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Pillar 2

Everything You Need to Build & Scale AI — One Platform

From Idea to Impact, Without the Friction

Bringing an AI product to market shouldn’t mean wrangling tools and infrastructure. Protean takes you from prototype to production—faster and leaner.

Built for the Entire AI Lifecycle

Fine-Tune: Adapt models to your domain with intuitive workflows and automated hyperparameter optimization

Features That Power Your Growth

Customized Models

Train and deploy models tailored to your specific industry, data, and use cases

SaaS-Like APIs

Integrate AI capabilities directly into your products with secure, developer-friendly APIs

Scalable Runtime

Automatically scale from proof-of-concept to enterprise production without re-architecture

App Distribution

Package and share AI-powered applications seamlessly across teams or to customers

Why Choose Protean for AI Development

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One Platform

Replace fragmented tools with a unified, managed environment

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Faster Time-to-Value

Go from concept to deployment in days, not months

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Operational Efficiency

Reduce overhead by consolidating workflows, monitoring, and infrastructure in one place

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Future-Proof

Built to evolve alongside the rapidly changing AI landscape

Code Less and Create More Magic with AI

Dream it up, bring it to life.

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One Platform

Everything You Need to Build & Scale AI — One Platform

From idea to impact without wrangling tools. Protean covers fine-tune, inference, and RAG with SaaS-like APIs, a scalable runtime, and app distribution—so you launch faster and operate lean.

Disparate tools for training, evals, model serving, auth, monitoring, and release management. Protean unifies these so product teams ship AI without stitching systems together.

Use no-code flows to fine-tune, one-click deploy for inference, and built-in retrieval for RAG. Each stage shares datasets, versions, and metrics so handoffs are seamless.

No. Product and backend engineers can train, evaluate, and deploy. Protean abstracts infra, scheduling, and rollouts so small teams can deliver production AI.

Expose models as secure, versioned APIs. SDKs and standard REST make it easy to call from services, apps, or workflows; auth and rate-limits are built in.

Yes. Ship internal tools or customer-facing endpoints with access policies, usage quotas, and analytics. Share across teams or business units with one click.

Autoscaling, canary rollouts, and health checks are built-in. Start small; scale horizontally without re-architecting or swapping platforms.

Most teams ship an initial workflow in days. Templates, evals, and managed deploys compress iteration loops and cut time-to-value.

Models, datasets, and configs are versioned. Promote to prod with change logs, roll back instantly if metrics regress, and compare runs apples-to-apples.

Right-sized serving, autoscaling, and lean models keep spend predictable. Per-model telemetry shows unit economics to guide optimizations.

Yes. Fine-tune with your data and tasks, evaluate with domain-specific metrics, and ship the best checkpoint directly to production.

Absolutely—ingest checkpoints, adapters, embeddings, and curated datasets. Keep what works; extend to new use cases without re-platforming.

Latency, throughput, errors, and quality metrics with dashboards and alerts. Track drift, compare releases, and tie usage to outcomes.

The Preferred way to build and distribute Sovereign AI Solutions

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