Scout
Maps your environment and surfaces the workflows that matter most
- Leverages enterprise knowledge and observability telemetry
- Identifies high-value automation opportunities
- Creates a foundation for learning
Now available for early access
Smaller Models. Smarter Agents. Bigger ROI.
Autonomously simulate, fine-tune, and deploy enterprise-grade AI agents in days.
Up to 100x price-performancelower inference cost compared to frontier models on your tuned workflows while maintaining performance
Private AI for every teamfine-tuning autonomously, no ML research required. Deploy custom models without a dedicated ML team
Secure Modelswith patent pending cyber-aware fine-tuning, compliance and resilience are learned behaviors continuously enforced for your AI
Built on 8+ Patents Pendingautonomous simulation and training platforms deliver unprecedented velocity for your AI agents
The thesis
Frontier APIs optimize for generality. PerceptEye optimizes for your business.
Every enterprise has a portfolio of repetitive, knowledge-intensive workflows that touch internal systems, software tools, and private data. Frontier AI services can do most of them, but using them directly is expensive, slow, leaks data, and produces unpredictable quality.
The alternative - training your own model - is the right move, but it stretches AI, machine learning, and data teams thin. Most of their time goes into plumbing rather than into the workflow itself. The talent is there. The hours are not.
PerceptEye gives those teams four specialist agents that handle the plumbing alongside them. A simulation engine gives everyone the same ground truth. Your team owns the model, the decisions, and the outcomes - they just get far more of each.
A model your team owns, that runs where you choose, that is measurably good at the work you care about - and keeps getting better, with the people you already have.
The Quartet Platform
Four agents. One closed loop.
Scout discovers the workflow. Compass designs the learning system. Maestro fine-tunes the model. Sherpa keeps it running reliably. Each agent is independently useful. Together, they form a self-improving system that extends your team's reach.
discovery
Compass
Shapes how your models learn from real-world feedback
- Bridges the gap between intent and outcome
- Defines success for each workflow
- Ensures data quality at scale
Maestro
Delivers production-ready models your team and customers can trust
- Runs experiments autonomously with one touch
- Optimizes for your specific use cases
- Provides clear evaluation metrics
Sherpa
Coming SoonKeeps your models running reliably at enterprise scale and budget
- Maintains reliability within your policies
- Monitors performance continuously
- Balances speed, cost, and quality
Architecture
Inside your environment. End to end.
All four agents share one source of truth. Learning flows continuously from production back into training. Your data never leaves your environment.
Your Environment - Private Cloud - On-Premise
01 / SCOUT
Discovery
Maps your systems into testable workflows
02 / COMPASS
Design
Defines success and shapes training data
03 / MAESTRO
Fine-Tunes
Runs experiments, delivers performant models
04 / SHERPA
Coming SoonDeploy
Serves, monitors, and self-heals in production
Continuous Learning Loop
SHARED ENGINE
MissionControl
The shared ground truth
The compound effect
Greater than the sum of four agents.
// 01
Force Multiplier
Your AI, machine learning, and data teams keep the steering wheel. The agents handle the heavy lifting in parallel - so the same team ships several times more work.
// 02
Privacy
Every step happens inside your environment. The model is yours. Your data never leaves. Run in a public cloud, private cloud, on-premise, in a sovereign region, or fully air-gapped.
// 03
Reliability
Quality checks, drift detection, self-healing, and patent-pending cybersecurity-aware fine-tuning keep your model performing and passing the toughest security benchmarks.
// 04
Cost efficiency
A small, well-trained private model routinely outperforms a frontier service on the workflow it knows - at a fraction of the cost per request.
Adoption
Start with one agent. Or all four.
Each agent is independently valuable and designed to slot into the stack your team already runs. Maestro and Sherpa especially can be adopted on their own. Start where the pain is - bring in the rest when you're ready.
Scoutdiscovery
Map your systems, generate a library of candidate workflows, and make your existing agents observable. Valuable even if you never train a model.
entry point
Scout and Compassdiscovery and design
Turn that workflow library into a training-ready learning system your team can act on - cleaner data, clearer success signals, reproducible practice runs.
data team
Maestrostandalone training partner
Already have data and a goal? Hand them to Maestro. It picks the right base model, runs experiments, evaluates rigorously, and hands your team a model ready to ship. Pairs with any serving stack.
machine learning team
SherpaComing Soonstandalone operations partner
Already have a trained model? Hand Sherpa the model and your target environment. It figures out the best way to run it, keeps it healthy, and lets your team focus on what to build next.
platform team
The full Quartetclosed loop
All four agents, plus AgentSimulator, plus the continuous learning loop. The platform becomes specifically good at your environment over time. Recommended when private AI is a core capability for the business.
recommended
A day in the life of AI practitioner
One workflow. Discovery to deployment.
Week 1 · Day 1
Scout discovers
Maps the customer system and generates verified workflows
Week 1 · Day 2
Compass designs
Defines success criteria and the evaluation strategy
Week 1 · Days 3–4
Compass prepares data
Simulates the environment and prepares training data
Week 1 · Days 5–6
Maestro experiments
Autonomously experiments and selects the best model
Week 2
Maestro model fine-tuning
Autonomously performs supervised fine-tuning (SFT) and reinforcement learning (RL)
End of Week 2
Maestro evaluates the model and flags it as a Go. Deploy to the inference provider of your choice, or download the checkpoints and run it yourself. Your team approves and moves on.
Partners & investors
Backed by builders. Trusted by platforms.
PerceptEye is proud to partner with the platforms pushing the frontier of AI infrastructure, and to be backed by investors who have been early to every major shift in enterprise software.
Your model. Your environment. Built by agents.
PerceptEye is a force multiplier for your AI, machine learning, and data teams - measurably better at a fraction of the cost of a frontier service on the work you actually do. Deploy one agent or the full team - on your terms, at your pace.