Ringlyn AI vs Bolna AI: Features, Pricing & Why Teams Switch

Bolna AI vs Ringlyn AI: What You Need to Know

The voice AI infrastructure market in 2026 is crowded with platforms promising to automate phone conversations with human-like fluency, but the practical differences between providers become stark once businesses move beyond proof-of-concept experiments and into production deployments handling real customer interactions at scale. Bolna AI and Ringlyn AI represent two fundamentally different philosophies in this space. Bolna AI, founded in 2024 in Bengaluru and backed by a $6.3 million seed round from General Catalyst through Y Combinator's F25 batch, emerged from India's thriving startup ecosystem with a partially open-source framework and a strong emphasis on Indian vernacular language support, particularly Hindi and Hinglish. Ringlyn AI, by contrast, was built from inception as an enterprise-grade voice AI platform designed for global deployment, with HIPAA-compliant architecture, native CRM integrations with HubSpot, Salesforce, and GoHighLevel, and premium voice quality powered by ElevenLabs and Gemini voice models. Understanding the differences between these two platforms is essential for any business evaluating voice AI vendors, because choosing the wrong platform at the start creates compounding technical debt, integration complexity, and migration costs that grow with every month of deployment.

This comparison is particularly relevant for businesses that have encountered Bolna AI through its open-source GitHub repository or its presence in the Indian startup ecosystem and are now evaluating whether the platform can scale to meet production requirements across multiple geographies, regulated industries, or enterprise clients. Bolna AI's open-source roots and low entry pricing make it an appealing option for initial exploration, but as we will detail throughout this analysis, the platform's limitations in compliance certifications, customer support responsiveness, white-label capabilities, and global telephony infrastructure create significant friction for businesses that need more than a development sandbox. Ringlyn AI addresses each of these gaps with purpose-built enterprise features, transparent pricing across four clearly defined tiers, and the kind of dedicated support infrastructure that production-dependent businesses require when voice automation is not an experiment but a revenue-critical operation. Whether you are a startup evaluating your first voice AI vendor, an agency looking to resell voice automation under your own brand, or an enterprise compliance team assessing vendor risk, this head-to-head comparison provides the factual foundation you need to make a confident decision.

Bolna AI Overview: Strengths and Limitations

Bolna AI deserves recognition for several genuine strengths that have earned it a foothold in the voice AI market. The platform was co-founded by Maitreya Wagh, a former Bain consultant and IIT Delhi graduate who serves as CEO, and Prateek Sachan, an ex-Zomato engineer and fellow IIT Delhi alumnus who leads the technical team as CTO. Their Y Combinator F25 backing and $6.3 million seed round from General Catalyst in January 2026 provide a credible financial foundation for a company with roughly 25 employees. Bolna AI's partially open-source framework, released under an MIT license and available on GitHub with 614 stars, represents a genuine commitment to developer accessibility that distinguishes it from fully proprietary competitors. The platform's architecture supports an impressive breadth of third-party integrations at the component level: multiple TTS providers including ElevenLabs, Cartesia, Sarvam, AWS Polly, OpenAI, and Deepgram; STT engines like Deepgram nova-2, Azure Speech, and ElevenLabs; LLM support spanning GPT-4o, Claude, Gemini, DeepSeek, Groq, and many others via LiteLLM; and telephony through Twilio, Plivo, Exotel, and Vobiz. For teams operating primarily in India, Bolna AI's specialty in Hindi, Hinglish, and over ten Indian vernacular languages fills a genuine gap that most Western-built platforms overlook entirely.

