The Hivemind learns how work really flows through your company, proposes the most efficient way to produce the same results from the same inputs, and proves it side by side before anything changes. Your people stay in charge of every decision that matters.
The models available today are already broadly capable. What they lack is your company: how the work actually gets done, who decides what, and why.
Most of what a company knows lives in people's heads and in exceptions no system records. A model that starts from zero gives generic answers.
Most steps were added for a reason, and some of those reasons expired years ago. Nobody inside the process is paid to find out which.
The people closest to a process are the ones best placed to improve it, and the ones with the least time and the most at stake.
When nobody can say which steps are controls, the safe choice is to change nothing. So nothing changes.
Same inputs. Same outputs. The most efficient path between them, found from first principles and proven before you switch.
Connect to the systems where the work happens and learn the real process, including the exceptions people handle by hand.
Map the inputs, the outputs and every artifact the business depends on: approvals, audit trails and compliance records, not just the obvious deliverable.
Propose the most efficient path from the same inputs to the same outputs, starting from nothing rather than from today's steps.
Run the new path in shadow next to the old one until the outputs match, measured, controls included.
Switch over behind governance gates: the system proposes, measures and executes; a person approves anything consequential.
The new process is observed too. Context compounds, and the next improvement arrives as a proposal, not a project.
The assumption ledger. For every step in a process, the system writes down the assumption that step exists to satisfy, for example "manager approval because spend could exceed budget", whether that assumption still holds, and what evidence would settle it. The process owner confirms, retires or overrides each one, on the record. It turns "the AI thinks your process is wrong" into specific questions with answers, and steps that turn out to be controls stay.
Getting real value from AI means questioning habits and assumptions. That is easier with a partner that has no stake in how things were done before.
Your people know the exceptions, the workarounds and why things are the way they are. The AI is not anchored to any of it. You need both: their context, and a perspective free of history.
The hard part of AI is not the model. It is deciding to stop doing things the old way. We make that decision explicit, evidence-based and reversible.
Not a replacement for your judgment. A way to apply it to the things that have gone unquestioned.
Most AI tools rediscover your world with every request. The Hivemind builds on what it already knows.
What has been learned about your operation, its decisions and their outcomes, kept and attributed, so the next task starts where the last one ended.
How your company wants work done, written once and enforced every time, instead of re-explained in every prompt.
When something goes wrong, the cause and the fix are recorded, so the same mistake is caught the next time instead of rediscovered.
Each cycle adds to what the system knows. Results improve with use rather than resetting with every session.
This is how our own fleet runs around the clock with limited human input: it builds on its stored context, directives and record of past work, and brings only the decisions that need a person to a person.
One control plane gives every agent an identity, a rulebook, a shared memory and a way to coordinate, so the work is accountable and safe to run at scale.
A shared memory across agents and sessions keeps what the system has learned about your operation, with each record scoped and attributed. Organizational knowledge stops leaving with the people who held it.
Every node and user carries a scoped identity with least-privilege entitlements. A central layer maps who and what to the capabilities, data and actions they are allowed - enforced fail-closed, not by convention.
Standing directives and standard operating procedures are machine-enforced, not just written down. Agents load them at the start of every session and are held to them - the way the org works, made executable.
Consequential actions pass explicit approval and least-privilege gates before they run. Anything above a set risk bar routes to a human owner; everything else proceeds autonomously within its authorized scope.
Every consequential action is timestamped, attributed to a node, and logged with the directive and authorization behind it - so what the fleet did, and why, is always reconstructable.
Capabilities are proven, not asserted. Every new function ships with its own test cases, changes are gated on measured results, and what the fleet claims it can do is checked against live behavior.
Enterprise authentication and identity provisioning - single sign-on and directory sync - so the platform fits an existing organization's access model instead of inventing its own.
Every change runs a gauntlet before it ships: vulnerability and dependency auditing, secret detection, prompt-injection checks, and an independent second model as reviewer.
The same governed fleet powers products you can use today and the longer-horizon work behind them.
One desktop client for every model you use. Chat, code, put models in a room together and let agents work unattended, with local models as a first-class option and your organization's rules enforced on the device.
Explore Forager →An assistant that learns how you live and work, remembers what matters, and keeps it yours. Talk to her like a person, and she picks up where you left off.
Meet Lily →Longer-horizon systems the fleet is building alongside the platform, from a memory-safe kernel and an LLM-authored programming language to robotics. Real work in active development, not a catalog.
See the research →The Hivemind runs its own engineering and operations on this method today. Two recent examples, and the numbers.
Asked how the fleet stores what it learns, the system found nine separate memory efforts that did not know about each other. It measured the actual data before choosing a storage design, then shipped the first consolidation step with a no-behavior-change guarantee, verified by identical outputs before and after.
During a credential cleanup, one "local" program kept appearing that no process could be found for. Matching timestamps to the millisecond showed it was the web server re-checking other machines' requests and being counted twice. The fix corrected the measurement and pointed the effort at the callers that really remained.
Measured from the fleet's own repositories and coordination system, not estimated. Customer engagements are starting with pilots; we don't claim results we haven't measured.
The fleet plugs into your existing systems of record and workflow through a common connector framework, so it works inside your environment instead of asking you to replace it.
A shared connector contract lets new chat tools, issue trackers, systems of record and workflow engines be added without re-plumbing the fleet - and anything that speaks the Model Context Protocol connects natively. Bring your own tools; the fleet meets them where they are.
Cloud, multi-cloud, private cloud, or your own hardware - the fleet deploys into the infrastructure you already have, not a walled garden.
Nodes already run in production across AWS and Oracle Cloud, with Google Cloud, Azure and Kubernetes deployment supported, alongside on-premise Windows, macOS and Linux endpoints - one coordinated fleet across all of them. Bring your own cloud and your own keys.
EU-sovereign hosting. Run the fleet on an EU-native cloud (validated on Scaleway, France): compute, storage, databases, secrets and keys stay in the EU, with EU-hosted open models (Mistral and open-weight models served from Paris). Frontier US models such as Claude remain available as an explicit opt-in.
Start with a single workflow. We learn its context, propose the redesign, and prove it side by side before anything changes.
Talk to us about a pilot