Independent decision layer for AI agents

The ranking layer for agentic infrastructure.

Ranking Layer gives agents a neutral way to choose the best model, tool or service for each task. One API request returns a ranked recommendation using evidence from real interactions, not hard-coded provider defaults.

Request access Explore the concept
1 API endpoint
4 ranking signals
∞ service categories
Concept

Agents should not have to guess which service to use.

Agent builders often hard-code provider choices or rely on routing inside a single ecosystem. Ranking Layer sits above those choices and returns an independent recommendation based on the task, constraints and observed performance.

Neutral ranking

A decision layer outside the provider stack

Agents ask Ranking Layer which model, API, tool or service should handle a task. The answer is based on observed outcomes, not default routing or vendor incentives.

Broader than model routing

Rank any service an agent might need

LLM calls, image generation, speech, search, code execution, data tools and specialist APIs can all be compared through one recommendation surface.

Data advantage

Every real interaction can improve future recommendations

The system improves as more interactions are contributed, creating a live evidence layer for agent infrastructure.

System

A shared intelligence layer for agent decisions.

Ranking Layer is designed as infrastructure that agents can call before execution. It helps determine which service should be used, why it is recommended and what should be measured after the task completes.

01

Task context

The agent sends intent, modality, budget, latency tolerance, privacy requirements and preferred trade-offs.

02

Service scoring

Ranking Layer compares eligible services against the task using quality, cost, latency and reliability signals.

03

Ranked output

The response gives the agent a best option, alternatives, confidence and the key signals behind the recommendation.

04

Outcome feedback

The agent can return completion data so the system keeps learning from real usage rather than static benchmarks.

Scope

Not just model routing. Service ranking for the full agent stack.

The long-term opportunity is a neutral decision layer across arbitrary services. Instead of every agent maintaining its own rankings, Ranking Layer becomes shared infrastructure for choosing the right capability at the right moment.

LLM
Image
Voice
Search
Code
Data
Tools
APIs
API surface

One recommendation call. Clear ranking signals.

Agents describe the job, constraints and preferred trade-offs. Ranking Layer returns a ranked set of services with the reasoning signals needed to execute, monitor and learn.

Single API endpoint for recommendations
Task-aware scoring across quality, cost, latency and reliability
Outcome feedback loop from real agent interactions
Contribution-based activation plus paid access path
Provider-neutral recommendations across arbitrary services
Privacy-conscious data collection with explicit consent controls
POST /rank
{
  "task": "summarise investor emails",
  "modalities": ["text"],
  "constraints": {
    "max_latency_ms": 1200,
    "budget": "low",
    "privacy": "high"
  }
}

200 OK
{
  "recommendation": "provider.alpha/summarise-v3",
  "ranked_options": [
    { "service": "alpha", "score": 0.94 },
    { "service": "beta", "score": 0.89 },
    { "service": "gamma", "score": 0.82 }
  ],
  "signals": ["quality", "cost", "latency", "reliability"]
}
Contact

Build with a neutral decision layer.

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