However, the gap between Bolna AI's technical architecture and its production readiness for global, regulated, or enterprise use cases is substantial. The platform's Trustpilot profile reveals a troubling pattern: a 2.8 out of 5 rating based on just three reviews, all of which are one-star ratings. The complaints are not minor quibbles about feature preferences — they describe fundamental operational failures including complete absence of customer support, ignored refund requests, and no callback after booking a demo. For a platform asking businesses to route their customer conversations through its infrastructure, these reviews signal a support gap that cannot be dismissed as growing pains. The platform's Product Hunt presence shows a perfect 5 out of 5 rating, but closer examination reveals that both reviews were posted by insiders, including one from a self-identified "Founding Engineer at Bolna" — a pattern that undermines the credibility of that score entirely. Meanwhile, a known GitHub issue documents unusually high token usage, with some conversations consuming over 150,000 input tokens, which can dramatically inflate the LLM costs that users are responsible for under Bolna AI's bring-your-own-key pricing model and erode the platform's apparent cost advantage.

Perhaps the most significant limitation for businesses evaluating Bolna AI is the complete absence of publicly documented compliance certifications. Despite operating in a space where voice conversations routinely involve protected health information, financial data, and personally identifiable information, Bolna AI has not published SOC 2, HIPAA, or GDPR certifications. For any business operating in healthcare, financial services, insurance, legal, or any other regulated industry, this is not a minor gap — it is a disqualifying deficiency. Compliance certifications are not optional checkboxes; they represent independently verified assurances that a vendor's infrastructure, data handling practices, and security controls meet established standards. Without them, a business routing sensitive customer conversations through Bolna AI assumes the full liability risk with no third-party verification to present to auditors, regulators, or enterprise procurement teams. Additionally, Bolna AI's customer base is heavily concentrated in India, with approximately 75 percent of revenue coming from self-serve Indian customers and notable clients including Spinny, Varun Beverages, and GoKwik — all primarily Indian companies. This India-centric focus is reflected in the platform's telephony integrations, language priorities, and support coverage, creating potential friction for businesses that need consistent global performance across North American, European, and APAC telephony networks.

Ringlyn AI Overview: Enterprise-Ready from Day One

Ringlyn AI was designed with a fundamentally different set of priorities than platforms like Bolna AI that evolved from open-source developer tools into commercial products. From its earliest architecture decisions, Ringlyn AI prioritized the requirements that enterprise buyers, regulated industries, and revenue-dependent operations teams demand before committing to a voice AI vendor: HIPAA-compliant infrastructure with enterprise-grade encryption, transparent and predictable pricing, premium voice quality through best-in-class synthesis providers, native integrations with the CRM platforms businesses already use, and a dedicated support model with real human accountability. The platform's real-time orchestration engine coordinates automatic speech recognition, large language model reasoning, sentiment analysis, and voice synthesis in a unified pipeline that delivers consistently low latency across concurrent call volumes. Rather than building a proprietary voice engine with inherent quality limitations, Ringlyn AI integrated ElevenLabs voices and Gemini voices with full multilingual support, ensuring that voice quality is always at the frontier of what the industry can produce. This strategic decision means that as ElevenLabs and Google continue to advance their neural voice synthesis models, Ringlyn AI customers benefit from those improvements automatically without waiting for an internal R&D team to catch up.

Ringlyn AI's four-tier pricing structure provides a clear growth path from individual operators to enterprise teams to white-label agencies. The Starter plan at $49 per month includes everything a small business needs to deploy production voice agents: ElevenLabs voices, sentiment analysis, batch calling, API access, call recordings and transcripts, and advanced analytics. The Growth plan at $99 per month and Professional plan at $199 per month scale capacity with additional included minutes while maintaining the complete feature set across every tier — there are no feature gates that force businesses onto higher plans to access core functionality. For agencies and SaaS companies that want to offer voice AI under their own brand, Ringlyn AI's White-Label plan at $2,497 per month provides complete brand removal, custom domain support, a white-labeled client dashboard, and Stripe rebilling integration, enabling agencies to launch a branded voice AI product without any engineering overhead. The platform's native CRM integrations with HubSpot, Salesforce, and GoHighLevel eliminate the middleware complexity that plagues platforms relying on webhook-based connectivity, and the 24/7 availability with unlimited concurrent calls ensures that businesses never encounter artificial throttling during peak traffic periods. This combination of enterprise infrastructure, premium voice quality, transparent pricing, and dedicated support is what makes Ringlyn AI the natural upgrade path for teams that have outgrown or been frustrated by platforms like Bolna AI.

