Your enterprise AI agents should automatically remember which model is right for which task. Mindstone built the capability with Rebel

AI agent orchestration platforms are popping up like weeds these days, but London-based AI transformation startup Mindstone’s Rebel might be among the most promising I’ve come across.

That’s because the system, which officially launched this week, is a local-first, agentic AI operating system distributed under a “Fair Source” license, allowing teams of under 100 users to freely adopt and customize it to suit their needs, while those organizations with more users will require paying for an enterprise license.

The marquee features are its simplicity and extensive customizability to fit any given team, no matter how unique or specific the workflows, all based around the common, open source standard file format markdown, and, as a result, an organizational memory layer that ensures agents reliably use the enterprise’s preferred AI models for each given task or even subtasks — dynamically switching between local and cloud ones in a predictable, visible way to save costs and maintain data privacy and security as needed.

“Shared memory is the most empowering thing you could possibly do with a knowledge-worker AI,” said Greg Detre, chief technology officer (CTO) of Mindstone, in a recent video call interview with VentureBeat. “You get this feeling of being a super-organism as a company that just gets smarter and smarter.”

Rebel is available now for macOS on Intel and Apple Silicon machines, as well as Windows, with Linux support in development.

Mindstone has raised $5 million from private investors including Pearson Ventures, Moonfire Ventures and Zanichelli Venture.

A distinctive, local-first architecture based on markdown files

What makes Rebel distinctive is its local-first architecture.

Instead of the approach found in developer-heavy agent frameworks such as as LangGraph, CrewAI and AutoGPT, which require teams to wire together databases, cloud infrastructure and state-management logic, Rebel’s core agent memory and instructions live across local markdown (.md) text files — arguably the simplest, easiest, and most popular way to steer AI agents, one that has been widely adopted by AI developers and power users around the globe.

Mindstone says Rebel stores its state, prompts, task instructions and memory hierarchy in these files, allowing users and companies to easily inspect, move or modify them as needed. A primary configuration file, agents.md, acts as the agent’s core instruction layer and runtime boundary.

That architectural choice is partly about cost. Mindstone argues that common office formats such as Word documents and PDFs often carry formatting and metadata overhead that consumes model token context and raises API costs. Markdown keeps the information closer to raw text, allowing more of the model’s context window to be spent on the actual task rather than document structure.

The company also positions the approach as a hedge against vendor lock-in. If a company’s agent instructions, automations and memory are stored locally as text files, they are not trapped inside one SaaS provider’s interface or database. That matters more as enterprises begin giving AI systems broader access to email, calendars, documents and internal workflows.

Rebel also lets users create repeatable AI workflows. “Skills” are saved multi-step procedures an agent can reuse. “Operators” adjust how the agent behaves for a given task, such as reviewing a pitch deck from an investor’s perspective or evaluating work through a security lens. “Automations” can run scheduled background tasks, such as scanning messages or files, finding relevant updates, drafting responses, or preparing work before an employee opens the app.

Automatically selecting the best, enterprise-preferred AI model for every task (and subtask)

Another important feature is multi-model orchestration. Rebel can break a task into parts and route different steps to different models, including splitting between local and cloud-based ones depending on the sensitivity of the information or as guided by enterprise policies.

A more powerful model can handle planning or complex reasoning; a cheaper model can handle routine work; a local model can handle sensitive steps or approval checks. This matters for enterprises that want flexibility or are seeking cost controls: not every task need be sent to the same expensive cloud model, and some enterprise workflows prohibit sensitive corporate data leaving local infrastructure.

“I want to be able to say, ‘Help me with this,’ and it knows what’s personal, what’s sensitive, and what can be shared with the whole company,” Detre explained.

That model-agnostic setup gives companies more control over cost and security. Data-heavy work can run on lower-cost models such as Llama or DeepSeek. Higher-level reasoning can be reserved for more expensive models. Sensitive work can be routed through a local model running on the user’s machine, keeping that information from leaving the device.

This approach also gives enterprise teams a way to mix cloud and local inference without treating the choice as all-or-nothing.

By shifting away from centralized, monolithic cloud interfaces toward a local file-driven architecture, Mindstone is introducing a model for how enterprise technical decision-makers orchestrate autonomous workflows without forfeiting data sovereignty or predictability

How it works in practice

Mindstone CTO Greg Detre designed Rebel’s memory system to avoid a common problem in enterprise AI: dumping large amounts of company information into a database and hoping search will retrieve the right context later.

Instead, Rebel uses a tiered memory structure. When an interaction happens, the system estimates how likely that information is to be useful again.

Information with a high expected value is written into a local readme.md file tied to a specific project space. Information with a moderate expected value becomes a reference link back to deeper historical records.

Lower-priority material is stored in an indexed memory directory, where it remains available but dormant until a relevant task calls it back.

An ROI dashboard for enterprise buyers

For larger organizations, Mindstone Pro adds an Impact Dashboard designed to show where Rebel is saving time and money across business units.

Mindstone says the dashboard uses a separate, closed LLM to evaluate telemetry and calculate business impact. The company says the system is calibrated conservatively, using the lower end of estimated performance gains to avoid inflated productivity claims.

That feature speaks to a practical problem for enterprise AI buyers: proving value without over-surveilling employees. Mindstone says the dashboard is isolated from individual workspaces, allowing IT and business leaders to evaluate adoption and return on investment without reading employees’ private agent activity.

