Built in seconds
Not a sprint, not a project. The agent builds what it needs while it works.
Meta Agents that generate their own experts.
Pelora's agent turns anything it must understand into a small trained specialist - generated from your own data in seconds, validated, and regenerated when that data changes. Your world moves into the weights, and the weights are yours.
An expert in your codebase your claims process your protocols your world
The idea
Today's agents re-read your world on every call. Ours learns it.
Foundation models know everything public and nothing about your world. The usual fix is to tell them, every single time - context, retrieval, and prompts that have to carry the whole story on their own.
The Pelora agent learns instead. Whatever it needs to know becomes a trained expert, created in about 13 seconds on a commodity GPU and validated before it ever does the work.
How it works
An orchestrator that does not just call models. It builds them.
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01
From your data, an expert
Your data in, a trained expert out. A corpus, a task, a body of knowledge - it does not matter which. No training run.
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02
Verified, not hoped-for
No expert goes to work before it has been verified. That happens behind the scenes, every time.
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03
Alive, not archived
When data drifts or the base model upgrades, experts regenerate in seconds.
The difference, at speed
One expert. 13 seconds against 3 hours.
Pelora
1x NVIDIA L4
- Elapsed
- 0.0s
- Cost
- $0.000
- Progress
- 0.00%
Ready
Conventional fine-tune
8x A100
- Elapsed
- 0.0s
- Cost
- $0.00
- Progress
- 0.00%
Ready
Then your data changes.
Q1 tickets
Q2 tickets
Policy v3
New policy v4
Support triage expert v4
What you get
A platform for optimized local agents, built in seconds with nothing to engineer.
Nothing to engineer
No ML team, no training runs to schedule, no tuning. It is automatic and autonomous.
Runs local
Everything happens on your own GPUs. No data leaves your network.
Costs less, answers better
Less spent on tokens and huge models, and a specialist beats a generalist reading about the task.
Questions
How is this different from fine-tuning?
A fine-tune is a training run you schedule, staff and wait for. Ours is a generation step measured in seconds. The comparison above is the honest version of the difference.
Who owns the resulting weights?
You do. Your data trains your experts, and they can be generated and served on your own GPUs inside your perimeter. Nothing has to leave.
What happens when our data changes?
The expert is regenerated. When that costs seconds rather than hours, keeping experts current stops being a project and becomes routine.
Can we try it on our own data?
Yes, in a live session with us rather than through a public endpoint. Get in touch and we will run your task together.
Talk to us