Side-by-Side Feature Comparison

Voice Quality and TTS Providers

Both Bolna AI and Ringlyn AI support ElevenLabs as a text-to-speech provider, which might suggest parity in voice quality at first glance. However, the reality is more nuanced. Bolna AI's architecture relies on a bring-your-own-key model where users provide their own API keys for ElevenLabs, Cartesia, Sarvam, AWS Polly, OpenAI TTS, or Deepgram, meaning the user bears the full cost and configuration complexity of the voice synthesis layer. This BYOK approach introduces several practical challenges: users must manage API key rotation, monitor usage quotas across multiple providers, handle rate limiting and failover scenarios, and troubleshoot voice quality issues without knowing whether the problem lies in Bolna AI's pipeline or the third-party TTS provider's API. Ringlyn AI takes a managed approach, providing ElevenLabs voices and Gemini voices as integrated, optimized components of the platform. Voice synthesis is pre-configured for telephony-grade audio quality, latency is optimized through pipeline-level integration rather than external API calls, and users never need to manage separate TTS provider accounts or API keys. The result is consistently high voice quality out of the box, with the natural intonation, emotional affect, and conversational fluidity that prevent callers from immediately identifying the agent as artificial intelligence.

Latency performance is another critical differentiator in voice quality perception. Bolna AI's marketing materials claim sub-300-millisecond response times, but their own technical documentation references sub-600-millisecond latency as the realistic benchmark, and real-world performance varies significantly depending on the chosen STT, LLM, and TTS provider combination. Because Bolna AI chains together independently hosted third-party services for each step of the pipeline — Deepgram for speech recognition, an LLM provider for reasoning, and an external TTS provider for voice synthesis — the total end-to-end latency is the sum of three separate network round trips plus processing time at each stage. This serial architecture means that any slowdown in a single provider cascades into a noticeable delay for the caller. The known GitHub issue documenting 150,000-plus input tokens per conversation suggests additional inefficiency in how Bolna AI manages context windows, which can further degrade response times as conversations grow longer. Ringlyn AI's orchestration engine was purpose-built to minimize end-to-end latency by parallelizing ASR, LLM, and TTS processing within a unified pipeline, delivering the responsive conversational rhythm that keeps callers engaged through multi-turn dialogues without the uncomfortable pauses that destroy caller confidence and increase abandonment rates.

LLM Support and Orchestration

Bolna AI's LLM support is genuinely broad, leveraging LiteLLM to provide access to GPT-4o, Claude, Gemini, DeepSeek, Groq, and a wide range of other models through a unified interface. This flexibility is one of Bolna AI's legitimate strengths, allowing developers to experiment with different models and select the one that best balances cost, speed, and reasoning quality for their specific use case. However, breadth of model access does not equal production-grade orchestration. Bolna AI's architecture treats the LLM as a single step in a linear pipeline, with limited ability to implement sophisticated orchestration patterns like parallel model inference, dynamic model routing based on conversation context, or fallback chains that automatically switch to a secondary model if the primary provider experiences latency spikes or downtime. The unusually high token consumption documented in GitHub issues suggests that Bolna AI's prompt management may not be optimized for telephony use cases, where keeping context windows lean is essential for both cost control and response speed. Ringlyn AI's real-time orchestration engine goes beyond simple model access to provide intelligent coordination across the entire conversation pipeline. The platform supports multiple LLM providers with the ability to route different conversation types to different models, apply sentiment-aware processing that adjusts agent behavior in real time, and maintain optimized context management that prevents the token bloat that inflates costs and degrades latency on platforms with less sophisticated pipeline architecture.

