AI App OS for production teams and buying committees

Ship AI apps you can actually trust in production.

Run every AI workload through one production control plane: route across frontier and open-weight model pools, operate agents with traces and fallback, prove ROI, and pass enterprise review without rebuilding your stack.

Frontier model poolsOpen-weight / private poolsPolicy fallbackSOC 2 readinessISO control map
Enterprise procurement and investor diligence path readyTrust Center · SLA · sub-processors · DPA

Modeled across 180+ composite production workload profiles

Aurelis Bank
Maple Health
CodeForge Labs
Northstar Commerce
Helios Insurance
Vector Mobility

Customer names, quotes, and logo-style marks on this site are composite illustrative profiles until public permissions are published. They show realistic teams and workloads, not verified third-party endorsements.

Built for the buying committee

Get developers, platform, security, and finance aligned on one page.

Enterprise AI platform purchases are rarely single-player decisions. SkyAIApp packages adoption, ROI evidence, risk controls, and procurement assets into one narrative so champions do not have to translate it internally.

18–35%
Cost savings
Modeled benchmark: routing + cache
< 1.2s
P95 latency
Sample P95 range
99.5%
Success rate
Fallback-policy model
Modeled benchmark suite

Make realistic data inspectable, not hand-wavy.

The numbers below are realistic planning benchmarks built from composite workload profiles, deterministic replay assumptions, and the public pricing model. They are not audited customer production claims.

Replay a single-model baseline

Each workload starts from a plausible single-provider setup so savings are measured against a stable baseline.

Apply routing policy variants

The suite compares balanced, cost-first, quality-first, cache-heavy, and fallback-heavy policies.

Score operational constraints

Results include cost, P95 latency, success rate, cache hit rate, traceability, and guardrail overhead.

Live Demo

Intelligent Routing in Action

Select your goal and strategy, watch SkyAIApp choose the optimal model

Live Routing Demo

6 providers • 50+ models

Supported Providers
Goal
Strategy

Selected strategy: Balanced.

✨ Supports 50+ models • Auto-fallback • Semantic cachingComing soon: Open-source models (Qwen, Yi, Baichuan)
Model strategy layer

Do not bet production on one model. Govern models like a supply chain.

SkyAIApp manages the fast-moving AI supply side with model pools, policy tags, regional constraints, budget ceilings, and fallback chains. When providers change, you update policy instead of rewriting apps.

Explore routing
Frontier reasoning pool
  • OpenAI
  • Anthropic
  • Google
Complex reasoning, code, research, long context
Low-latency serving pool
  • fast proprietary
  • small frontier
  • cached responses
Support, classification, real-time copilots
Open-weight / private pool
  • Llama
  • Mistral
  • DeepSeek
Cost-sensitive, residency, private deployment
Governed fallback pool
  • regional pins
  • policy tags
  • provider failover
Switch by region, compliance tag, and SLO
Why SkyAIApp

Built for Production

Not just an API wrapper, but complete AI application infrastructure

Multi-Model Routing

Intelligently route to optimal models

Reliable Orchestration

Auto-retry, failover, tracing

Cost Optimization

Semantic cache, budgets, analytics

Enterprise Security

SSO/RBAC, audit, data isolation

Use Cases

Powering Every AI Use Case

From customer support to code generation, SkyAIApp provides production-grade infrastructure for all AI applications

View all use cases

AI Support

24/7 automated customer service

Knowledge QA

Smart enterprise knowledge retrieval

Dev Assistant

Code generation & review

Content Moderation

PII detection & content filtering

SkyAIApp benchmark suite, by the numbers

These numbers come from composite workload replay and the published pricing model for procurement, POC, and capacity planning, not audited customer production claims.

12.4B
Replay requests modeled
Across composite benchmarks
$28M
Modeled savings
Vs. single-model baselines
180+
Workload profiles
Across 14 industries
99.98%
Target uptime model
Modeled multi-region probes
Composite scenario language

Three teams, three workloads, one platform.

In the composite benchmark, migrating 14 AI workloads behind one routing layer cut token spend by 31% and reduced P95 latency by 22% while keeping every decision traceable for audit review.
Priya Ramanathan
VP, AI Platform · Aurelis Bank
The router, guardrails, and agent runtime model replaced four homegrown systems in the rollout plan, taking a HIPAA-bound triage copilot from mirror traffic to governed launch in 47 days.
Daniel Yoon
Chief Technology Officer · Maple Health
The marketplace pricing model shows how dimensional usage, model quality, and seat controls can become one invoice instead of three disconnected billing systems.
Alessia Marchetti
Founder & CEO · CodeForge Labs

Compatible with leading AI providers

OpenAI • Anthropic • Google • Azure • AWS Bedrock

OpenAI logo
Anthropic logo
Google logo
Azure logo
AWS logo

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