Zencoder's AI Targets the 75% of Work Beyond Coding

SILICON VALLEY, Calif. – April 09, 2026 – Zencoder today launched Zenflow Work, an ambitious expansion of its AI orchestration platform designed to tackle the vast ocean of tasks that surrounds core work. While AI coding agents have made software engineers dramatically faster at writing code, Zencoder argues that this only addresses a fraction of their workload. The new platform targets the other 75 percent: the planning, coordination, reporting, and communication that consume the majority of a professional's day, not just for engineers, but for teams across the entire organization.

AI coding tools like Claude Code and Cursor have revolutionized software development, but coding itself often represents only a quarter of an engineer's time. The rest is a whirlwind of chasing information, attending meetings, managing project tickets, and writing updates. Zenflow Work aims to bring order to this chaos with goal-driven AI agents that operate across a company's existing software stack.

"Coding agents made engineers 10x faster at writing code. But there's actually more routine work outside of coding than there is in coding, with people spending three quarters of their time doing that," said Andrew Filev, CEO and Founder of Zencoder. "Zenflow Work is for the other 75 percent."

The 'Unseen Work' Revolution

Zencoder's central claim—that a vast majority of work is administrative or coordinative—is a reality that resonates across industries and is well-supported by productivity research. Reports from firms like McKinsey & Company consistently show that knowledge workers spend a substantial portion of their week on automatable tasks like email, data consolidation, and internal communication, rather than their primary, value-creating functions. Zenflow Work is engineered to reclaim that lost time.

The platform introduces multi-step automations that execute complex workflows across popular business tools. For an engineering team, this means an AI agent can automatically prepare a daily standup brief by scanning Jira for recent updates, grouping them by status, and delivering a concise summary before the day even begins. It can read pull requests merged during the week and draft user-facing release notes in Google Docs, transforming a half-day task into a ten-minute review. Similarly, it can compile project updates and draft stakeholder emails, ensuring communication doesn't fall through the cracks during busy periods.

But this challenge isn't unique to engineering. Zencoder emphasizes that every department has its own version of the "75 percent problem." Sales representatives spend hours drafting proposals instead of selling; marketing teams get bogged down in coordination rather than creation; and finance departments chase receipts instead of analyzing spending. Zenflow Work extends its capabilities to these areas, offering to draft sales proposals from email threads, generate marketing briefs from engineering updates, and scan for unapproved or wasteful SaaS subscriptions to cut costs.

The Economic Edge of AI Orchestration

Beyond automating tasks, Zencoder is making a compelling economic argument centered on its multi-model orchestration engine. The company claims its approach can make AI-driven workflows significantly more cost-effective. Internal benchmarks, conducted on a difficult subset of the SWE-bench Pro evaluation, suggest that Zenflow's architecture is approximately 2.7 times cheaper per resolved task than using a single, powerful frontier model for every step.

This efficiency is achieved by intelligently routing different stages of a workflow to the most appropriate AI model. For example, a complex planning stage might be handled by a sophisticated model like Anthropic's Claude Opus, while a more straightforward implementation or summarization task could be assigned to a faster, less expensive model like Google's Gemini Flash. The Zenflow orchestration layer manages this routing automatically, optimizing for both quality and cost without requiring manual intervention.

This multi-model strategy is supported by emerging research in the AI field. Studies have shown that multi-agent systems often outperform single models in reliability and accuracy by leveraging the strengths of different architectures. By building a system that can verify outputs across models, Zenflow also aims to combat the problem of "AI slop"—the proliferation of low-quality, unverified AI-generated content—which can create more work than it saves. The platform's deep integrations with tools like Jira, Linear, Notion, Gmail, and Slack are crucial, allowing the AI agent to not only read data but also write back, create documents, and coordinate actions across the digital workplace.

Redefining the Human-AI Partnership

With its ability to manage complex, cross-functional tasks, Zenflow Work points toward a future where the nature of professional work itself is redefined. The launch moves beyond the concept of AI as a simple tool and positions it as a collaborative partner. This aligns with broader analyses from institutions like the World Economic Forum and MIT, which suggest that AI is more likely to transform jobs by augmenting human capabilities rather than eliminating roles outright.

By offloading the repetitive, time-consuming aspects of a job, the platform promises to free up human talent to focus on more strategic, creative, and interpersonal work. A manager preparing for a performance review, for instance, can have an agent compile six months of an employee's project contributions from Jira and key discussion points from 1:1 notes, allowing them to enter the conversation with comprehensive data rather than relying on memory and impressions. This shift allows human workers to operate at a higher level, focusing on judgment, strategy, and connection.

Users interact with their Zenflow agents through familiar messaging apps like Slack and Telegram, reinforcing the feeling of having a capable assistant. This conversational interface allows for a dynamic partnership where a user can kick off a task, receive updates, and provide feedback to refine the AI's output. The platform's architecture, which has already earned Zencoder key security certifications like SOC 2 Type II and ISO 27001, provides the trust necessary for enterprises to deploy these agents across sensitive business functions.

As Zencoder enters a competitive market for enterprise automation alongside established players like Workato and Appian, its distinct focus on the "unseen work" and its cost-effective, multi-model engine serve as key differentiators. Zenflow Work is available now as a free update, offering a model-agnostic platform that supports leading AI from Anthropic, OpenAI, and Google, signaling a determined push to make comprehensive, intelligent automation an accessible reality for businesses of all sizes.