Put it to work.
Run a model trained for your task. Capture the outcomes, corrections, and difficult cases that matter.
CONTINUOUS LEARNING FOR YOUR AI
Your AI gets
better with
every cycle.
A model trained for your task. Use it in the real world, learn from feedback, and repeat with a better version.
Use–Learn–Repeat
THE PERENO LEARNING LOOP↻ CONTINUOUS
Put your model to work.
Run your task model and capture the corrections and outcomes that matter.
INTERACTIVE OVERVIEW
Built around your task.Your data, examples, and quality standards.
Improved through real use.Turn corrections into the next training cycle.
Designed for efficient inference.Specialized models for repeatable workloads.
A SHORTER PATH TO A BETTER MODEL
Production is where
the learning begins.
Your work changes. Your model should keep up. Use–Learn–Repeat connects real-world work with reviewed feedback and model improvements, so each cycle can return a better version to production.
Turn feedback into improvement.
Review feedback, curate useful examples, and fine-tune a candidate. Evaluate it against your current model on held-out tasks.
Bring the learning back.
Release the improved model into your workflow. Use the next cycle to find the next improvement.
Less time between “that needs fixing” and a model that does it better.Use–Learn–Repeat
ONE COMPUTE FLEET. A FULLER DAY.
Work by day.
Learn by night.
During peak hours, your computers serve your workload. When demand falls, spare capacity trains the next model. The same compute, moving through Use–Learn–Repeat.
PEAK HOURS14:00
USE
Your workload
comes first.
Peak demand puts the fleet to work serving requests. Training yields capacity to your production workload.
Serving workloads10 / 12
Learning & fine-tuning0 / 12
Available capacity2 / 12
00:0006:0012:0018:0023:00
UseLearnAvailable
Illustrative capacity schedule, not live utilization. Actual allocation depends on demand and hardware compatibility. Training uses spare capacity; candidate models are evaluated before release.
LEARNING WITH A QUALITY BAR
Every new version
earns its place.
More training is only useful if the model gets better at your task. Compare each candidate with your current model before moving it into production.
Set the evaluation criteria together: task accuracy, response quality, latency, and inference cost.
EXAMPLE RELEASE WORKFLOW
Curate reviewed examplesTraining data
Fine-tune a candidateNew version
Compare on held-out tasksQuality gate
Review & releaseNext cycle ↻
Cadence and release criteria are agreed for your workload. This is a workflow illustration, not a live training run.WHERE THE LOOP COMPOUNDS
Repeated work.
Growing intelligence.
The strongest fit: a high-volume task, a clear definition of good output, and feedback you can turn into examples.
Customer support
Learn from agent edits, corrected routing, and reviewed replies as your product and customer needs evolve.
Document workflows
Turn corrected extractions and human reviews into training examples for your specific document types.
AI-first products
Build a model around a core product task, then use reviewed user feedback to inform each new version.
TYPEDESK ROOTS. A CONTINUOUS LOOP.
Better at your task.
Lighter on your budget.
Keep the economics of a specialized model, with an ongoing path to improve it. Scope your task, quality bar, and running cost together before training begins.
$50,000
Explore a 1/10th inference-cost scenario.
START WITH A SCOPED PILOT
Your task. Your benchmark.
Following the Typedesk approach: prepay for inference, with initial model development included. Agree on the learning cadence, ongoing training scope, and delivery terms before getting started.
GOOD QUESTIONS
Before the first cycle.
What does continuous learning mean here?
Use–Learn–Repeat: use the model, learn from reviewed feedback through fine-tuning and evaluation, then repeat with an improved version. Learning happens in model updates; the model does not change its weights during every request.
How short is the learning cycle?
The cadence depends on your workload, the volume of useful feedback, and the evaluation required. We scope the shortest practical cycle for your task rather than promise a fixed turnaround for every model.
What data do we need?
Representative inputs, examples of good outputs, and a way to review real-world results. Data handling, permissions, and which production examples may be used for training are agreed before work begins.
Is every interaction used for training?
The loop is built around selected, reviewed examples. Usage provides a source of possible learning signals; useful feedback is curated before it becomes training data.
How do we know a new model is better?
Evaluate the candidate and current model on the same held-out tasks using agreed criteria. Release decisions should account for regressions as well as improvements.
How is this related to Typedesk AI?
Pereno builds on Typedesk AI’s task-specific model approach, with continuous learning at the center: Use–Learn–Repeat.
YOUR OWN INTELLIGENCE. ALWAYS EVOLVING.
Make the next model
your best one yet.
Start with one task worth getting better at.