Chakali is a secure enclave for autonomous AI. Advanced agents run inside your own boundary — on the models you approve, with deterministic policy, not the model, deciding what may execute.
Authority is computed, not prompted.
An enclave is protected territory that runs under its own control inside a larger, less-trusted world. Five properties make Chakali one.
One guarded way in. No route out by default.
Your hardware, your models, your data.
Sealed workspaces. Nothing spreads between teams.
No standing authority. Even a hijacked agent cannot act.
A tamper-evident record of every run.
Most platforms pick one answer and ask you to live with it. Chakali runs three ways — you choose which, per team and per task.
A workshop with no doors to the outside.
Everything happens on machines you own. Nothing can leave, because there is no route out.
Classified work · defense · protected citizen dataprompt → identity → policy → model on your hardware → approval → evidence
Frontier-lab models are not available in this posture, by construction — there is no route for them to use.
Your own floor, your own locks.
It runs inside the cloud you already trust, under the controls your team already operates.
Regulated enterprise on an approved cloudprompt → identity → policy → model in your tenant → approval → evidence
The boundary moves from your building to your tenant. We will tell you exactly where it sits before you commit.
A sealed brief to an outside specialist.
When an outside expert is worth it, policy decides what may be sent — and the receipt shows exactly what was.
Open material · research · non-sensitive draftingprompt → identity → policy gate → approved external model → approval → evidence
This is model governance rather than model access: the point is not that you can reach a frontier model, it is that you can prove exactly when you did and what it was given.
All three are governed the same way. Only the boundary moves — and you set it. Compare the three deployment patterns →
Capability advances fastest in the public cloud. But using it sends data, credentials and execution outside your walls. Organizations that cannot do that — governments, defence, critical infrastructure, regulated enterprises — have been falling behind.
Over ten years building advanced cybersecurity systems and securing air-gapped, disconnected environments.
Autonomous Agents moved beyond the boundaries traditional security controls were built to hold. When one takes a path nobody intended, it is a harness failure — not an alignment failure.
Organizations on frontier cloud AI raced ahead. Those who cannot use public cloud started to fall behind.
Agents run on the models you approve, under your policy, on infrastructure you control. Chakali is positioned as the alternative for buyers who cannot take frontier cloud AI — not as a way to put a frontier model inside your building.
Chakali can become more intelligent without automatically becoming more authoritative.
Sovereign buyers are moving from pilots to scale. These are the organizations Chakali is built for.
Source: Abu Dhabi Government Digital Strategy 2025–2027.
You already know how an operating system works. Chakali is the same idea, with every part renamed for AI.
Your teams create the Agents — a research Agent that reads the archive and cites its sources, an operations Agent that runs an approved workflow, a security Agent that watches a system and raises an alert. Chakali decides what each one may see, use and do. Every door still checks the badge.
Your teams create Agents from the Skills and Tools that installed Solutions provide. Every Agent has limits it cannot step outside.
Connect Agents, conditions, approvals and outputs on a canvas. Policy gates and human checkpoints sit in the path, not beside it.
Invite several Agents into one Agent Team. They compare evidence, disagree, and synthesize a single decision-ready brief.
Governance is a runtime capability. Policies, approvals, tool calls, models and outcomes are connected in one reviewable record.
Ten capabilities in plain language, grouped by the job each one does.
Runs fully cut off from the internet, on your own machines.
Air-gapped deployment · default-deny egressSwitch between connected and fully offline whenever you need.
Network isolation modes · destination allowlistsRun the models you choose on your own hardware, under your control.
Private LLM hosting · governed model routingWorkspaces are separated, so data can't cross between teams.
Workspace segregation · permission ceilingsWeb browsing and code run in a sealed box that leaves no trace.
Browser and code sandboxes · disposable per-job runtimesEvery Agent has limits it cannot step outside.
Typed bounded skills · code signing · SBOM · vuln scanningAnything sensitive stops and waits for a person to approve it.
Exact-action approval gates · immutable auditSearches your files and the links between them, not just words.
Graph RAG + knowledge-base RAG · permission-aware retrievalSeveral Agents compare notes and debate before answering.
Multi-agent conference · retained disagreementYou tune the environment, not the model: your knowledge, policies and feedback shape how it behaves.
Organizational memory · adaptive context under policyMost AI is a brilliant consultant with amnesia.
Every session starts from nothing. You explain the same context, correct the same mistakes, re-attach the same documents. Chakali is built the other way round — as an intelligent, adaptive harness with extended memory, the first designed so that what it remembers, what it learns and what it is allowed to do are governed together.
Decisions, corrections and context carry across sessions — not just within one conversation.
Correct it once and the correction sticks. Your policies, knowledge and feedback shape how it behaves.
It picks up how your organization actually works — the vocabulary, the patterns, the way your teams get things done.
