Autonomous AI for business,
full productivity without compromise.
Employees use the assistant for one-time requests and recurring daily jobs. IT administrators maintain total authority over system connections, rules and audit records.
Why choose Devctrl
Devctrl is your solution to profit from AI
Every employee gains the benefits of AI while staying fully within your security boundaries. You build the solution around your actual workflows, not the other way around.
The business impact
Policy-compliant
Set your rules once in the console and enforce them instantly across every team, integration and AI action. Centralised controls ensure consistent compliance regardless of who initiates the task, while any policy updates apply globally in real time.
Automated audit
Because every action runs through identical checks, your audit logs are instantly complete. You get immediate proof of who initiated the task, what occurred, which rule was triggered and who granted approval.
Zero training required
Employees simply assign tasks and receive results. They only interact with policies when an action requires an approval.
The console is where you set and enforce governance policies. The assistant is where your employees execute daily tasks. Security rules remain active. You can also deploy the compliance gateway directly to your internal systems, no assistant required.
The assistant
Ad hoc tasks and automated playbooks.
Employees use the chat assistant for everyday tasks. Routine workflows convert easily into automated playbooks, letting your team delegate work that is policy-compliant and predictable.
- You hand over the job, not the instructionsDescribe in your own words what should come out. If something is open, the assistant asks rather than guessing. No prompt craft, no technical knowledge.
- Secure file uploadAttach a PDF, a spreadsheet, an image or a note to the conversation. The upload goes through the server rather than around it: size and content type are checked mid-stream, and the contents are scanned before the model sees them.
- What lives in your systems stays thereGoogle Drive, Notion and the rest come in through the same approved connections, under each person's own account. No second route, and no copy in a folder nobody knows about.
- Once becomes every timeWhat worked, the assistant writes down as a playbook. As a conversation instead of a blank form: you say in one sentence what you want, and it asks about what you left out. One question at a time, with answers to click, and every one of them can be skipped.
Two routes, one system
Chat
For work nobody has done before
- You watch and steer as it goes
- The assistant asks back when something is open
- Files and notes join in while you work
Playbook
For work that comes round again
- The steps are settled, written by the person who knows the work
- The rules hang off the playbook, not off good intentions
- Runs on a schedule, with nobody watching
The difference is not the machinery underneath. It is whether the steps are settled yet.
How playbooks workYour security comes first
Every action goes through the same check
In the chat or in autonomous processes: a tool is used only if it is allowed for that. There is no second route around the check.
Documents are scanned before the model sees them
An uploaded file is screened on the server and only then handed to the model. Uploading it, using it and downloading it each stand in the record separately.
Your file stays your file
The assistant works on a copy and never writes to the original. You save the result yourself, with one click.
How it holds up
Four structural guarantees.
While the daily interface empowers your team's productivity, these four core protections run continuously alongside every task, enforcing your policies without requiring manual oversight.
Identity
It acts as a person, not a service account
Because the agent works under each person's own access, the connected system's own audit log names them too. A shared service account can never say who did it.
Approvals
Equal standards for background tasks
Unattended workflows route critical decisions to human reviewers before proceeding. If an approval request goes unanswered, execution cannot continue, so background automation never breaks policy enforcement.
Recording
Independent logging controls
How much you record and how long you keep it are completely separate choices. Set light activity logging for one team and deep records for another, without changing how the product behaves.
Proof
Tamper-proof audit trails
A permanent decision chain captures every interaction step, from initial user identity to final output delivery. Stored independently from day-to-day operational data, these records provide clear proof for security and compliance teams.
Onboarding
Four step AI Transformation.
Set your governance parameters once and let the platform evaluate every task against them automatically. Your team executes daily work while every output passes through consistent, mandatory checks.
1 · Create the teams
Every department controls its own integrations, policies and record settings. The platform enforces an organisation-wide floor underneath, so global security rules are always respected.
2 · Approve connectors
Agents reach the platforms your team relies on, including GitHub, Linear, Notion and any MCP server. Employees authenticate with their individual credentials, while administrators manage connection approvals centrally.
3 · Write policies
Configure security policies using prebuilt templates or custom logic. Set strict blocking conditions, enforce human approval steps and filter out confidential secrets before tasks run.
4 · Choose recording depth
Set custom logging depth and storage limits for each department. Once configured, every executed workflow records its activity automatically according to your policies.
Direct or AI-assisted rule creation
Write policies in plain language or let the assistant guide you through configuration. Either path produces identical AI policies.
No-code policy authoring
Build guardrails in three straightforward steps. Select your targets, configure enforcement behaviour and finalise your settings, without writing a single line of code.
Natural language policy generation
Describe your desired guardrail in plain text and let the assistant propose a matching policy bundle. Review the proposal, apply it with a single click, and the record is saved to the audit log automatically.
Practical policy examples
See how policy enforcement works in daily operations. Explore how specific rules, triggers and automated actions protect your organisation across common workflows.
- Finance
Payments above €1,000 need approval
- Trigger
- The agent prepares a payment run with one item at €1,240.
- Outcome
- The action stops and waits for an approval. Everything below passes straight through.
- Organisation-wide
Personal data is masked before the model
- Trigger
- A document containing bank details is handed to the model.
- Outcome
- The fields are replaced before the model sees them. The action carries on.
- Engineering
Approved tools only, nothing external
- Trigger
- An agent reaches for a tool outside the catalogue, or one marked as leaving the company.
- Outcome
- The call is blocked and logged. Nobody has to notice it afterwards.
Bottom line
Two core components
Devctrl consists of a console and an integrated chat interface. The console runs independently alongside your existing internal infrastructure, while adding the chat creates a unified workplace for your entire company.
Console
For compliance and IT
- Declarative policy control: configure rules in natural language, directly in the editor or with AI assistance
- Central system approvals: manage and authorise third-party platforms centrally
- Pre-execution screening: every incoming action is evaluated automatically to allow, redact, require approval or block
- Customisable audit logging: set team-specific capture depth and retention periods, with dedicated export tools
Chat
For the workforce
- Accessible interface: delegate complex tasks in plain language, with no technical training
- Secure attachment handling: uploaded files are filtered and screened server-side before any data reaches the model
- Personalised authentication: connections to target systems run under each employee's own credentials
- Automated playbooks: turn recurring tasks into self-running workflows that execute independently
Unified platform governance: console policies stay fully active however the work is started. They are a core security foundation that cannot be bypassed, not an optional add-on layer.
Take control of your AI.
Bring a workflow you actually want to delegate. We'll walk through what happens, what pauses for a human, and exactly what the record shows afterwards.