ToolPicker
Contextual AI Rerank

Contextual AI Rerank

52/100

Instruction-following reranker (Rerank v2) for RAG.

contextual.ai

// scorecard · 52/100

verified 1mo ago
methodology →
Agent-readiness32/35
Agent registration (api_key)6/9
Public API5/5
OpenAPI spec9/9
MCP server9/9
llms.txt3/3
Reliability & performance0/15
Actively maintained
Production-ready0/4
Updated recently
Pricing6/13
Itemized public pricing6/6
Free tier / trial0/4
Free-tier generosity
Docs & DX2/12
Docs quality
Public API2/2
Quickstart / examples0/2
Security & compliance8/12
SOC 26/6
ISO 270010/2
GDPR0/2
Security / trust page2/2
Openness4/10
Open source0/6
Open / un-gated API4/4
Company maturity0/3
Company size
Years operating

// rankings · cost per standard workload

#4 in Rerankers$0.5/ 1k rerank searches

Rerank-v2 $0.05/1M tokens x10M = $0.50/mo (v2-mini $0.20)

contextual.ai/platform/pricing

// overview

Contextual AI is a context engineering platform providing a unified context layer for production-grade AI agents that reason over technical documentation, specs, logs, and institutional knowledge. It supports RAG workflows, agent orchestration via Agent Composer, and specialized tools for search, root cause analysis, IP research, and structured extraction. The platform targets advanced industries with enterprise connectors, groundedness checks, and flexible deployment.

Best for: Enterprise teams in financial services, engineering, manufacturing, and legal needing production AI agents over complex technical data

// pricing

On-demandPay-as-you-go
  • $25 free credits
  • Unlimited users/agents/datastores
  • SOC2 Type II
  • Basic ($3/1k pages) and multimodal ($40/1k pages) parsing
EnterpriseCustom
  • Unlimited workspaces
  • VPC deployment
  • Dedicated support
  • Custom data retention
  • RBAC and observability

// security & compliance

SOC 2 ISO 27001 GDPR HIPAA

// features

  • Agent Composer for custom workflows
  • RAG components (Parse, Retrieve, Rerank, Generate, Groundedness)
  • Pre-built agents and templates for technical use cases
  • Support for simple/complex/unstructured/structured data
  • Enterprise connectors and continuous ingestion
  • Sentence-level attributions and visual bounding boxes

sources