Contextual AI
Contextual AI components
State-of-the-art component APIs to enhance every step of your RAG pipeline
Core RAG building blocks
Optimize your RAG pipeline end-to-end with APIs for document understanding, reranking, generation, and evaluation
State-of-the-art performance
Ground agents in your enterprise knowledge with RAG components that outperform competing tools on leading benchmarks
Simple, flexible integration
Incorporate components into your existing RAG pipeline without needing to overhaul your entire architecture
Better RAG performance in minutes
Powerful, modular RAG components for when accuracy and simplicity matter
Parse
Our multi-stage document understanding pipeline for converting unstructured content into AI-ready formats
Rerank
The first instruction-following reranker, providing greater control over how retrieved knowledge is prioritized
Generate
The most grounded large language model in the world, engineered specifically to minimize hallucinations
LMUnit
Our evaluation-optimized model for preference, direct scoring, and natural language unit test evaluation
Flexible, modular components
Parse
Extract complex multimodal content from any document
Convert unstructured documents into structured output optimized for your RAG pipeline
Easily process text, charts, tables, code, and other complex modalities
Infer document hierarchy and add positional metadata to each chunk, enabling agents to connect information across hundreds of pages
Rerank
Resolve knowledge conflicts with the only instruction-following reranker
Steer how your reranker prioritizes information with natural language instructions
Serve the most relevant content to your model’s context—with or without instructions
Drop into your existing RAG pipeline with just a few lines of code
Generate
Maximize accuracy with the world’s most grounded language model
Generate answers strongly grounded in your retrieved documents
Minimize hallucinations with response tags to delineate between facts and model commentary
Provide in-line attributions to source documents for end-users to verify responses
LMUnit
Evaluate LLM responses with fine-grained natural language unit tests
Assess responses for your defined criteria, like conciseness, technical precision, and more
Test for accuracy with out-of-the-box evaluation tests for equivalence and groundedness
Reduce your dependence on end-user testing and get to production faster
Simple APIs to improve any RAG pipeline
# Convert unstructured content into structured output
import os
from contextual import ContextualAI
client = ContextualAI(api_key=os.environ.get("CONTEXTUAL_API_KEY"))
with open("your filepath", "rb") as fp:
response = client.parse.create(
raw_file=fp,
parse_mode="standard",
figure_caption_mode="concise",
enable_document_hierarchy=True,
page_range="0-5",
)
job_id = response.job_id
results = client.parse.job_results(job_id, output_types=['markdown-per-page'])
Testimonial
"At ClaimWise, we work with complex patents and scientific articles. Meeting the quality expectations of our users has always been one of our main technical challenges. The Contextual Al reranker's instruction-following capability is great; we have some nuanced requirements about how to prioritize different types of paragraphs for different fields of invention, and the reranker follows those instructions well."
Milo Moerkerke - Co-founder at Claimwise
Additional Resources
Pricing
Understanding Contextual Al's pricing model
Learn how our token-based pricing model for Component APls makes it easy to keep costs in check as your workload scales
Platform
Delivering superior accuracy and performance
Understand how the Contextual Al Platform jointly optimizes and orchestrates our components to further improve end-to-end accuracy
Security
Protecting and safeguarding your enterprise
See how Contextual Al enforces robust security controls, stringent data protection, and comprehensive compliance measures
AI that understands your business
Build smarter, context-aware AI, that understands your data, workflows, and edge cases.