The founding problem
Between 2018 and 2022, our founders spent years at e-commerce and logistics companies watching the same pattern repeat: operations teams burning 15–20 hours a week on repetitive, high-stakes website work that a machine could do safely — if the machine were built right.
Inventory updates that had to be hand-synced between three systems. Marketing copy changes that went through four rounds of email because nobody had a single source of truth. Price adjustments that took two days because they required three separate approvals and two manual deploys. A customer service team spending 30 minutes per ticket just formatting data for the website instead of actually helping customers.
The software existed to automate parts of this. But every tool required either total autonomy (which terrified operations leaders with reason) or so much human handoff that it saved nothing. No middle ground. No guardrails. No way to say: this agent owns inventory updates up to 10%, but anything bigger requires my eyes first.
So we built SiteCtrl.ai on a single premise: AI agents can handle real production work if you build trust first, not after. That means explicit autonomy levels, full logging, graduated deployment, and human override always present. Not as an afterthought. As the architecture.
What we do
We build AI agents that run the operational grind of your website — content updates, form handling, data sync, routine deploys — so your team can spend time on actual strategy instead of copy-paste work.
Every agent we deploy is purpose-built for one workflow, operates within explicit guardrails, and reports every decision. Your team stays in control. Nothing ships without your approval. Nothing runs unsupervised until it proves it should.
Senior engineers who ship
Our founders spent a combined 18 years building inventory systems, fulfillment infrastructure, and operational software at scale. They learned early: production systems don't forgive guesses. So SiteCtrl.ai is built by people who've already seen the failure modes.
Today the team is small and intentionally senior. We don't scale by adding junior engineers in a queue; we scale by making sure every engineer owns an engagement end-to-end. You don't get handed between departments. You get a technical principal who understands your infrastructure, your risk appetite, and your actual workflow — and who stays accountable for every change.
That's not a philosophy. That's a staffing constraint. It means we grow slowly and we turn away work that doesn't fit. We'd rather have 30 deep engagements than 300 shallow ones.
Production track record
production agent deployments
Since 2022. Each one live and handling real customer data.
average engagement length
Long enough to see the payoff. Too short to coast.
major incidents from agent autonomy
No unauthorized deploys. No data loss. No customer-facing outages caused by an agent acting out of guardrails.
of deployments still active
We measure success by client retention, not by contracts signed.
Concrete examples: Northwind Retail (automated 40+ weekly inventory syncs across three storefronts), Fenwick Logistics (AI agent handles shipment status updates — cut manual overhead by 18 hours/week), Vantage Health (appointment scheduling + patient communication through one unified agent), Almeida & Cole Law (knowledge base maintenance and client case summaries, fully auditable).
Trust is built, not assumed
Every agent we deploy starts constrained. Every action gets logged. Trust is earned one low-stakes task at a time.
Autonomy is explicit and finite. We don't give an agent permission to "update the website." We say: this agent can change product descriptions if the diff is under 200 characters and the category stays the same. Anything else needs your sign-off. You set the rule. The agent follows it. We log every boundary crossing attempt.
Every decision is visible. You see what the agent did, why it did it, and what it touched. Not a summary. The actual diff, the actual decision tree, the actual logs. If something went wrong, you know exactly where and how to roll it back.
Graduation happens gradually. A new agent starts on lower-stakes work: copy updates, form submissions, internal syncs. After proving it can handle those safely, you expand its autonomy. After proving that, you hand it something closer to production. If it fails at any stage, guardrails catch it before it touches live data.
Your team is always in control. You can revoke an agent's permissions instantly. You can review any action before it ships. You can run a one-off task through the agent and compare it side-by-side with a human version to see if it's ready for autonomy. This is infrastructure, not magic. Infrastructure you can touch and inspect and break when you need to.
That's the operating principle. Not "trust AI more." Trust the system that keeps AI honest.