Integration Ecosystem

The integration strategy of a voice AI platform determines how deeply it can embed into existing business workflows and how much engineering effort is required to maintain those connections over time. Bolna AI's integration approach relies primarily on Zapier and Make.com for connecting to external business tools, which means that every data flow between a voice conversation and a CRM, helpdesk, or marketing automation platform requires configuring a separate automation tool, managing webhook triggers, handling data transformation logic, and maintaining the integration as either Bolna AI or the destination tool updates its API. This middleware-dependent approach is workable for simple use cases like sending a Slack notification after a call completes, but it quickly becomes unwieldy for complex business workflows that require bidirectional data sync, real-time contact enrichment during live calls, or multi-step automation sequences triggered by specific conversation outcomes. Each additional Zapier or Make.com workflow introduces another point of failure, another subscription cost, and another system that the operations team must monitor and troubleshoot when data stops flowing correctly.

Ringlyn AI eliminates this middleware layer entirely with native, pre-built CRM integrations for HubSpot, Salesforce, and GoHighLevel. These are not webhook-based connectors that require configuration in an external automation platform — they are first-party integrations built directly into the Ringlyn AI platform that handle bidirectional data synchronization, automatic contact creation and enrichment, deal stage updates based on call outcomes, and activity logging without requiring a single line of custom code or a third-party subscription. For businesses using GoHighLevel, which has become the dominant platform for marketing agencies and service businesses, Ringlyn AI's deep integration provides a seamless bridge between voice automation and the broader marketing, sales, and fulfillment workflows that agencies depend on for client delivery. The practical difference is significant: a Bolna AI user connecting to HubSpot must configure Zapier webhooks, map data fields, handle error cases, and maintain the integration as APIs evolve, while a Ringlyn AI user connects to HubSpot with a native integration in minutes and never thinks about middleware again. For teams managing dozens of client accounts or complex multi-system workflows, this difference in integration architecture translates directly into reduced engineering hours, lower operational risk, and faster time to value.

White-Label Capabilities

For agencies, BPO providers, and vertical SaaS companies, white-label capabilities are not an optional feature — they are the foundation of the entire business model. These organizations need to offer voice AI services under their own brand, manage multiple client accounts from a unified dashboard, control their own pricing and billing relationships, and ensure that the underlying platform provider is completely invisible to their end clients. Bolna AI does not offer a formal white-label program. There is no turnkey path for an agency to launch a branded voice AI product powered by Bolna AI without significant custom development, and the platform's open-source origins mean that technically savvy agencies could self-host the framework, but this requires substantial DevOps investment, ongoing maintenance of the infrastructure stack, and forfeiture of any managed support or SLA guarantees. Ringlyn AI's White-Label plan at $2,497 per month was purpose-built for exactly this market segment. It provides complete brand removal from every customer-facing touchpoint, custom domain support so clients access the platform through the agency's own URL, a white-labeled dashboard that agencies present as their own proprietary product, and Stripe rebilling integration that allows agencies to define their own pricing tiers, automatically collect recurring revenue from their clients, and manage the entire billing relationship without Ringlyn AI ever appearing in the client experience. An agency can go from signing up for Ringlyn AI's white-label plan to launching a fully branded voice AI product for their first client within days, not months.

Enterprise Compliance

Compliance certification is the single most consequential evaluation criterion for businesses operating in regulated industries, and it is where the difference between Bolna AI and Ringlyn AI is most stark. Bolna AI has no publicly documented SOC 2, HIPAA, or GDPR certifications as of April 2026. For a platform handling voice conversations that may contain protected health information, financial account details, social security numbers, or other sensitive personal data, the absence of these certifications is not merely a marketing gap — it represents a fundamental risk that compliance officers, legal teams, and enterprise procurement departments cannot overlook. Without SOC 2 certification, there is no independent verification that Bolna AI's infrastructure security controls, access management practices, and data handling procedures meet industry-recognized standards. Without HIPAA compliance, healthcare organizations cannot route patient conversations through the platform without assuming the full liability risk of a potential data breach or unauthorized disclosure.