Fair Source licensing aims to reduce platform risk

Mindstone is releasing Rebel under a Fair Source license, a model meant to sit between fully closed SaaS and permissive open source.

Under the license, Rebel’s code is viewable, auditable, modifiable and deployable. Individuals and organizations with up to 100 concurrent users can run it for free. Once an organization exceeds that threshold, it needs a commercial Mindstone Pro license.

The license also includes a two-year sunset clause. Twenty-four months after a given version is released, that version automatically converts to the MIT open-source license.

For enterprise buyers, the practical pitch is that Rebel reduces the risk of being trapped. If every automation, memory file and agent instruction is stored locally in markdown, a company can move its data and workflows elsewhere if needed. The product may be commercial, but the underlying work is designed to remain inspectable and portable.

Security questions focus on local approvals and shared memory

Rebel’s debut on the open access tech product sharing platform Product Hunt this week prompted technical questions about how a local-first agent should handle permissions, safety checks and shared memory.

One developer, Nikita Pokryschko, asked whether approval checks for sensitive actions could run entirely on a local model, or whether the gating logic still required a cloud call.

Detre responded by explaining Rebel’s separation between planning, execution and background safety logic. Wöhle added that companies can configure Rebel to rely entirely on a local model for gating decisions.

That distinction matters for corporate security teams. Autonomous agents often need broad permissions to read files, draft emails or interact with internal systems. If the final approval layer depends on an external cloud model, some companies may see that as a compliance risk. Mindstone is arguing that Rebel can keep those approval boundaries local.

A second discussion focused on how Rebel decides what memory can be shared. Product developer Clement Morel asked whether shareability is determined by content, user settings or learned behavior, and what happens if the system gets it wrong.

Detre said Rebel uses the user’s local “Chief-of-staff README” and defined spaces to separate private, team and company-wide information. When the agent encounters ambiguous context, the system pauses and asks the user for approval before proceeding.

That emphasis on visibility is part of Mindstone’s broader argument against opaque agent systems. As CEO Joshua Wöhle put it in a post on his LinkedIn account: “If an agent is going to sit inside your workspace, remember your context, and ask permission before changing the world, you should be able to see how it works. Not because everyone will read the code, but because someone can.”

Mindstone points to customer rollout as early proof

Mindstone says Rebel has already been deployed across the 250-person workforce of customer Epignosis, covering sales, engineering, product, finance and customer success teams.

“The entire organization is operating on Rebel today,” Wöhle told VentureBeat.

Over a 12-week deployment, Mindstone says Epignosis recaptured the equivalent capacity of eight full-time roles. The company says adoption spread organically after employees saw colleagues automate time-consuming work, a pattern employees reportedly called the “potatoes effect.”

The Epignosis case is central to Mindstone’s argument that enterprise AI should not be treated as a set of isolated personal tools. Rebel’s shared-memory design is meant to let workflows move across teams and improve as more employees use them.

“The border between learning and doing is fading out – and that changes everything about how you scale,” Epignosis CEO Dimitris Tsingos said in a statement provided to VentureBeat by Mindstone.

Background on Mindstone

Mindstone Learning Limited, headquartered in London, launched in 2020 under the direction of CEO Joshua Wöhle, previously a co-founder of the digital child safety firm SuperAwesome. Originally positioned in the consumer education technology market, the company built a digital curation tool likened to a “Spotify for learning” that utilized compound learning methodologies.

However, following the widespread commercialization of generative artificial intelligence platforms between 2022 and 2024, Mindstone moved into business-to-business enterprise enablement. Leadership identified a critical “last-mile” barrier: while AI tools promised substantial productivity gains, traditional corporate training failed to equip the workforce to practically integrate them into daily operations.

Today, Mindstone functions as a comprehensive enterprise software and training ecosystem designed to maximize corporate return on investment for existing AI licenses. The product architecture systematically addresses different organizational tiers through highly contextualized, “live-fire” software applications rather than abstract slide presentations.

Financially, Mindstone utilizes a hybrid capitalization strategy that interweaves institutional venture capital from entities like Moonfire Ventures and Pearson Ventures with community-based equity crowdfunding on platforms such as Seedrs and Crowdcube.

Mindstone has successfully penetrated the enterprise market, securing commercial contracts with blue-chip corporations including The Home Depot, Hyatt Hotels Corporation, Pearson, and Ernst & Young.

Ultimately, Mindstone positions itself as the crucial antidote to corporate inertia, ensuring organizations establish the internal competency required to execute successful AI transformations.

Mindstone’s bet: enterprise AI needs shared memory, not more seats

Rebel arrives as companies are trying to move from AI experimentation to AI operations. The first wave of enterprise adoption centered on access: giving employees chatbots, copilots and model subscriptions. Mindstone is betting the next wave will center on coordination.

That means shared memory, reusable workflows, local control, flexible model routing and measurable business impact. It also means giving enterprises a way to inspect the systems they are being asked to trust.

The company’s challenge now is execution. Local-first software can be harder to manage than cloud SaaS. Shared memory raises governance questions. Multi-model routing adds complexity. And enterprises will still need proof that agentic workflows can deliver reliable productivity gains without creating security or compliance headaches.

But Mindstone is making a clear argument: buying AI seats is not the same as building AI infrastructure. Rebel is its attempt to turn scattered employee experiments into an operating layer for work.