Government Savings Bank · AI Laboratory · Est. 2026

GSBAIL

built in-house!

Intelligence that stays at home. Models trained, tested and served inside the bank.

We research and build language models, document intelligence and financial scoring for Thailand's national savings bank, with every byte inside infrastructure the bank controls.

See the four projects →🤗 Hugging Face
0flagship projects
0Bparameters in research
0%local-first design
0customer files published

We build AI a national bank can stand behind: transparent enough to audit, robust enough for millions of customers, precise enough to earn the trust of every household.

Four projects, one stack.

all local-first ↓
01

KaoChat LLM

Local chatbot LLM

An in-house large language model assistant that serves chat on the bank's own infrastructure. Every conversation stays inside the organization's security boundary.

On-prem LLMThai languageBank infrastructure
active
02

KaoOCR

Local OCR LLM

Reads and extracts information from financial documents entirely within the organization. Built for heterogeneous layouts, Thai text and numerically sensitive fields, with confidence scoring and human review for uncertain results.

Domain-adaptiveLocal inferenceConfidence-aware
active
03

Kao AI Research Laboratory

Research LLM fine-tuning · 27B

Our core research program: investigating and fine-tuning a 27-billion-parameter language model for banking. The foundation for the lab's next generation of in-house models.

27B parametersFine-tuningOpen research
research
04

Kao-LEAF Score

Financial scoring using AI

Learned Embeddings for Automated Feature representation. LEAF learns merchant financial behavior from transaction histories, keeping behavior representation, credit-outcome prediction and lending policy as separate, auditable layers.

Temporal embeddingsCredit scoringInterpretable

Project page ↗

active

Three principles.

more than accuracy
01 · TrustTrustworthy

Rigorous evaluation, fairness auditing and governance from day one, not bolted on after deployment.

02 · ScaleScalable

Systems built for national-scale workloads, from chat to transactions.

03 · ClarityInterpretable

Outputs that can be explained to regulators, auditors and the customers behind every number.

Open models & artifacts

Find us on Hugging Face.

Checkpoints and research artifacts from Dr. Teerapong Panboonyuen are published here as the lab's work matures. Only public, authorized artifacts are released; customer data and internal documents are never published.

🤗 Open profile ↗
🤗 kaopanboonyuen
huggingface.co/kaopanboonyuen
// more soon…
Dr. Teerapong Panboonyuen
Founding head of lab

Dr. Teerapong Panboonyuen

ดร. ธีรพงศ์ ปานบุญยืน (ดร. เก้า) · Vice President of AI

Dr. Kao leads GSBAIL's strategy and research direction, setting the technical and ethical standards for AI across Thailand's national savings bank. Creator of KaoChat, KaoOCR and the GSB-LEAF framework, with a focus on systems that are rigorously evaluated, transparently governed and genuinely useful at scale.

Full profile ↗🤗 Hugging Face ↗

Publications, soon.

not yet published ✎

Placeholder BibTeX entries, marked "in preparation". Titles and details may change before publication.

@unpublished{panboonyuen2026kaochat,
  title  = {{KaoChat}: A Local Large Language Model Assistant for
            Bank-Controlled Infrastructure},
  author = {Panboonyuen, Teerapong},
  year   = {2026},
  note   = {Manuscript in preparation, not yet published}
}
@unpublished{panboonyuen2026kaoocr,
  title  = {{KaoOCR}: A Domain-Adaptive Local {OCR} Framework for
            Organization-Specific Document Recognition},
  author = {Panboonyuen, Teerapong},
  institution = {Government Savings Bank},
  year   = {2026},
  note   = {Manuscript in preparation, not yet published}
}
@unpublished{panboonyuen2026kaoresearch27b,
  title  = {Domain Adaptation of a 27B-Parameter Language Model
            for Banking: A Research Program},
  author = {Panboonyuen, Teerapong},
  year   = {2026},
  note   = {Manuscript in preparation, not yet published}
}
@unpublished{panboonyuen2026gsbleaf,
  title  = {{GSB-LEAF}: Learned Embeddings for Automated Feature
            Representation in Temporal Financial Behavior Modeling},
  author = {Panboonyuen, Teerapong},
  year   = {2026},
  note   = {Manuscript in preparation, not yet published}
}