Automation at enterprise scale.
Built for organisations where one process touches a thousand staff.
Your team builds it. We teach them how. Deployed on-prem if you need it.
Think. Draw. Click to publish.
Operational automation that scales without scaling your AI bill.
Most enterprise AI deployments share the same shape: AI orchestrates everything, costs scale with usage, every decision lives inside a probabilistic model nobody can audit. At enterprise scale, that architecture does not fit the operating reality of a regulated business.
Hotchilli is built the other way around. 90% deterministic. 10% AI. A Parser walks the diagram. The diagram is the runtime. AI is the seasoning, not the meal. Auditable. Repeatable. Boring, in the way enterprise finance and compliance teams want boring.
The architecture means a 200-staff workload runs the same as a 20,000-staff workload. The only variable is volume, not the predictability of the answer.
90% Functional
Deterministic by design. The Parser walks every flow the same way every time. A million times. Same modules. Same path. Same outcome. Auditable. Repeatable.
90% CPU
Most of the work runs on CPU at fixed cost. AI only shows up where it earns its place: voice, judgement, the bits that genuinely need it. 10% GPU. 10% AI.
AI costs that don't scale
As your business scales, your AI bill doesn't. A 200-staff business often spends £100/month on AI inference, not the thousands competing platforms would bill for the same workload.
Same architecture, every team. Lower running costs. Your team owns it.
Your people. Your processes. Your platform.
Not Forward Deployed Engineer programmes at £500K to £4M depending on scope. Not transformation consultancies that leave a system your team can't maintain. The system rots because nobody on your team built it, owns it, or can maintain it.
The people who already run the process build the version that runs itself. The credit control team. The customer service team lead. The operations coordinator. The branch manager. They hold the 90%, institutional knowledge an FDE would spend three months learning. The platform handles the 10%: integrations, modules, glue.
At enterprise scale, this is the difference between automation that scales and automation that stalls.
- Manual process owners do the same work every day
- Bottlenecks live in people's calendars
- New volume needs more headcount
- Knowledge walks out when staff leave
- The platform runs the process; people own the outcome
- Bottlenecks disappear or surface as flow problems anyone can fix
- New volume costs near-zero to absorb
- Knowledge lives in the flow, not in someone's head
We did this in 2012. Same trick. Every process.
A receptionist with no engineering training built a working phone system in 2012, because we'd already hidden everything that didn't matter to her. She drew what she already knew. The system ran it.
Same trick. Every process.
The platform's composite modules absorb the complexity (APIs, databases, AI calls, branching logic) so the person doing the work draws what they already do manually. They don't learn engineering. They draw their process. The platform runs it.
Onboarding new staff in 30 minutes. They don't read a codebase. They read the flow. The flow shows them what the system does, because the flow is what the system does.
I'm not selling a platform and disappearing. I'm building a substrate, training your team to use it, and staying around for what comes next.
Where the platform lives is your call.
Hosted by us on AWS. Or deployed on-prem in your estate. Or air-gapped for regulated environments. No outbound calls to LLMs you haven't approved. No PII leaving the network. Guardian sits in front of every AI module by composition rule, the platform refuses to ship a flow that doesn't tokenise sensitive data before it hits an LLM.
Your security team configures it. They know what your data looks like better than any vendor would.
Deploy where you need it
Hosted by us on AWS. On-prem in your estate. Or air-gapped for regulated environments.
PII safe by architecture
Guardian tokenises sensitive data before any LLM ever sees it. The platform refuses flows that bypass tokenisation.
Your audit, your rules
Every flow is its own documentation. The diagram is the runtime; what's drawn is what's running.
What would you automate first at scale?
Tell us where the operational drag is biggest. We'll pick the one process where automation moves a material P&L line. First session is on us. Sixty minutes. No obligation.