Ringlyn AI maintains HIPAA-compliant architecture with enterprise-grade encryption, providing the foundation that regulated industries require before entrusting a vendor with sensitive conversation data. The platform includes call recordings and full transcripts on every pricing tier, ensuring that every interaction is documented with the completeness that HIPAA, state medical board regulations, financial services compliance requirements, and legal industry standards demand. Ringlyn AI's built-in sentiment analysis provides an additional compliance layer by flagging conversations where callers express distress, frustration, or confusion — triggers that may require human review under clinical communication standards or financial suitability requirements. The advanced analytics dashboard generates the detailed reporting that auditors expect during compliance reviews, including call volume metrics, outcome tracking, agent performance data, and quality assurance scoring. For enterprise procurement teams conducting vendor risk assessments, the contrast between Ringlyn AI's documented compliance posture and Bolna AI's absence of any publicly verifiable certifications makes the evaluation straightforward: Ringlyn AI presents a defensible vendor selection, while Bolna AI requires the business to accept compliance risk that may be difficult to justify to regulators, auditors, or the organization's own legal counsel.

Pricing and Scalability

Bolna AI's pricing structure is built around a $0.02 per minute platform fee plus the cost of each component in the pipeline — STT, LLM, and TTS — which users pay for separately, often through their own API keys. The platform offers volume-based tiers ranging from $0.07 to $0.125 per minute for bundled pricing and provides $5 in free credits upon signup for initial testing. At first glance, this component-based pricing appears cost-effective, particularly for teams that already hold API keys for providers like Deepgram, OpenAI, or ElevenLabs. However, the true cost of operating on Bolna AI is significantly higher than the headline $0.02 per minute platform fee suggests. When you factor in the separate charges for speech recognition, language model inference, and voice synthesis, along with the documented issue of unusually high token consumption that can push LLM costs well above expected levels, the actual per-minute cost frequently reaches $0.10 to $0.15 or higher depending on the provider combination selected. The bring-your-own-key model also means that users are responsible for managing billing relationships with multiple providers, monitoring usage across separate dashboards, and handling the operational complexity of rate limiting, quota management, and cost optimization across three to four different vendor accounts simultaneously.

Ringlyn AI's pricing was deliberately designed to eliminate this complexity with a transparent, all-inclusive tier structure. The Starter plan at $49 per month, Growth plan at $99 per month, and Professional plan at $199 per month each include a defined allocation of minutes with the complete feature set available on every tier: ElevenLabs voices, Gemini voices, sentiment analysis, batch calling, CRM integrations, API access, call recordings, transcripts, and advanced analytics. There are no separate charges for STT, LLM, or TTS processing — the per-minute rate is the total cost with no hidden component fees lurking beneath the surface. For a business making 2,000 minutes of calls per month, forecasting costs on Ringlyn AI requires a single calculation based on the plan's included minutes plus any overage, while forecasting costs on Bolna AI requires modeling the per-minute rates of three to four separate providers, accounting for variable token consumption that may spike unpredictably based on conversation complexity, and monitoring usage across multiple billing dashboards. For finance teams accustomed to SaaS pricing predictability, Ringlyn AI's structure is a straightforward budget line item, while Bolna AI's disaggregated model creates the kind of billing uncertainty that makes CFOs and procurement teams uncomfortable.

Complete Comparison Table

When Bolna AI Makes Sense (and When It Doesn't)

In the interest of providing a balanced evaluation, there are specific scenarios where Bolna AI may be a reasonable choice. If your business operates exclusively within the Indian market, your call volumes are modest, your use cases do not involve regulated data, and your engineering team has the bandwidth to manage a multi-vendor BYOK infrastructure, Bolna AI's low platform fee and strong Hindi and Hinglish language support can deliver functional voice automation at a competitive cost point. The partially open-source framework is genuinely valuable for developer teams that want to inspect the codebase, understand the pipeline architecture, or contribute customizations that the managed platform does not yet support. For early-stage Indian startups with technical co-founders who are comfortable self-hosting infrastructure and managing API key configurations across multiple providers, Bolna AI's component-based architecture provides flexibility that some fully managed platforms do not offer. The $5 free credits on signup lower the barrier to experimentation, and the platform's integration with India-specific telephony providers like Exotel and Vobiz addresses connectivity requirements that Western-built platforms often neglect entirely.

