Level3AI Raises $13M to Scale APAC CX AI

Level3AI raised $13M led by Lightspeed to expand AI agents for enterprise customer engagement across APAC covering voice, email and chat.

Level3AI Raises $13 Million in Seed Round to Scale Enterprise AI Customer Service

Level3AI, a Singapore-based startup building AI assistants for enterprise customer engagement, has raised $13 million in a Seed round led by Lightspeed, with participation from BEENEXT, 500 Global, Sovereign’s Capital, and Goodwater Capital.
From Global Martech Alliance’s lens—where we focus on helping teams evaluate and adopt the right marketing and CX tools—this round is another signal that “AI agents” are moving from pilots into accountable, measurable enterprise deployments across APAC.

Funding snapshot: who backed Level3AI and why it matters

The Seed round was led by Lightspeed, joined by BEENEXT, 500 Global, Sovereign’s Capital, and Goodwater Capital.
Level3AI has said it will use the capital to accelerate research and development and expand across key Asia-Pacific markets.

Lightspeed is a multi-stage venture firm with a global footprint across regions including the U.S., Europe, India, Israel, and Southeast Asia, and it reports managing about $28B in assets under management on its platform.
That global scale matters because enterprise customer engagement is rarely a single-market problem—especially in APAC, where companies often operate across languages, regulatory environments, and service expectations that vary country to country.

For martech and CX leaders, the headline isn’t just “another chatbot startup raised money.”
The bigger story is that investors are increasingly betting on outcome-driven support automation: tools that integrate deeply, follow operational playbooks, and can be held to performance targets rather than demo-day promises.

The APAC enterprise shift: from chatbots to accountable AI agents

Customer service has become a frontline growth lever, not a back-office cost center, because support experiences directly affect retention, reviews, referrals, and even conversion on high-consideration purchases.
In that context, the old “scripted bot” model has clear limits: it may deflect FAQs, but it struggles when issues span systems (orders, refunds, bookings, identity checks) or require policy-aware decisioning.

Level3AI is positioning itself explicitly beyond basic chat automation by focusing on “enterprise-grade AI agents” that can handle end-to-end engagement across multiple channels.
The company frames the opportunity as delivering human-quality interactions at scale by combining modern language models with deep system integration and operational playbooks.

This positioning aligns with what we see across the broader martech landscape: enterprises are no longer impressed by a model’s fluency alone.
They care about controllability (can it follow policy?), observability (can we audit outcomes?), and integration (does it actually resolve issues or just talk about them?).

In other words, enterprise buying is shifting from “Which AI sounds smartest?” to “Which AI can be trusted in production?”
That’s the bar Level3AI will be measured against as it expands beyond early customers and into more complex deployments.

What Level3AI is building: multi-channel agents designed for real workflows

Level3AI is an AI-native company headquartered in Singapore that builds customer engagement agents across voice, email, and chat.
DealStreetAsia reports the company was founded in July 2024 by Harry Yu and Zachary Wang, with Yu serving as co-founder and CEO.​

On its website, Level3AI emphasizes that its agents are “built for APAC’s complexity,” citing localization across 16+ countries and 95+ languages.​
It also highlights a design goal of translating a company’s SOPs and brand voice into reliable “AI playbooks,” and integrating into both legacy and modern systems.​

This matters because most enterprise support failures aren’t caused by a lack of “nice wording.”
They happen when AI can’t correctly execute the next step: verifying a customer, pulling the right record, applying a policy exception, escalating with context, or completing an update inside the ticketing/CRM stack.

Level3AI also promotes a time-to-value message—claiming teams can unlock value at scale in under three months via a scope/build/deploy approach.​
According to the site, the process starts with scoping success metrics, then building a proof of concept trained on SOPs, and then deploying with a four-week free trial and a money-back guarantee.​

On customer traction, Level3AI’s materials and coverage reference deployments with brands including Carousell, GetGo, Yuu Rewards, and Carsome.
Level3AI’s website includes outcome-style claims such as reduced resolution time and improved agent efficiency in specific customer stories (presented as customer quotes and case metrics).​

From a GMA (tool-evaluation) viewpoint, these are the right proof points to watch—but they should be validated during procurement.​
If a vendor claims measurable outcomes, buyers should request definitions (how CSAT is captured, what “resolution time” includes, what baseline period is used) before treating those numbers as comparable across tools.

How Level3AI may use the funds: product depth, go-to-market, and trust

Level3AI has stated that the Seed capital will be used for R&D and to expand across key APAC markets.
That combination is important, because “enterprise AI agent” adoption tends to fail for two predictable reasons: the product isn’t operationally ready, or the go-to-market doesn’t match enterprise buying reality.

On the product side, deeper R&D typically means improving reliability, guardrails, integrations, and evaluation—especially for voice and email, which create different challenges than web chat.​
In practice, that often includes better policy enforcement, safer tool-calling behaviors (when the agent must execute actions in systems), and improved monitoring so ops teams can see why an interaction succeeded or failed.

On the market expansion side, APAC growth is not a single playbook.
Winning in one market doesn’t guarantee success in another, because contact-center maturity, language needs, customer expectations, and compliance requirements vary dramatically.

Coverage also notes that Level3AI differentiates with performance guarantees tied to metrics such as customer satisfaction or conversion rates, including refunding if agreed benchmarks aren’t met.​
If the company can maintain that stance as deployments scale, it becomes a strong buying signal—because it aligns incentives between the vendor and the enterprise.

Lightspeed’s partner Pinn Lawjindakul has been quoted describing Level3AI’s agents as delivering “human-level” customer experiences at enterprise scale and positioning the company to transform customer engagement in Asia-Pacific.
That statement matters less as marketing—and more as a hint of what Lightspeed expects: category leadership in a region where enterprise AI adoption is accelerating.

What enterprise teams should evaluate before adopting AI agents

At Global Martech Alliance, our core mission is helping teams discover, evaluate, and adopt tools with clarity—using comparisons, reviews, integrations, and real-world examples.​
So if your team is exploring “AI agents for customer engagement” (including vendors like Level3AI), here’s a practical evaluation checklist you can run in WordPress-friendly, procurement-ready terms.

• Define success metrics upfront: CSAT, first-contact resolution, handle time, cost per ticket, containment rate, revenue assist (for sales/support hybrids), and agent productivity.
• Demand integration clarity: Which systems are supported today (CRM, ticketing, order management, identity, payments), and what is “native” vs “custom build.”
• Validate channel readiness: Level3AI explicitly targets voice, email, and chat—so buyers should test each channel’s quality separately, not assume parity.
• Ask how SOPs become guardrails: Level3AI says it translates SOPs into operational playbooks, so request examples of policy enforcement, escalation logic, and exception handling.​
• Governance and auditability: Require logging, role-based access, redaction controls, and an escalation framework for high-risk intents (refunds, cancellations, identity, safety).
• Pilot with real complexity: Don’t pilot on FAQs only; include messy cases (partial refunds, multi-order issues, cross-language interactions, policy exceptions).
• Commercial model alignment: If a vendor offers performance guarantees or refund promises, confirm the exact measurement method and reporting cadence.

If Level3AI’s claims around rapid deployment, localization depth, and outcome guarantees hold up under rigorous testing, it could become a strong contender for APAC enterprises that need to scale support without sacrificing brand experience.
If not, the market will still benefit—because this wave of funding pushes vendors to compete on operational credibility, not just impressive demos.