysis found a median response latency of under one second, while the 95th percentile reached approximately 2.1 seconds. Although most responses remained fast enough to preserve conversational flow, slower responses could introduce unnatural pauses and undermine the sense of immediacy.

This makes tail latency particularly important. Monitoring p95 performance, optimizing frequently used responses, and reducing unnecessary processing delays can have a direct impact on perceived conversational quality.

Multilingualism Is Fundamental to the Indian Market

One of the defining characteristics of Indian communication is fluid movement between languages. Conversations can transition from Hindi to English within a single sentence, creating a linguistic environment that conventional single-language testing does not adequately capture.

The dataset identified English, Hindi, and Hinglish as the most commonly observed language patterns. Speech-to-speech systems demonstrated greater consistency in mixed-language interactions than some cascade architectures, where separate transcription and generation stages can introduce pronunciation errors, contextual losses, or transcription drift.

For voice AI providers, multilingual capability should therefore be evaluated against authentic conversational behavior—not merely against a checklist of supported languages.

Clear Objectives Produce Stronger Outcomes

The data also demonstrates that use case selection is fundamental to AI performance.

Pre-sales lead qualification emerged as the largest and most successful use case in the dataset, followed by webinar and event attendance. Both scenarios have clearly articulated objectives, structured interactions, and measurable outcomes.

This provides a practical principle for organizations adopting voice AI: begin with conversations where success can be defined precisely. Once those workflows are reliable, more complex and open-ended applications can be introduced incrementally.

Timing Is an Overlooked Growth Lever

Even a highly capable voice agent cannot compensate for poor timing.

For campaigns targeting working professionals, three periods demonstrated particularly strong connectivity: 10 AM–12 PM, 4 PM–6 PM, and 8 PM–9 PM.

These intervals should not be interpreted as universal rules. Instead, they demonstrate a broader principle: audience availability is not evenly distributed throughout the day. Businesses can improve efficiency by identifying their own connectivity patterns and concentrating outbound activity accordingly.

Inbound Calling Reveals the Power of Intent

The distinction between inbound and outbound conversations is equally compelling.

Inbound interactions accounted for approximately 16% of total call volume, yet achieved around 89% goal completion. Their average duration was approximately 13 minutes, while user-initiated disconnects stood at only 0.01%.

The underlying advantage is intent. An inbound caller has already taken the initiative to engage. The AI therefore spends less effort establishing relevance and more time resolving the caller's immediate objective.

What the Million Call Dataset Ultimately Reveals

The central lesson is that voice AI cannot be evaluated solely through the quality of its synthetic speech.

Performance emerges from the interaction of multiple variables: number reputation, retry architecture, response latency, language adaptability, conversation design, timing, and use-case selection.

The strongest systems are consequently not those that merely imitate human speech. They are those that understand the operational conditions surrounding a conversation and optimize each stage of the interaction.

For businesses exploring AI calling, the path forward is relatively clear: begin with a high volume and structured workflow, establish measurable objectives, monitor the complete calling cycle, and continuously refine the experience using real interaction data.

India's voice AI opportunity will ultimately be defined not by how convincingly machines can speak, but by how effectively they can participate in conversations that people actually find useful.

AI Voice Agents in Hindi: Use Cases & Benefits

Tanya qoulomb — Tue, 18 Aug 2026 10:55:19 +0000

The way businesses communicate with customers is changing rapidly with the growth of artificial intelligence. AI Voice Agents are becoming an important part of customer support, sales, appointment management, and other business operations. In India, where Hindi is spoken by hundreds of millions of people, the ability to communicate naturally in Hindi makes voice AI especially useful.

A Hindi AI Voice Agent can understand spoken Hindi, process a customer's request, and respond through natural sounding speech. Unlike traditional IVR systems that depend on fixed menus, AI voice agents can understand conversational language and handle a wider range of customer queries.

What Is a Hindi AI Voice Agent?

A Hindi AI Voice Agent is an AI powered conversational system designed to communicate with users through voice in Hindi. It uses technologies such as speech recognition, natural language processing, large language models, and text to speech to understand and respond to conversations.

For example, instead of asking customers to "Press 1 for sales and Press 2 for support," a voice agent can simply ask, "Aap kis cheez mein madad chahte hain?" The customer can answer naturally, and the system can continue the conversation accordingly.

This makes voice interactions more convenient, particularly for users who are more comfortable speaking than typing.

Key Use Cases of Hindi AI Voice Agents

Customer Support

One of the most common applications is customer service. Businesses can use Hindi voice agents to answer frequently asked questions, provide order updates, explain services, and resolve basic customer issues.

This allows companies to offer support beyond traditional business hours while reducing the workload on human support teams.

Sales and Lead Qualification

AI voice agents can also help sales teams contact potential customers. They can introduce a product or service, ask qualifying questions, understand customer requirements, and collect relevant information.

For businesses handling large volumes of leads, automated voice conversations can help identify interested prospects before transferring them to a sales representative.

Appointment Scheduling

Healthcare providers, salons, financial services, educational institutions, and other businesses can use voice agents for appointment-related tasks.

A Hindi speaking customer could call and say that they want an appointment for a particular day. The AI agent can check available slots, confirm the preferred time, and provide the relevant information.

Payment and Reminder Calls

Businesses can use AI voice agents for automated reminders related to payments, subscriptions, renewals, deliveries, or appointments.

Instead of relying entirely on prerecorded messages, conversational AI can respond when customers ask questions or need additional information.

E-commerce and Order Updates

Online businesses can use voice AI to notify customers about order confirmations, delivery schedules, cancellations, or returns. Customers can also ask questions about their orders through a voice conversation.

This can be particularly valuable for users who prefer Hindi communication over English language applications.

Benefits of Hindi AI Voice Agents

Better Customer Experience
Communicating in a customer's preferred language can make interactions easier and more comfortable. Hindi voice AI can reduce language barriers and make digital services more accessible.

24/7 Availability
AI voice agents can handle conversations around the clock. Customers don't necessarily have to wait for business hours to get basic information or assistance.

Faster Response Times
A voice agent can handle multiple conversations without putting every customer into a queue. This can help businesses respond faster during periods of high demand.

Choosing the Right Voice AI Platform

Businesses evaluating voice AI solutions should consider factors such as Hindi language accuracy, naturalness of speech, response latency, integration capabilities, analytics, scalability, and security.

Rather than choosing a platform only because it supports Hindi, companies should test how accurately it understands different accents, conversational phrases, regional variations, and mixed Hindi-English speech.

For organizations comparing the best voice AI platform in India, it is also important to evaluate whether the platform can integrate with existing CRM, contact center, telephony, and business systems.

The Future of Hindi Voice AI

As conversational AI continues to improve, voice agents are likely to become more capable of handling complex, multi turn conversations. Support for Hindi and other Indian languages can help businesses reach customers who may not be comfortable using English first digital interfaces.

The biggest opportunity is not simply automating phone calls. It is creating more natural and accessible customer interactions where people can communicate with technology in the language they use every day.

For Indian businesses, Hindi AI Voice Agents can therefore become an important tool for improving accessibility, automating repetitive communication, and delivering faster customer experiences at scale.