Build an Automated Information Integrity Audit Pipeline with LangChain and Modal
Construct a scalable, multi-modal content verification pipeline for regional media monitoring. Using LangChain for stateful workflow execution and Modal for serverless GPU inference, your workflow will analyze digital media streams, verify factual claims, and flag deepfakes efficiently.
What you are building
The core problem, expected build, and operating context for this challenge.
Build a serverless LangChain workflow running on Modal GPU infrastructure to analyze public media integrity and detect misattribution.
How work is evaluated
Evaluate accuracy of claim verification and detection speed on Modal infrastructure.
Shared data for this challenge
Review public datasets and any private uploads tied to your build.
How submissions are scored
These dimensions define what the evaluator checks and which criteria separate a passable run from a strong one.
trust_score_in_range
Trust score calculated between 0.0 and 1.0
This dimension contributes its full weight only when the submission satisfies the requirement. Partial credit is not awarded.
verification_precision
Precision of classified claim statuses • target: 0.88 • range: 0-1
This dimension contributes its full weight only when the submission satisfies the requirement. Partial credit is not awarded.
What you should walk away with
Build a LangChain multi-step chain for claims extraction and media auditing
Deploy custom audio/image deepfake models onto Modal serverless GPUs
Integrate vector search indices for rapid fact-checking against trusted sources
Output standardized trust scores for digital news publications
Reference links and supporting material
Curated collection of regional news articles with human-annotated factual claims and misattribution flags.
How this agent runs
Evaluate accuracy of claim verification and detection speed on Modal infrastructure.
Challenge input
JSON containing article_text and article_id
LangChain
Popular agent framework for stateful task chaining.
Modal
Provides serverless model execution infrastructure.
Evaluated output
JSON containing claims_list with fact_check_status and trust_score
- Trust score calculated between 0.0 and 1.0
- Precision of classified claim statuses • target: 0.88 • range: 0-1
- Benchmark: FactCheck_Bench_v2
- Verification Precision target: 0.88
- 1 public reference case
- Python execution harness
- Python sandbox (unavailable on Versalist)
View technical recipe
Configured tools
- Modal · Optional
- LangChain · Required
- Langchain · Optional
- Modal · Optional
- LangChain · Required
- Langchain · Optional
Evaluation contract
- trust_score_in_range · Weight 1
- verification_precision · Weight 1
Recipe state
This is a preview. The configuration can change before the evaluation recipe is locked.