The Model is Commodity. Routing, Evaluation, and Governance Are the Work.
Connect your product or internal tools to any of 100+ models, routed by task and benchmarked against your own data, governed on every call. CreateOS forward-deployed engineers build and operate the unified AI execution layer so your team stops picking models and starts shipping reliable AI.
- ISO 27001 and SOC 2 Type II certified
- 100+ models, model-agnostic
- Automatic failover on every call
- Governed and auditable by default
The Gap is Production, Not the Model
Most teams spend engineering time picking models instead of shipping products. The right answer is a routing layer that selects, evaluates, and governs model calls so you never rebuild when a provider changes.
of enterprise AI pilots never reach production.
MIT NANDA, 2025
average breach cost in financial services, the exposure ungoverned AI adds.
IBM, 2025
of new cyber-insurance now excludes ungoverned AI risk.
Munich Re, 2026
What We Deliver
A unified model layer built for production: routing, evaluation, cost tracking, and governance from the first call.
Unified model API with failover
One routing layer across 100+ models. If a provider returns an error or degrades, the router falls over to the next-best option automatically, with no code change on your side.
Task-based model routing
Different tasks need different models. We configure routing rules so each request goes to the model that performs best on that task class, by latency, cost, and output quality.
Custom evaluation and benchmarking
We build evaluation harnesses against your business metrics, not generic leaderboards. Before a model swap goes live, it is benchmarked on your actual production inputs.
Cost tracking and optimization
Per-model, per-request cost tracking with aggregated reporting. We surface which models are running over budget and which tasks can be routed to a cheaper model without a quality drop.
Provider swap without rebuild
The routing layer abstracts the provider API. You swap a model, adjust a routing rule, or add a new provider without touching application code or redeploying.
Governance and output validation
Policy enforcement, hallucination checks, and PII masking run on every model response before it reaches a user or downstream system, regardless of which model produced it.
How an Engagement Works: The Production Path
A staged path from concept to governed production. Value lands early and governance holds at every step.
- 01
Discover
We map your current model usage, identify the highest-value routing and evaluation gaps, and produce a build spec and production roadmap. Fixed pricing agreed in writing.
- 02
Prove
We stand up a routing layer on the execution layer, benchmark candidate models against your own production data, and prove the cost and quality case before any broader rollout.
- 03
Productionize
Forward-deployed engineers harden it: task-based routing rules, evaluation harnesses, per-model cost tracking, output validation, and a full audit trail.
- 04
Scale
It goes live, then spreads. Model lifecycle management, ongoing benchmarking, and provider swaps on the governed layer you keep.
Proof: 100+ models behind one governed layer
CreateOS integrated 100+ LLM models into a single routing layer for an AI SaaS platform, with RAG knowledge retrieval, automatic fallback handling, and per-model cost tracking. The platform team went from managing individual provider integrations to a single governed interface that routes, evaluates, and reports on every call. The result was a 60% reduction in model-related operational overhead.
Models reachable through one routing layer, with automatic fallback across providers.
Reduction in model-related operational overhead after the unified routing layer went live.
Cost tracking and per-model reporting across every request, with no manual aggregation.
Integrations We Put into Production
Common LLM integration patterns we take live on the execution layer, each cited and auditable.
Unified API layer with failover
A single interface across 100+ models with automatic failover. When a provider degrades or returns an error, the router switches without application downtime or a code change.
Per-task model routing
Routing rules that send each request class to the model that performs best on it. Extraction tasks, summarization tasks, and generation tasks each get the model suited to them.
Custom benchmarks on business metrics
Evaluation harnesses built against your production inputs and your quality criteria, not generic academic benchmarks. Model selection is based on what matters to your product.
Cost tracking and model swaps
Per-model, per-request cost visibility with aggregated reporting. Provider swaps and routing rule changes go live without a rebuild or redeployment.
Evaluation harnesses for ongoing monitoring
Automated evaluation pipelines that run on a schedule or on deployment. When a model update changes output quality, the harness catches it before users do.
Provider migration without rebuild
The routing layer abstracts the provider API. Adding a new model or swapping a provider is a configuration change, not an engineering project.
Governed output validation on every call
Policy enforcement, hallucination detection, and PII masking run on every response before it leaves the layer. The same governance applies regardless of which model produced the output.
RAG integration with model routing
Retrieval-augmented generation wired into the routing layer. The retrieval step and the generation model are selected together, benchmarked against your knowledge base.
Why CreateOS for LLM Integration
Model-agnostic, no lock-in
The routing layer works across all major providers. You are not locked to one model or one API. Swap providers or add models without touching your application code.
Benchmarked on your data, not leaderboards
We evaluate models against your production inputs and your quality criteria. A model that tops a leaderboard may perform poorly on your specific task class.
Swap providers without rebuilding
The abstraction layer means model changes are configuration, not engineering. Add a new provider, adjust a routing rule, or swap a model in hours, not sprints.
Governed on every call
Policy enforcement, output validation, and a full audit trail are on from the first call. Governance does not degrade when you change models or add providers.
Common Questions
Which models does CreateOS support?
Through the CreateOS router we route across 100+ models. The routing layer is model-agnostic, so adding a new provider or model is a configuration change, not a rebuild.
What does '100+ models' mean in practice?
It means your application sends one request to the routing layer, and the router selects the provider and model based on your configured rules: task class, cost budget, latency target, and fallback order. You are not calling 100 provider APIs. You are calling one.
What does this engagement cost?
Engagements run on fixed-scope pricing, not hourly retainers. A discovery sprint and routing layer design is agreed in writing before any build begins. Cost depends on the number of models, integration depth, and evaluation requirements.
How long does it take to go live?
A routing layer for an existing product typically goes live in four to eight weeks. Engagements that include custom evaluation harnesses and per-model cost tracking run eight to twelve weeks. A discovery sprint produces the exact scope and timeline before build begins.
How do you benchmark and evaluate models?
We build evaluation harnesses against your production inputs and your business quality criteria. Candidate models run against the same inputs and are scored on the metrics that matter to your product: output accuracy, latency, cost, and task completion rate. A model swap does not go live until it passes the benchmark.
Who owns the IP after the engagement?
All code, routing configuration, evaluation harnesses, and IP are yours outright. We document everything and train your internal team to manage and extend what has been built.
Where does our data live and how is it protected?
In your environment. CreateOS runs in your VPC or on-premise, with region-locked compute and zero data retention by default. Model calls are routed through the governed layer, and every call is logged with a full audit trail. Regulated data never crosses a boundary you did not approve.
Where do you want to start?
Bring your current model integration. We will benchmark it, build the routing layer, and put it under governance.