INFERENCE INTELLIGENCE PLATFORM

See everyinference.Control everydecision.

An intelligence and control layer around your AI stack. Explore every model, provider, request, token, latency spike, failure, and routing decision.

Start observing Explore the platform

INFERENCE / SANDBOX

SIMULATED ENVIRONMENT

P50 LATENCY418 msmedian response

P95 LATENCY1.02 stail response

TOKENS / SEC87.4example throughput

COST / 1K$0.0082example unit cost

INFERENCE LATENCY

P50P9560s WINDOW

P50 418ms P95 1.02sILLUSTRATIVE STREAM

REQUEST STREAM DEMO RUNNING

Generated example inference requests, not live traffic
REQUEST	MODEL	LATENCY	TOKENS	STATUS
r_8f31a	claude-sonnet	612ms	1,824	200
r_7ae91	gpt	388ms	924	200
r_551ca	gemini-pro	821ms	2,142	200
r_22f90	llama-maverick	1.2s	1,602	429 → fallback

ORBITRIVA CONTROL PLANESAMPLE DATA

VISIBILITY BEFORE OPTIMIZATION.FOLLOW THE REQUEST

REAL-TIME TRACINGMODEL ROUTINGTOKEN INTELLIGENCELATENCY ANALYSISPROVIDER HEALTHCOST ATTRIBUTIONQUALITY SIGNALSFALLBACK CONTROLCACHE ANALYTICS

01 / CONTROL PLANE

One intelligence layer
across your AI stack.

Bring applications, model endpoints and inference signals into one coherent view. This interactive architecture illustrates the platform concept—not a set of active provider connections.

APPLICATIONS

⌁Agent

↳Copilot

⌕Search

≋Support

⤷Workflow

ORBITRIVAInference Control PlaneOBSERVE · DECIDE · TRACE

MODEL ENDPOINTS

Observe the request.

Connect latency, token usage and outcome with the workload that produced them.

Make the decision explicit.

Understand a routing choice in terms of policy, constraints and provider signals.

Retain the evidence.

Follow the request beyond a summary metric to its individual trace and context.

02 / INFERENCE INTELLIGENCE

Know why your inference
behaves the way it does.

A technical view of the signals an inference control plane can bring together. All figures and states below are illustrative scenarios.

LATENCY01 / 04

Find latency before
users feel it.

Separate queue time, provider delay, time-to-first-token, generation speed and total duration.

Queue42ms

Policy18ms

Provider158ms

Generation394ms

REQUEST BREAKDOWN612ms example

ECONOMICS02 / 04

Understand cost
down to the request.

Attribute usage and examine the shape of model, output, retry and routing costs.

EXAMPLE MONTHLY INFERENCE$12,482.16

↘ 18.4%scenario delta

Model 62%Output 21%Retries 9%Routing 8%

QUALITY03 / 04

Compare models with
contextual quality signals.

Relate evaluation dimensions to the task. A fast response is not automatically a useful one.

Answer quality0.87

Tool success0.94

Fallback rate0.03

User retry0.06

Grounding0.82

ILLUSTRATIVE EVALUATIONNOT A MEASURED SCORE

RELIABILITY04 / 04

Make provider
degradation visible.

Understand health as a changing signal, not a permanent property of an endpoint.

OpenAIHealthy

AnthropicHealthy

GeminiElevated latency

BedrockHealthy

vLLM ClusterHealthy

SIMULATED PROVIDER HEALTHNO ACTIVE CONNECTIONS

03 / ADAPTIVE ROUTING

Route every
request with
context.

Explore how latency, cost, quality, capacity and availability can shape an inference decision.

The simulator cycles through three predefined scenarios. It does not call providers, score real models or route actual traffic.

CONSTRAINTS IN.EXPLAINABLE DECISION OUT.

ROUTING SIMULATOR

INCOMING REQUEST / EXAMPLE

{
  "task": "customer_support",
  "priority": "realtime",
  "max_latency": 900,
  "quality": "high"
}

Claude Sonnet

Latency: 612ms
Quality: High
Cost: $$
Availability: Ready

SELECTED

GPT

Latency: 388ms
Quality: High
Cost: $$$
Availability: Ready

CANDIDATE

Gemini

Latency: 821ms
Quality: High
Cost: $
Availability: Busy

CANDIDATE

ROUTE SELECTEDClaude Sonnet

quality within 900ms SLA

EXAMPLE DECISION TIME4.8 ms

04 / TRACE EXPLORER

From user request
to final token.