However, the list of scenarios where Bolna AI falls short is considerably longer and more consequential for businesses that depend on voice automation for revenue-critical operations. If your business operates in healthcare, financial services, insurance, legal, or any other regulated industry, the complete absence of SOC 2, HIPAA, and GDPR certifications makes Bolna AI a non-starter from a compliance perspective — no amount of cost savings justifies the liability exposure of routing sensitive conversations through an uncertified platform. If your customers are located outside India, particularly in North America or Europe, Bolna AI's India-centric telephony infrastructure, support coverage, and language optimization may create quality and reliability gaps that erode the caller experience. If you are an agency or SaaS company looking to offer voice AI under your own brand, the absence of a formal white-label program means you would need to build and maintain your own client-facing infrastructure on top of Bolna AI's API, a significant engineering investment with no guarantee of long-term platform stability. And if your team has experienced or is concerned about customer support responsiveness, Bolna AI's Trustpilot profile — three reviews, all one-star, citing no support, ignored refund requests, and no callback after booking a demo — provides a data point that should factor heavily into any vendor risk assessment.

Why Growing Teams Choose Ringlyn AI Over Bolna

The decision to switch from Bolna AI to Ringlyn AI — or to choose Ringlyn AI from the outset — is driven by a consistent set of priorities that become non-negotiable as businesses scale their voice automation operations beyond initial experiments. The first and most frequently cited factor is compliance readiness. As businesses grow, they inevitably encounter enterprise clients, regulated industries, or procurement processes that require vendors to demonstrate independently verified security and data handling practices. Ringlyn AI's HIPAA-compliant architecture with enterprise-grade encryption provides a defensible compliance posture that passes procurement reviews and satisfies audit requirements, while Bolna AI's absence of any publicly documented certifications creates a roadblock that no amount of technical capability can overcome. The second factor is support reliability. When voice automation is handling hundreds or thousands of customer conversations daily, a platform outage, a misconfigured call flow, or a billing discrepancy is not an inconvenience — it is a revenue-impacting event that demands immediate, accountable human support. Ringlyn AI provides priority support with dedicated account management, while Bolna AI's support track record, as documented by its own customers on Trustpilot, suggests a pattern of unresponsiveness that creates unacceptable operational risk for production-dependent businesses.

The third factor driving teams toward Ringlyn AI is global readiness. Bolna AI's strength in Indian vernacular languages and its partnerships with India-centric telephony providers like Exotel and Vobiz are genuine advantages for businesses operating within India, but they do not translate into reliable performance for organizations serving customers in North America, Europe, the Middle East, or the broader Asia-Pacific region. Ringlyn AI's multilingual support through ElevenLabs and Gemini voices covers dozens of languages with the natural intonation and cultural fluency that international callers expect, and the platform's telephony infrastructure is optimized for global coverage rather than regional specialization. The fourth factor is total cost predictability. Teams that initially chose Bolna AI for its low $0.02 per minute platform fee often discover that the true cost of operation — when factoring in separate STT, LLM, and TTS provider charges, the management overhead of multiple vendor relationships, and the unpredictable token consumption that inflates LLM costs — is not meaningfully lower than Ringlyn AI's all-inclusive pricing, and in many cases is actually higher when the hidden operational costs of managing a disaggregated multi-vendor stack are honestly accounted for.

