Sign-off is a read, not an investigation
Every worker arrives with its workflow, permissions and named creator already documented, so security and procurement review a record instead of reverse-engineering a black box. See what's on a listing →
Enterprise
The trust layer for enterprise AI Workers.
The question isn't whether to use AI — it's which AI you can trust. Every Lancis worker ships with its workflow, guardrails, a named creator and an evaluation, so security and procurement can sign off before it reaches production.
What changes for your organization
Adopt AI without approving it one tool at a time
Your guardrails, not ours
Set what each worker may read, touch and do on its own — and where it has to stop and ask a human. The limits are yours, per team.
Private workers, not just the catalog
Your own workflows, encoded by your own experts, deployed to your teams only. They never appear in the public marketplace.
One standard, instead of shadow AI
Roll out workers that already cleared review, so teams stop quietly adopting whatever tool they found last week.
Enterprise capabilities
Built for how enterprises actually adopt AI
A worker is only useful where the work already happens — in your tools, on your data, under your access rules, with someone able to see what it did afterwards.
- Private AI Workers
- Custom workflows
- Security controls
- Team collaboration
- Analytics
For AI companies
Building the models, not buying the workers?
You sell intelligence. What decides whether it gets used is the professional judgment wrapped around it — and that is a staffing problem, not a training one. Lancis is where that judgment gets encoded, which makes us a distribution channel rather than a competitor.
Distribution into finished work
Your model shows up inside workflows that complete a job and hand back a file — not in a chat window someone abandons after two turns.
Verticals you don't have to staff
Security engineers, designers, researchers and ops leads encode their own domains on top of your model. You reach those verticals without hiring into every one of them.
Chosen per step, on merit
Every step of a workflow routes to the model best at that step. Where yours is the right call, it gets the call — and the expert can see why.
Evaluation on real professional tasks
Workflow-level results show where your model holds up and where it doesn't, measured on work practitioners actually get paid for.
Why not just use a free prompt pack?
Prompt packs are a starting point.
They are not AI Workers.
Thousands of free agent prompts exist, and many are genuinely useful. What none of them tell you is whether the output is right.
A prompt pack gives you
A description of quality
- ✕ Quality asserted in a README
- ✕ No measurement of accuracy or reliability
- ✕ Unclear who wrote it, or why
- ✕ A single prompt, not a workflow
- ✕ No permissions or guardrails
- ✕ Maintenance is yours
A Lancis worker gives you
Evidence of quality
- ✓ Accuracy, reliability, and safety measured
- ✓ Evaluation results published up front
- ✓ An accountable domain expert behind it
- ✓ Repeatable multi-step workflows
- ✓ Permissions and guardrails defined
- ✓ Improved through expert feedback
The hard part was never writing the prompt. It was knowing you can trust the output.
Early Development
Bring specialized AI to your teams
Tell us about your workflows and we'll walk you through how AI Workers are created, evaluated, and deployed inside an organization.