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Klearly

Klearly is positioning as a series a horizontal AI infrastructure play, building foundational capabilities around agentic architectures.

series aHorizontal AIGenAI: coreklearly.com
$14.0Mraised
Why This Matters Now

As agentic architectures emerge as the dominant build pattern, Klearly is positioned to benefit from enterprise demand for autonomous workflow solutions. The timing aligns with broader market readiness for AI systems that can execute multi-step tasks without human intervention.

Klearly powers B2B revenue teams with the visibility and confidence to know where, when, and how to grow revenue.

Core Advantage

Klearly’s unique advantage is its focus on actionable, prescriptive insights that unify marketing and sales data, enabling revenue teams to know exactly where, when, and how to act for growth.

Agentic Architectures

high

Klearly (DialOnce) implements agentic architectures through autonomous AI agents capable of multi-step reasoning, tool use (e.g., routing to live agents, integrating with CCaaS/CRM), and orchestrating customer journeys across channels.

What This Enables

Full workflow automation across legal, finance, and operations. Creates new category of "AI employees" that handle complex multi-step tasks.

Time Horizon12-24 months
Primary RiskReliability concerns in high-stakes environments may slow enterprise adoption.

Continuous-learning Flywheels

high

The system leverages continuous training and feedback loops to improve intent recognition and agent performance, using real-time KPIs and customer interactions to refine models.

What This Enables

Winner-take-most dynamics in categories where well-executed. Defensibility against well-funded competitors.

Time Horizon24+ months
Primary RiskRequires critical mass of users to generate meaningful signal.

Vertical Data Moats

high

Klearly/DialOnce leverages proprietary, industry-specific datasets and deep domain expertise in sectors like banking, insurance, and transport to create a defensible data moat.

What This Enables

Unlocks AI applications in regulated industries where generic models fail. Creates acquisition targets for incumbents.

Time Horizon0-12 months
Primary RiskData licensing costs may erode margins. Privacy regulations could limit data accumulation.

RAG (Retrieval-Augmented Generation)

medium

The platform hints at retrieval-augmented generation by surfacing context-aware answers and recommendations for agents, likely combining retrieval from knowledge bases with generative responses.

What This Enables

Accelerates enterprise AI adoption by providing audit trails and source attribution.

Time Horizon0-12 months
Primary RiskPattern becoming table stakes. Differentiation shifting to retrieval quality.
Competitive Context

Klearly operates in a competitive landscape that includes Clari, Gong, People.ai.

Clari

Differentiation: Klearly emphasizes actionable insights for where, when, and how to grow revenue, with a focus on marketing and sales alignment, while Clari is more focused on sales forecasting and pipeline management.

Gong

Differentiation: Gong is conversation intelligence-centric, analyzing calls and emails, while Klearly focuses more broadly on revenue team actions and marketing/sales data to drive growth recommendations.

People.ai

Differentiation: People.ai is heavily focused on activity capture and CRM enrichment, while Klearly positions itself as providing confidence and visibility into where and how to grow revenue, with a more prescriptive approach.

Notable Findings

Klearly (DialOnce) leverages a sovereign, France-hosted AI infrastructure, emphasizing data residency and compliance—a rare technical choice in global AI agent deployments, especially for regulated industries.

The platform integrates Visual IVR to redirect inbound phone calls to digital channels (chatbot, AI agent, FAQ, etc.), blending telephony and digital customer service journeys in real time. This hybrid orchestration is technically complex and not commonly seen in most AI agent platforms.

Their AI agent architecture combines generative AI with proprietary NLU and continuous disambiguation mechanisms, focusing on high intent understanding rates and sector-specific adaptation. This layered approach goes beyond standard chatbot frameworks.

Deep integrations with CCaaS (Contact Center as a Service) and CRM systems (Salesforce, Genesys, Kiamo, etc.) enable automated post-call summaries, mail classification, and advisor assist features—indicating hidden complexity in workflow automation and context sharing across channels.

The system is designed for omnichannel orchestration, maintaining context and seamless handoff between digital and voice channels, including live advisor escalation with full context transfer. This cross-channel context persistence is technically challenging and rare.

Risk Factors
overclaimingmedium severity

The marketing is heavy on buzzwords such as 'AI-powered', 'generative AI', 'sovereign AI', and 'next-generation agent', but lacks technical specifics about the underlying models, architectures, or proprietary technology. There is frequent mention of 'intent understanding' and 'omnichannel orchestration', but no concrete details on how these are achieved or what differentiates their approach from standard LLM integrations.

no moatmedium severity

While the company claims a 'vertical data moat' and industry-specific expertise, there is little evidence of a defensible data advantage or technical differentiation. The platform appears to be a service layer integrating with existing CCaaS/CRM systems and leveraging generic AI capabilities, which could be replicated by competitors or incumbents with access to similar APIs and integration partners.

feature not productmedium severity

Many of the highlighted capabilities (Visual IVR, mailbot, agent assist, post-call summary) are features that could be absorbed by larger platforms or added by incumbents. The product positioning seems to rely on bundling these features rather than presenting a platform with a clear, defensible scope.

What This Changes

If Klearly achieves its technical roadmap, it could become foundational infrastructure for the next generation of AI applications. Success here would accelerate the timeline for downstream companies to build reliable, production-grade AI products. Failure or pivot would signal continued fragmentation in the AI tooling landscape.

Source Evidence(12 quotes)
"Powered by generative AI, they accurately understand natural language and enable your users to find answers autonomously (self-care), reducing the number of requests directed to your support teams."
"Our conversational agents go far beyond a simple chatbot and provide immediate, personalized assistance across all your channels."
"Agent Assist: streamlined access to information and answer recommendations based on knowledge and collected context"
"Mailbot: automatic classification of incoming emails (including attachments) and answer generation"
"Automatic Call Summary: instant post-call summary generation."
"Thanks to disambiguation mechanisms, the combination of NLU with generative AI, and continuous training, DialOnce’s conversational agents and chatbots achieve best-in-class intent understanding rates, significantly outperforming a traditional chatbot."