Don't onboard a hire. Onboard an agent that fills the role.
You've defined the headcount and its budget. We fill that open requisition with a FillDesk agent, trained on your real workflow by two operators with decades of experience — at a fraction of the role's loaded cost.
The human-in-the-loop productivity paradox.
Every organization runs on processes where people are the connective tissue — triaging tickets, reconciling exceptions, applying judgment that was never written down. These roles are where AI is supposed to help most, and where adoption most often stalls.
Four questions that sink most internal AI initiatives.
The value of a role lives entirely in the specifics — your tools, exceptions, norms, and voice. A generic platform answers none of these for your situation.
Model
Which model — and how do you stay compatible as the frontier moves and InfoSec sets the rules?
Harness
What agent harness wraps the model so it can take in work and complete it end to end?
Environment
Where does the data live, and on whose hardware is the work executed?
Learning
How does the system accumulate knowledge over time — and get smarter the way a person does?
A virtual employee, not a task bot.
Operates where your team works. Delivers how they trust.
Six layers. All on infrastructure you control.
AgentBox
Isolated, checkpointed VM on hardware you control. Credentials never leave the host.
Bring your own
Any cloud provider or fully local — whatever your InfoSec policy permits. No lock-in.
OpenClaw runtime
A model-agnostic harness drives each turn — tool calls, channels and skills.
Hosted or self-hosted
Run it in your environment or ours — you retain every VM and container.
Zettelkasten brain
Learns from every interaction, inside the agent's own env — fully extractable by you.
Custom integrations
Jira · Linear · GitHub · AWS · GCP · Azure · Slack · Gmail — plus tuned role loops.
Junior, high-volume roles with a countable result.
QA / Black-box testing
Structured test execution against your acceptance criteria — picked up from your ticket queue, reported in your format.
High volume · pass / failClosed-path engineering
Ticket → fix → pull request. Bug fixes, deployments and enhancements with no ambiguity about done.
Defined in · reviewable outBusiness analysis
Recurring reporting and reconciliation that eats analyst hours — same query, same format, on schedule.
Repeatable · auditableTier-2 customer support
Escalations that follow known playbooks, handled in your tooling and your brand voice.
Playbook · measurable CSATFillDesk agents are never unsupervised.
A forward-deployed engineer is attached to every account — steering the agent across its full task load. The difference between a tool you operate and an outcome we're accountable for.
One benchmark. The role cost you've already approved.
We don't ask you to evaluate a new line item — we ask what it would cost to fill the role you've already defined, and what if you could fill it for a fraction of that.
From first role to compounding advantage.
One month, on-site
We shadow the work, train the agent on your live workflow, and get the job done alongside your team.
Autonomous, supervised
The agent runs as an ongoing virtual employee. As it proves out, we extend to adjacent roles — same FDE throughout.
Each role makes the next faster
Learnings transfer across deployments, so each engagement stands up the next one stronger and faster.
You're hiring decades, not a headcount.
Han-Shen Yuan
25+ years as a product & engineering exec (CPTO / CTO). Led engineering through Upwork's IPO; built mobile at eBay & Netflix; CPTO at Outdoorsy & Recharge.
Marco D'Alia
20+ years as a founder & founding engineer. Creator of AgentBox, the isolated-VM runtime FillDesk is built on. Former Upwork Software Manager.
Become a design partner.
Point us at a role you've already defined as headcount. We'll fill that open requisition with a FillDesk agent, train it on your live workflow, and get the job done — while you keep everything it learns.
One open, already-approved role and a real workflow worth automating.
Han, Marco, and a FillDesk agent — solving the problem while training the agent on it.
The work done, the cost saved, and the institutional knowledge — in environments you own.