LankaBangla Securities: একটি Air-Gapped LLM অ্যাপ্লিকেশন লেয়ার

Overview

Ternary's mandate was to deploy an air-gapped open-source LLM environment and build the extensible application layer that mediates end-user interaction with AI capabilities.

The engagement was designed to deliver institutional control and practical workflow value at the same time.

Client & Segment

The client operates in capital markets, where data governance and execution speed are both critical.

This segment requires AI systems that are secure by architecture and directly useful in high-tempo operational decisions.

Problem

Dealer-brokers needed faster access to synthesized information, while retail outreach required more relevance than generic campaign logic.

At the same time, AI deployment had to avoid uncontrolled data exposure and remain compatible with enterprise governance expectations.

Solution

Ternary implemented a governed application layer over an air-gapped open-source LLM and shipped two initial functions.

The first is an AI assistant for dealer-brokers to retrieve and synthesize information quickly; the second is automated marketing for retail traders informed by trade behavior and account context.

Impact

The result is a secure, extensible AI foundation with immediate business-facing functionality across brokerage productivity and retail engagement workflows.

Public quantitative impact metrics are not disclosed in the available source material.

Tech Stack

An air-gapped open-source LLM runtime.

A reusable application layer for role-aware AI workflows and module-based functional expansion.

Why It Matters

This implementation shows how financial institutions can operationalize AI safely.

Control sits at the infrastructure boundary, usefulness at the workflow boundary, and extensibility at the platform boundary.