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Challenge

Multilingual Corporate Banking Onboarding Agent with Pydantic AI and Sarvam AI

Streamlining corporate bank account opening for regional enterprises requires multi-dialect document validation and verification. Build an automated onboarding workflow using Pydantic AI and Sarvam AI to inspect corporate registration documents. Deliver accurate validation schema checks and reduce manual document verification cycles.

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Challenge brief

What you are building

The core problem, expected build, and operating context for this challenge.

Build an automated corporate onboarding verification pipeline using Pydantic AI and Sarvam AI models.

Delivery guide

How work is evaluated

Evaluation

Evaluates field extraction accuracy and valid schema output from multi-page corporate docs.

Datasets

Challenge datasets

Public datasets, plus the private datasets you have access to.

Evaluation rubric

How submissions are scored

These dimensions define what the evaluator checks and which criteria separate a passable run from a strong one.

Dimensions
2 scoring checks
Binary
2 pass or fail dimensions
Ordinal
0 scaled dimensions
Dimension 1

uen_format_check

Validates that UEN follows standard Singapore regex patterns.

Binary check

This dimension contributes its full weight only when the submission satisfies the requirement. Partial credit is not awarded.

Dimension 2

field_extraction_accuracy

Ratio of accurately extracted fields. • target: 0.98 • range: 0-1

Binary check

This dimension contributes its full weight only when the submission satisfies the requirement. Partial credit is not awarded.

Learning goals

What you should walk away with

  • Construct Pydantic AI validation agents for corporate registration extraction

  • Utilize Sarvam AI models for multilingual document parsing across Indic and SEA languages

  • Implement cross-field consistency validation (UEN, Director names, Shareholder structures)

  • Automate exception handling for incomplete corporate filings

Resources and assets

Reference links and supporting material

Dataset notes

Synthetic dataset of multi-language ACRA corporate registration profiles and bank application forms.

Challenge setup

Evaluates field extraction accuracy and valid schema output from multi-page corporate docs.

Challenge input

Unstructured text representation of corporate filing

Pydantic AI

Assigned framework for typed agent validation.

Sarvam AI

Assigned AI tool for regional language extraction.

Evaluated output

Validated Pydantic JSON schema

Evaluation checks
  • Validates that UEN follows standard Singapore regex patterns.
  • Ratio of accurately extracted fields. • target: 0.98 • range: 0-1
Configured evaluation evidence
  • Benchmark: DocKYC-Extract-Bench
  • Field Extraction Accuracy target: 0.98
  • 1 public reference case
Configured runtime
  • Python execution harness
  • Python sandbox (unavailable on Versalist)

Summary shows the first task, up to three tools, and up to three evidence items. Open the technical recipe to view all configured items.

View technical recipe

Configured tools

Action Space
  • Pydantic AI · Required
  • Sarvam AI · Optional
  • Arcee AI · Optional
Orchestration
  • Pydantic AI · Required

Evaluation contract

  • uen_format_check · Weight 1
  • field_extraction_accuracy · Weight 1

Metrics

  • field_extraction_accuracy · Target 0.98

Benchmarks

  • DocKYC-Extract-Bench
  • general

Reference cases

  • 1 public reference case

Workflow tasks

  1. Task 1

    Input: Unstructured text representation of corporate filing

    Output: Validated Pydantic JSON schema

    Execution harness: Python

Evaluation tools

  • Pydantic AI · Agent Architecture
  • Sarvam AI · Multilingual AI Service

Recipe freeze marker

No recipe freeze time is recorded.

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