Chamelio Raises $26M Series A to Build an AI-Native Operating Layer for In-House Legal Teams

Chamelio, an AI-native legal technology company focused on in-house legal departments, has raised $26 million in Series A funding as it looks to challenge traditional contract lifecycle management (CLM) systems with software capable of not only organizing legal work, but increasingly performing it.

The round was led by Entrée Capital, with participation from existing investors Work-Bench and Emerge Ventures, as well as Bright Pixel Capital. Chamelio says it now serves hundreds of customers, including Wiz, monday.com, Socure, AppsFlyer and Wonderful.ai.

Founded in 2024 by Alex Zilberman, Gal Lellouche and former general counsel Gil Banyas, Chamelio previously announced seed financing in January 2026 that brought its total funding at the time to $10 million. Combined with the new Series A, the company has now announced roughly $36 million in funding. Chamelio also says its annual recurring revenue has increased fourfold since its seed financing.

The new capital will be used to expand Chamelio’s product and legal engineering teams, improve its onboarding technology and continue developing what the company describes as its proprietary “legal action model.”

From Contract Storage to Legal Action

For years, much of the enterprise legal technology market has revolved around CLM platforms designed to centralize contracts, manage approvals and track documents through different stages of their lifecycle.

Chamelio is betting that generative and agentic AI will fundamentally change what companies expect from that software.

Rather than treating the contract repository as the final destination, the company describes its platform as a system of action that turns previous contracts, negotiation history, internal policies and legal decisions into context that can be used when new work arrives.

One component, Chamelio Negotiate, works inside Microsoft Word and uses a company’s previous negotiations and playbooks to assist with contract review and redlining. The platform can identify deviations, flag potentially problematic terms, compare documents and propose edits based on the legal department’s established approach rather than relying solely on generic legal knowledge.

Its repository layer automatically extracts and structures contract information, while reminders and automations can track renewals, obligations and other deadlines. Legal teams can then query information across multiple documents instead of manually locating and reading individual agreements.

That distinction is increasingly important as legal AI evolves from tools that answer questions toward software expected to participate directly in business processes.

Why In-House Legal Teams Are a Natural Target for Agentic AI

The timing reflects growing pressure on corporate legal departments.

The Corporate Legal Operations Consortium’s 2026 State of the Industry Report found that workloads are increasing particularly quickly in areas such as regulatory compliance and cybersecurity, even as hiring and spending expectations weaken. Only 32% of surveyed legal departments expected attorney headcount to increase, while 85% reported having dedicated AI oversight or resources.

The Association of Corporate Counsel found a similar tension. Its 2026 Chief Legal Officers Survey reported that 35% of respondents identified budget and resource constraints as their top barrier to success, while operational efficiency remained the most frequently cited strategic initiative.

That creates an unusually compelling environment for legal automation. Unlike some applications of enterprise AI where efficiency gains can be difficult to quantify, corporate legal departments regularly handle highly repetitive processes involving NDAs, vendor agreements, procurement requests, employment documents and standardized commercial contracts.

The question is increasingly whether those processes need a lawyer involved at every individual step.

Agents That Can Execute Multi-Step Legal Workflows

Chamelio’s answer is to introduce agents capable of moving work through several stages rather than limiting AI to isolated drafting or summarization tasks.

The company’s website gives an example of an agent receiving a contract, determining its value and risk tier, routing it through appropriate approvals, coordinating legal review and redlining, collecting signer information, performing a final compliance check and eventually preparing the agreement for signature.

That is a considerably broader role than the legal copilots that initially defined generative AI adoption in the sector.

Chamelio also connects legal processes with the other systems employees already use. Its platform can ingest documents from shared drives, interact through communications channels and email, and connect contract information with CRM systems. The company says deployments can also integrate with platforms including Salesforce, Slack, NetSuite and procurement systems.

This matters because a contract rarely exists entirely within the legal department. A sales agreement may originate inside a CRM, require approval from finance, trigger a security review, involve procurement and ultimately create obligations that another business unit must fulfill months later.

Agentic legal software becomes much more useful when it can coordinate those steps rather than simply analyze the underlying document.

Keeping Lawyers in the Loop

Greater autonomy also introduces a more difficult problem: determining when an AI system should act independently and when a human lawyer should intervene.

Chamelio’s model is designed to allow companies to automate routine or lower-risk work while escalating exceptions and more consequential decisions to legal staff.

That architecture could prove particularly important in corporate legal environments, where a system making a technically plausible but contextually wrong decision may create substantially greater consequences than a mistaken answer from a general-purpose chatbot.

Company-specific context is therefore central to Chamelio’s approach. The platform uses contracts, historical redlines, playbooks and previous decisions to determine how a particular organization handles legal questions. Chamelio says AI answers can be traced back to their underlying source documents, while administrators can apply fine-grained access controls to contracts and data.

The company also says it is SOC 2 Type II compliant and that customer information is not used to train external or public AI models.

What Agentic Legal AI Could Change

The broader impact of agentic legal AI may be a shift in how work is divided between lawyers and software.

If AI systems can reliably handle routine contract review, approvals, obligation tracking and other repeatable tasks, in-house lawyers could spend more time on negotiation, regulatory interpretation and higher-risk decisions.

That shift will also create new governance challenges. Legal teams will need clear rules for when AI can act autonomously, when human approval is required and how automated decisions are audited.

Over time, this could push contract lifecycle management beyond document storage and workflow tracking toward platforms that combine legal knowledge, automation and AI agents. The key question will be whether these systems can operate reliably across complex legal processes without weakening oversight or accountability.