Search the sample request set, filter by execution context, and expand a trace to inspect its illustrative waterfall.

ModelProviderLatencyStatusEnvironment4 TRACES

Interactive synthetic inference trace dataset. Expand a trace to view example stage timing.
TIME	ROUTE	MODEL	TTFT	TOTAL	TOKENS	COST	STATUS
12:42:18.112	support-prod	claude-sonnet	284 ms	820 ms	1,842	$0.014	OK
12:42:17.891	research	gpt	431 ms	1.18 s	3,091	$0.028	OK
12:42:17.617	chat	gemini-pro	198 ms	644 ms	883	$0.008	OK
12:42:17.520	copilot	llama	712 ms	2.02 s	2,201	$0.005	FALLBACK

05 / PROVIDER-AGNOSTIC BY CONCEPT

One view.
Every inference provider.

A reference landscape spanning commercial APIs, cloud platforms and self-hosted inference. Listed names identify ecosystem examples, not active integrations or partnerships.

Select a provider to inspect its reference integration category.

06 / AN ILLUSTRATIVE SCALE SCENARIO

Inference,
measured properly.

Example values for this demonstration—not operating results, measured uptime, adoption or customer savings.

2.4B+Example requests observedILLUSTRATIVE VOLUME

86 msExample overhead deltaNOT A BENCHMARK RESULT

31%Example cost reductionNOT A CUSTOMER OUTCOME

99.99%Control-plane design targetNOT MEASURED AVAILABILITY

14Endpoint types illustratedNOT CONNECTED ACCOUNTS

07 / THE DEVELOPER INTERFACE

Instrument once.
Understand the path.

Explore a proposed SDK and gateway interface. These are reference examples, not released packages or an operational API. The existing benchmark workspace is a separate developer release.

quickstart.pyPROPOSED INTERFACE / NOT RELEASED

from orbitriva import Orbitriva

client = Orbitriva(
    api_key="orb_live_••••••••"
)

response = client.responses.create(
    model="auto",
    input="Summarize this support thread.",
    route={
        "optimize": ["latency", "quality", "cost"]
    }
)

REFERENCE EXAMPLENO REQUEST IS EXECUTED

EXAMPLE RESPONSE200 SIMULATED

{
  "model": "claude-sonnet",
  "provider": "anthropic",
  "route_reason":
    "best_quality_within_latency_budget",
  "latency_ms": 612,
  "ttft_ms": 218,
  "input_tokens": 1328,
  "output_tokens": 421,
  "cost_usd": 0.0127,
  "trace_id": "tr_94810"
}

ENTERPRISE CONTROL / ARCHITECTURE PREVIEW

Production AI should
not fail silently.

A reference architecture for the controls production teams evaluate. The capabilities below are proposed—not included in the current benchmark workspace and not a security certification.

01 / PRIVATE NETWORKING

Network boundaries.

VPC and private-endpoint options to evaluate.

02 / DATA CONTROL

Data on your terms.

Retention and regional-processing policies to define.

03 / ACCESS

Governance by design.

RBAC, SSO and workspace controls to specify.

04 / OBSERVABILITY

Evidence beyond the UI.

Metrics, traces and log export in the reference design.

05 / POLICY ENGINE

Explicit rules.

Centralized inference constraints and decision policies.

06 / SUPPORT

Engineering context.

Discuss deployment needs without an implied support SLA.

Talk to engineering View security architecture

START WITH ONE REQUEST

Make inference
observable.
Then make it
intelligent.

Explore what happens between a prompt and a response—and what an inference intelligence layer can make visible.

Start building Book a technical demo

See everyinference.Control everydecision.

One intelligence layeracross your AI stack.

Observe the request.

Make the decision explicit.

Retain the evidence.

Know why your inferencebehaves the way it does.

Find latency beforeusers feel it.

Understand costdown to the request.

Compare models withcontextual quality signals.

Make providerdegradation visible.

Route everyrequest withcontext.

From user requestto final token.

One view.Every inference provider.

Inference,measured properly.

Instrument once.Understand the path.

Production AI shouldnot fail silently.