SimplyFI — AI Agents for Banking

Intelligent automation for Customer Engagement, Compliance, Lending, and Trade Finance.
Models, datasets, and Spaces for document understanding, risk, and decisioning — production-first and audit-ready.

What we publish

• Models: OCR/IE, invoice understanding, entity linking (KYC/AML), credit signals, dialogue agents (RAG+RLHF).
• Datasets: Synthetic & anonymized corpora for invoices, SWIFT/LC patterns, trade compliance, FAQs.
• Spaces: Demo apps for document triage, explainable decisions, and agentic workflows.
• Notebooks: End-to-end examples — from fine-tuning to human feedback (RLHF) and safe deployment.

Prefer bank-grade controls? We support gated access, EULAs, and private org Spaces for sensitive assets.

Highlights

• SIMBA — our production platform for Trade Finance & Banking AI: OCR ➜ extraction ➜ validation ➜ decisioning
• Agentic Workflows — multi-tool agents with grounded retrieval, guardrails, and human-in-the-loop review
• Compliance by design — redaction, PII handling, lineage, prompt/trace logging, and model cards with usage guidance

Explore

Featured models

Replace links above with your actual repos (or keep as placeholders until published).

Example: quick inference

from transformers import AutoModelForTokenClassification, AutoTokenizer, pipeline

repo = "SimplyFI/invoice-extractor"  # update if different
tok  = AutoTokenizer.from_pretrained(repo)
mdl  = AutoModelForTokenClassification.from_pretrained(repo)
ner  = pipeline("token-classification", model=mdl, tokenizer=tok, aggregation_strategy="simple")

text = "Invoice 9081 from Alpha Plastics Pvt Ltd, Total ₹1,24,560 due on 2025-11-15"
print(ner(text))