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Pillar 3

Less Experts Needed, Run Leaner Models — Simple & Efficient

AI Without the Overhead

Build, tune, and deploy high-accuracy AI without a large research team or heavy infrastructure. Lean models, faster cycles, predictable costs.

Lean Models, Maximum Impact

Big models aren’t always better. Smaller, well-tuned models can deliver higher accuracy, faster response times, and lower operational costs.

Reduce model size

Shrink footprints without sacrificing performance

Optimize for your data

Fine-tune to your domain and use case

Deploy where it matters

Run on modest hardware or edge devices with confidence

Frictionless AI Development

1
No-Code Training

Build and refine models without writing a single line of ML code.

2
No-Code Deployment & Scaling

Go from prototype to production in clicks; scale seamlessly as demand grows.

3
Automated Training Optimization

Let Protean handle hyperparameter tuning, resource allocation, and performance monitoring.

4
Reuse, Not Retool

Extend existing models for new use cases without starting from scratch.

Empower Your Team

With Protean, your data scientists, analysts, and developers can all work in a single, integrated environment. Non-technical teams can experiment and launch AI applications without waiting on scarce specialist resources — accelerating time to market and boosting ROI.

Data Scientists

Focus on high-impact experiments; let the platform run the boilerplate.

Developers

Ship features with simple APIs; integrate and observe in one place.

Analysts

Prototype quickly with no-code tools and structured evaluation.

Business Teams

Launch internal apps and automations without specialist bottlenecks.

Code Less and Create More Magic with AI

Dream it up, bring it to life.

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Lean Models

Less Experts Needed, Run Leaner Models — Simple & Efficient

Build, tune, and ship high-accuracy AI without a large research team or heavy infrastructure. Protean enables no-code training, automated optimization, and low-cost deployment—on your hardware or at the edge.

Smaller, well-tuned models deliver faster responses, lower latency, and predictable costs. For focused tasks, they often match or beat large general models—while using far less compute.

Product and backend skills are enough. Protean abstracts ML plumbing—your team selects data, defines tasks, and evaluates outputs. No in-house MLOps or research team required.

Yes. Use no-code training flows to define labels, upload datasets, and start fine-tunes. Protean handles data splits, checkpoints, and evaluation automatically.

The platform tunes hyperparameters, manages early-stopping, and allocates resources. It compares runs with consistent metrics so you can pick the best model confidently.

Start with modest GPUs or shared accelerators for training; serve compact models on standard CPUs or small GPUs. Edge and on-prem targets are supported for low-latency use cases.

Yes. Deploy lightweight runtimes to edge devices, VMs, containers, or Kubernetes clusters inside your VPC. Keep data local while meeting performance targets.

Use no-code deployment, autoscaling, and health monitoring. Versioned models and canary rollouts are built-in, so you scale safely with minimal ops overhead.

Absolutely. Start from your current checkpoints, adapters, or embeddings. Reuse curated datasets with versioning, and extend models to new tasks without re-platforming.

Built-in evals, hold-out tests, and drift alerts. Compare runs apples-to-apples and monitor latency/accuracy in production with dashboards and alerts.

Smaller models mean cheaper training and serving. Autoscaling and right-sizing cut idle time; per-model version metrics make unit economics transparent.

RBAC, model and dataset versioning, and end-to-end audit logs are standard. Every training run and prediction is traceable for compliance reviews.

Most teams ship a first workflow in days, not months—thanks to no-code training, one-click deploys, and built-in evaluation that shortens iteration cycles.

The Preferred way to build and distribute Sovereign AI Solutions

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