Finally, the white-label opportunity represents a significant strategic consideration for agencies and SaaS companies evaluating these two platforms. The voice AI reseller market is growing rapidly as marketing agencies, BPO firms, and vertical SaaS providers recognize that offering AI voice agents as a service creates a high-value recurring revenue stream with strong client retention dynamics. Ringlyn AI's White-Label plan provides a turnkey path to launching a branded voice AI product within days, complete with custom domains, a white-labeled client dashboard, full brand removal, and Stripe rebilling integration that automates the entire billing relationship. Bolna AI offers no equivalent program, which means agencies choosing Bolna AI must either build their own client-facing infrastructure from scratch — a multi-month engineering project with ongoing maintenance requirements — or simply forgo the white-label opportunity entirely. For agencies and SaaS companies where voice AI reselling is a strategic growth initiative, this capability gap alone can justify the platform decision.

“We initially chose Bolna AI because the $0.02 per minute platform fee looked unbeatable, and the open-source codebase gave our engineering team confidence in the transparency of the system. Three months in, our actual per-minute cost was averaging $0.11 after accounting for separate Deepgram, OpenAI, and ElevenLabs charges, and we had burned over 40 engineering hours managing API keys, debugging token consumption spikes, and troubleshooting latency issues that Bolna AI's support team never responded to. When we migrated to Ringlyn AI's Professional plan, our all-in cost dropped to a predictable $199 per month plus overages, our call completion rate improved by 28 percent thanks to lower latency and better voice quality, and we reclaimed our engineering team's time for product work instead of infrastructure maintenance.”

— Illustrative scenario based on common platform migration patterns

Getting Started: Switching from Bolna AI

1. Audit your current Bolna AI configuration: Document every active agent, conversation flow, API key dependency, telephony number, and integration workflow currently running on Bolna AI. Export all call logs, transcripts, and performance data you want to preserve. Catalog the STT, LLM, and TTS providers you are currently using along with their per-minute costs so you can accurately compare your true Bolna AI spend against Ringlyn AI's all-inclusive pricing.
2. Select a Ringlyn AI plan: Based on your monthly call volume, choose the tier that matches your needs. Most teams migrating from Bolna AI find that the Growth plan at $99 per month with 120 included minutes or the Professional plan at $199 per month with 300 included minutes provides equivalent or greater capacity at a lower total cost of ownership when compared to Bolna AI's disaggregated component pricing.
3. Recreate your agents in Ringlyn AI's no-code builder: Use the visual agent builder to reconstruct your conversation flows, branching logic, data collection fields, and call transfer rules. Unlike Bolna AI's developer-oriented configuration, Ringlyn AI's builder is designed for business users to configure complex multi-turn conversations without writing code or managing API integrations separately.
4. Activate native CRM integrations: Connect your HubSpot, Salesforce, or GoHighLevel account directly within the Ringlyn AI dashboard. These pre-built integrations replace the Zapier or Make.com workflows you maintained on Bolna AI, handling contact syncing, deal creation, activity logging, and workflow triggers automatically without any middleware configuration.
5. Test, validate, and go live: Select your preferred ElevenLabs or Gemini voice, run a series of test calls to validate conversation flows and CRM data accuracy, then port your existing phone numbers to Ringlyn AI's telephony infrastructure. The support team assists with number porting and can run your new agents in parallel with your Bolna AI setup during the transition to ensure zero downtime.

Ringlyn AI's onboarding team provides hands-on migration assistance for businesses switching from any competitor platform, including Bolna AI. Unlike the support experience documented in Bolna AI's Trustpilot reviews, Ringlyn AI assigns a dedicated account contact who understands your specific use case, call volume requirements, and integration architecture, and who is accountable for ensuring your migration is completed successfully. The typical migration timeline from Bolna AI to Ringlyn AI is one to two weeks for standard deployments with straightforward conversation flows and CRM integrations. More complex configurations involving multiple agents, advanced branching logic, or integrations beyond the three natively supported CRMs may require three to four weeks. During the transition period, Ringlyn AI's team can run your new agents in parallel with your existing Bolna AI setup, allowing you to compare call completion rates, voice quality feedback, latency metrics, and CRM data accuracy side by side before fully cutting over. This parallel-run approach eliminates migration risk and gives your team data-driven confidence that the new platform meets or exceeds every performance benchmark established on your previous platform.