OneByZero Raises $20M to Scale Enterprise AI in APAC

OneByZero builds for the moment after an AI pilot wins approval. The model still has to enter a bank, telecommunications provider, or retailer, connect to systems that were never designed for autonomous software, follow local controls, leave an audit trail, and return judgment to a person when the workflow reaches its boundary.

The Singapore company announced a $20M Series A on October 5, 2026, led by Jungle Ventures. It is OneByZero's first external financing, and the capital is intended to expand forward-deployed teams across nine existing markets, establish a local presence in Japan, enter additional regulated industries, and accelerate the NEO platform that sits behind its enterprise AI deployments.

What OneByZero Raised

The $20M Series A gives OneByZero its first outside capital after three years of building with enterprise customers. The company did not disclose its valuation, other participating investors, Jungle Ventures' check size, ownership terms, board changes, or the legal close date, so the verified capital record is intentionally narrow: $20M, Series A, led by Jungle Ventures, announced October 5.

OneByZero is headquartered in Singapore and says it operates in Australia, India, Indonesia, Malaysia, the Philippines, Singapore, Thailand, the United States, and Vietnam. The new funding will support more engineering and customer coverage in those markets, a Japan team, and expansion beyond financial services and telecommunications into conglomerates, healthcare, and the public sector.

The Work Begins Where the Demo Ends

CEO and co-founder Niket Vaidya and CTO and co-founder Vibhore Kumar have built OneByZero around a forward-deployed model. Engineers work alongside customer teams to find a valuable workflow, integrate AI with the data and software already running the business, move the system into production, and remain involved as the deployment changes.

That proximity matters in regulated enterprises because the buying decision is larger than model quality. A production system needs permissions, escalation rules, evaluation, auditability, local operating knowledge, and a person who remains accountable when software reaches the edge of its authority. OneByZero is selling the engineering work that establishes those conditions as much as it is selling the agent that performs the task.

The company calls those agents AI Coworkers. Its NEO platform is designed to govern them through role-based access, audit trails, kill switches, evaluation loops, enterprise connectors, and human approval inside the customer's AWS environment. The commercial argument is that each deployment should leave behind reusable infrastructure and industry knowledge, allowing the next deployment to begin with more than a blank services statement of work.

The Evidence Is Promising and Company-Reported

OneByZero says its revenue has more than doubled annually for the past three years. It also says some deployments automate more than 90% of customer-facing interactions and that complex data-modernization projects have been delivered 50% faster. Those figures appear in the company's funding announcement and production case studies, but they are not audited financial results or independently verified customer benchmarks.

The distinction strengthens the record because the available evidence still shows where the company has been working. OneByZero's published cases span telecommunications, banking, retail, cloud migration, customer service, campaign operations, and data modernization across Asia Pacific. Several customers remain unnamed, while disclosed relationships and current company materials show that the platform is being positioned for environments where data residency, governance, and legacy integration decide whether an AI project reaches production.

Why Jungle Ventures Backed the Deployment Layer

Jungle Ventures Managing Partner Yash Sankrityayan framed the investment around execution. OneByZero has spent three years deploying inside large organizations while much of the enterprise AI market has remained concentrated on experimentation, and the investor believes that experience matters as companies move AI into core operations.

That thesis places value on the unglamorous middle of enterprise adoption. Foundation models are broadly available, but access to a capable model does not resolve a bank's permissions, a telecommunications provider's multilingual customer workflows, or a retailer's mixture of old systems and new data. The same production gap appears across the market in agent authorization and enterprise AI management; OneByZero is attacking it through embedded delivery plus a control platform.

OneByZero's founders know the trade-off. Embedded engineering can build trust, operating knowledge, and switching costs, but it can also scale with headcount unless the platform captures what each engagement teaches. The Series A is therefore financing both sides of the model: more people close to customers and more reusable infrastructure inside NEO.

Japan Will Test Whether the Model Compounds

Japan is a consequential next market because local presence, enterprise relationships, language, procurement, and governance all influence adoption. OneByZero plans to build a local team there rather than sell the same delivery motion from Singapore, while its careers operation already spans engineering, data, AI research, delivery, sales, and strategy.

The company also plans to build industry templates and help enterprises deploy open-weight and smaller language models. That work could make the deployment layer more repeatable across customers that want control over data, cost, and model choice. It also pushes OneByZero toward sectors where the consequences of weak governance become visible quickly, particularly healthcare and the public sector.

The $20M round gives Vaidya, Kumar, and their team room to expand the part of enterprise AI that customers cannot outsource to a demo. The next phase will be measured in how much of today's close-in engineering becomes tomorrow's reusable control layer, and whether each new market adds institutional knowledge to NEO faster than it adds complexity to the teams responsible for keeping the systems running.