What Happened
Thinkrr.ai issued a press release presenting Cody Getchell, its Chief Marketing Officer and part-owner, as a recognized authority in AI voice technology, following his participation at the Kicking SaaS Summit Costa Rica 2026. The company describes itself as providing AI voice solutions that enhance digital communication, improve user engagement, and streamline software workflows for businesses.
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Why This Matters
Thinkrr.ai's announcement positioning Cody Getchell as a recognized authority in AI voice technology, following his engagement at the Kicking SaaS Summit Costa Rica 2026, underscores how much thought leadership now matters in a crowded software and AI voice technology market. It is worth being clear about what the release is: a marketing and leadership announcement. It highlights Getchell's more than 15 years of marketing experience and his role guiding the company's AI voice initiatives (Business Insider), but it contains no product metrics, customer results, or reliability figures. Thinkrr's own site positions the company as "the always-on voice layer for agencies, businesses, and pharmacies," with agents that answer calls, follow up on leads, and book appointments (Thinkrr.ai). The announcement still signals something real: voice AI vendors increasingly compete on executive visibility and credibility, not only on features, as the category moves from experimental tools toward production use. Visibility at events like the Summit can build investor confidence and partner ecosystems, which may speed consolidation around scalable voice platforms.
For enterprise businesses evaluating AI, the useful question is not which vendor has the most visible executives but whether a platform can automate customer-facing workflows reliably. Thinkrr.ai's tools, such as voice agents for inbound calls, outbound follow-up, and booking, aim at exactly those workflows. Enterprises, particularly in regulated sectors like banking and healthcare, weigh compliance (e.g., PCI DSS for financial voice interactions and HIPAA for patient engagement), integration ease, and scalability. Uptime and setup speed matter too, and buyers should ask any vendor for evidence on both rather than relying on announcements. Interaction quality deserves close attention in any pilot: callers notice when a conversation does not feel natural. Getchell's profile may reassure some risk-averse CIOs, but vendor selection should rest on a pilot with your own calls. Visible thought leaders help a platform get shortlisted in RFPs; they do not replace proof of performance.
The news fits broader trends in AI democratization and multimodal interfaces, signaling a shift from text-based chatbots to voice-first experiences. The Summit's focus on automation, AI solutions, and leadership strategies reflects the industry's pivot toward "voice AI employees" for 24/7 operations. It also signals rising demand for no-code voice tools amid labor shortages. From a contrarian perspective, skeptics argue this hype risks overpromising on speech recognition accuracy in noisy environments, which is why testing on real call audio matters before any rollout.
Historically, this fits into the AI narrative's evolution from rule-based IVR menus to generative voice models that can hold real-time, context-aware conversations. Earlier voice products that struggled to scale are a reminder that reliability and measurable outcomes, not visibility, decide which platforms last.
Implications vary by business size and sector. SMBs gain most from accessible tools that automate sales and support without a development team. Mid-market firms in e-commerce can use voice to keep customers engaged after hours, while enterprises in banking (e.g., PCI DSS compliant voice authentication) and healthcare prioritize data sovereignty. Sectors like SaaS and real estate benefit from website voice widgets for lead generation, in contrast to manufacturing's slower adoption due to legacy systems. Integration costs for large deployments argue for phased pilots.
Competitors like SoundHound, Cerence, and PolyAI may put more weight on executive storytelling to counter momentum like Thinkrr.ai's. More useful, for them and for buyers, would be published reliability data, quantified customer outcomes, and simpler setup. Multiple perspectives apply: incumbents may view this as niche hype, but startups see validation for aggressive marketing.
Long-term, this points toward voice AI as a core layer in unified agent platforms, fostering hybrid human-AI workforces. Industry-wide, expect M&A activity and emerging standards for ethical voice (bias mitigation) and interoperability. Contact centers are likely to automate more routine work, which would reshape the BPO industry; however, regulatory scrutiny, including the EU AI Act, requires compliant innovation, and thought leaders like Getchell can help shape resilient ecosystems.
Agxntsix Expert Perspective
Thinkrr.ai's announcement spotlighting Cody Getchell as an AI voice authority and its CMO is a commendable milestone, particularly following his participation at the 2026 Kicking SaaS Summit in Costa Rica. "AI voice technology is rapidly reshaping digital interaction," Getchell said in the release, and the company's approach reflects that view: its site emphasizes fast, no-code setup, a white-label offering for agencies that runs inside GoHighLevel, and agents for businesses and pharmacies (Thinkrr.ai). This recognition underscores Getchell's mix of marketing experience and AI voice leadership.
What drives Thinkrr.ai's approach is a deliberate emphasis on simplicity and speed: AI agents that answer calls, follow up on leads, and book appointments around the clock without complex setups. By syncing with the CRM and triggering workflows, they aim to eliminate voicemails and manual follow-ups, and by putting voice on the website they turn it into a proactive channel for revenue moments. This approach resonates in a market shifting from rigid chatbots to voice-driven responses, where unanswered calls represent lost opportunities.