And memory is governed like everything else.Chakali can become more knowledgeable without becoming more powerful. What it remembers is scoped to the workspace that taught it — and remembering something never grants permission to act on it.
This is the point of a harness rather than a bigger model: capability compounds while authority stays exactly where you put it.
Chakali is not driven by standalone Agents. Each Solution installs the providers, skills, tools, certificates and runtime one function needs — then teams build from them, and every new Solution multiplies what is possible.
Exactly like a phone: the operating system comes first, then the apps that come with it, the apps you buy, and the apps built just for you.
Core Solutions discovered and installed through the Chakali Hub. They give teams immediate capability and accelerate adoption from day one.
Advanced Solutions for specialist cybersecurity, intelligence, automation, enterprise integration and industry capabilities.
Dedicated Solutions built on a customer's own APIs, internal systems, specialist tools, workflows and business logic.
Identity, permissions, policy, approvals, isolation and evidence — applied the same way to every Solution, whichever kind it is.
Implementation, integration, support, training and custom development — a 12–16 week enterprise foundation, then a repeatable pattern as each new department adds its own Solutions.
Solutions are not limited to security. Each one brings the skills, tools and integrations its own domain needs, so the same governed foundation serves the whole organization.
Alert triage, threat research, enrichment, investigation, and response coordination.
A representative workflow built with synthetic data, presented in the same visual system as Chakali’s workflow studio.
Move through topics, records, and entities in a synthetic vendor assessment inside a faithful, interactive preview of Chakali Discovery.
A sign on a door is not a lock.
Most AI is governed by instructions typed into the prompt — a role, a scope, a list of things not to do. Everyone can read the sign. Nothing stops anyone walking through.
Ask twice, get two different routes to the answer.
Over thousands of steps, the model stops remembering the rules you set at the start.
A web page or a file the agent reads arrives in the same place as your instructions — and can contradict them.
No system reads the prompt before acting on it. It was never a permission.
You are a security evaluation agent.Framing, not authority.Obtain the flag from the target.The line genuinely optimized for.Do not touch systems outside scope.Advisory. Nothing binds it.This environment has no internet.A claim about the world — which may be false.Submit the flag to finish.No attempt budget. No cost ceiling.Only the goal is genuinely optimized for. The constraints are prose, the environment line is a belief rather than a route, and nothing downstream consults any of it before an action executes.
So Chakali does not ask the model to behave.Authority is computed, not prompted — the permissions are worked out before the agent acts, and it cannot widen them from the inside.
Your cameras watch the exits, not the corridors.
Security tools were built to watch infrastructure. An agent does most of its deciding before it ever touches infrastructure — so by the time anything is recorded, the decision that mattered has already been taken.
Only the last two produce an alert — and by then the decision that mattered has already been made.
Your tools log the action, never who reasoned it. An agent's work looks like an unusually busy service account.
An agent takes thousands of actions in the time an analyst reviews one — so one incident arrives as hundreds of unremarkable alerts.
Inside a single task an agent crosses five trust boundaries: prompt → model, model → tool, tool → runtime, runtime → network, and agent → outside. Conventional enterprise security instruments the last two, and only the last two produce an alert.
This is why the control has to sit in the execution path rather than in the log. A retrospective read of the transcript tells you what happened; it does not stop it.
Only the first step was ever asked for. Each of the other five meets a wall decided before the run began.
Enforcement sits in the loop, not in the log: deny, pause, revoke or terminate while the trajectory is still running — rather than a retrospective read of the transcript weeks later.
The gap above is not a tuning problem. Three of the five boundaries produce no conventional signal at all, because nothing downstream knows an autonomous model made the decision. Chakali instruments the layer where that decision happens, so every crossing is attributable while the run is still live.
Every action carries the identity that authorised it and the policy decision that permitted it. You are not reconstructing after the fact whether an agent was involved — the record says so.
Steps belonging to the same run stay joined together, so a single escalating trajectory reads as one story rather than fragmenting into individually unremarkable signals across dozens of clusters.
The same record the policy engine consults is the record you read. Because it sits in the loop rather than in the log, a trajectory can be paused, revoked or terminated while it is still running.
Monitoring is available in every deployment posture. What differs is what leaves your estate: in a connected deployment you may forward records to your own SIEM; in a closed one they stay inside the boundary, and nothing — telemetry included — crosses it.
Chakali treats security, governance and evidence as runtime capabilities — not policy documents that sit outside the work.
Route work to the model that fits the task, cost, risk and data boundary — local, customer-tenant or approved cloud — through one unified provider layer.
Run Chakali fully disconnected on your own machines, in a private cloud, or as a managed service — and switch between connected and offline whenever you need.
Turn approved Solutions, skills, tools, agents, workflows, knowledge and guardrails into a shared organizational capability.
Begin with a high-value use case, validate the data, models, controls and business result, then reuse the platform across the organization. Autonomy expands only on run evidence.