Agxntsix approaches the same problem from the enterprise side. Plug-and-play tools optimize for speed of setup; enterprise deployments also have to fit regulated workflows, existing systems, and internal controls.
Agxntsix's focus is custom AI training on your business data, real-time analytics dashboards, and customization for regulated industries, so the agent works within the rules your business already follows.
Market timing matters. Thinkrr.ai's momentum signals a maturing market, and enterprises that wait risk falling behind competitors who are already automating routine calls. Speed should not replace rigor, though: choose a solution that can scale, and prove it on your own call data first.
Agxntsix's methodology, the AI-First Enterprise Framework, applies directly here. It starts with a business knowledge upload to train agents on your operations, then layers in predictive analytics for sentiment-driven routing, multi-channel orchestration (voice, SMS, email), compliance auditing, and clear escalation paths to humans. This mirrors the always-on vision Thinkrr.ai describes but adds the controls enterprises need.
For immediate action, enterprises should: (1) Book a free AI business audit at agxntsix.ai/audit; (2) Ask every vendor you shortlist, including us, to show how its deployment meets the HIPAA or PCI DSS requirements that apply to your industry. Contact Agxntsix at contact@agxntsix.ai or through agxntsix.ai/contact.
What Enterprise Leaders Should Do Now
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Assess business requirements for Voice AI deployment by conducting stakeholder interviews and mapping current processes. Identify high-value use cases like customer service automation. Establish baseline metrics such as average handle time and first call resolution rate, and use these to measure ROI post-implementation. Start with a 2-week assessment phase involving cross-departmental teams to prioritize workflows like after-hours support.
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Evaluate technical infrastructure for Voice AI compatibility, including network bandwidth and server capacity for peak usage. Load-test with realistic concurrent call volumes and target sub-second response times. Conduct a full audit of CRM, ERP, and telephony systems for API integration readiness, and compare the costs of cloud and on-premises deployment. Engage IT teams in a 1-month evaluation to spec out upgrades and select hybrid cloud providers.
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Analyze call volume data to identify repetitive interactions suitable for Voice AI automation. Focus on routine queries like appointment scheduling, and test how accurately an AI agent handles them before committing. Calculate your current cost per interaction and model the savings case from your own data; benchmark using call analytics software. Implement over 4 weeks by reviewing 3 months of historical data and tagging the top 10 query types.
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Audit after-hours call coverage to capture missed opportunities. Estimate the revenue lost to missed calls and compare it with Voice AI costs. Pilot after-hours coverage on 20% of calls initially, then roll out in phases starting with low-volume nights, monitoring via real-time dashboards.
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Calculate total cost per customer interaction, including agent salaries, turnover, and facilities. Break down costs across direct and indirect categories and model AI ROI using spreadsheets. Conduct this audit quarterly, integrating with ERP for automated tracking.
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Design integration architecture linking Voice AI to CRM systems like Salesforce or HubSpot for contextual responses. Integration lets the agent use customer history to resolve calls and spot upsell opportunities; ensure PCI DSS compliance for banking. Map APIs for real-time data access (customer history, inventory) within 2 weeks. Test in staging environments mirroring production, validating with 100 simulated calls.
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Prepare training data using historical conversations to customize language models for industry terminology. Include the accents and dialects relevant to your customer base, and measure recognition accuracy and escalation rates on held-out calls. Curate data over 3 weeks, anonymizing it to meet HIPAA and SOC 2 requirements, then iterate with vendor support.
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Launch a limited-scope pilot program targeting one department or use case like order status checks. Track KPIs like response accuracy and CSAT, and set pass thresholds before launch. Limit the pilot to 10% of call volume for 4 weeks, collecting feedback via post-call surveys. Use the insights to refine before scaling enterprise-wide.
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Monitor key performance indicators continuously, including first-call resolution, CSAT scores, and cost per interaction. Deploy analytics dashboards integrated with Voice AI platforms for real-time alerts. Review metrics weekly in the pilot phase and monthly after deployment.
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Implement a phased rollout, department by department, to minimize disruptions. Begin with one function, such as sales, then extend to support; train staff in 2-week sessions. Provide go-live support with 24/7 vendor monitoring for the first 30 days.
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Develop change management and training programs to drive Voice AI adoption. Create role-specific guides for agents and managers and simulate 50 scenarios; address resistance via town halls. Roll out training 2 weeks before the pilot, with ongoing quarterly refreshers and support hotlines.
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Optimize Voice AI agents post-deployment through A/B testing and monthly analytics reviews. Test new flows quarterly based on failure patterns (e.g., accent errors), and update for regulations like GDPR. Assign a dedicated optimization team that meets bi-weekly with your vendor.
The Bottom Line
Thinkrr.ai's announcement is another sign that voice AI is moving into the mainstream. For enterprises, the test stays the same: measure a baseline, pilot on real calls, and scale what the results support.
For Dallas-area enterprises and national organizations: Don't wait for your competitors to implement Voice AI first.
Agxntsix is an Enterprise Voice AI company. Contact us at https://agxntsix.ai to learn more.