মূল বিষয়বস্তুতে যান

Artificial Intelligence

Practical AI, built into your products and operations. Not demos — working systems we ship, monitor, and own after launch.

Production-ready, not proof-of-concept

Plenty of AI never leaves the demo stage. We integrate AI into products and operations where it does useful work every day — and we hold ourselves accountable for how it performs once it is live.

LLM applications

Software built on large language models — the technology behind modern AI assistants — applied to your real workflows.

ML pipelines

The behind-the-scenes machinery that keeps a model learning from fresh data, reliably and repeatably.

Retrieval & search

Connecting AI to your own information, so answers come from your business — not guesses.

Model operations

The ongoing care of a live AI system: watching its behavior and keeping its quality steady.

Shipped, monitored, and owned

Our approach to AI is the same one we bring to every system: understand the real workflow first, build with discipline, and stay accountable after launch.

  1. Start with the workflow

    We stay close to the people the system will serve — real context, not assumptions.

  2. Build for production

    We engineer AI the way we engineer everything — for reliability and the long term.

  3. Own it after launch

    We monitor, support, and improve the system in production — we run what we build.

Work behind this practice

A few engagements where this practice did the heavy lifting.

Capital markets · LankaBangla Securities

An air-gapped LLM for a regulated brokerage

Problem

The institution wanted practical AI in daily workflows without exposing client or market data to public AI endpoints.

Approach

We deployed an open-source LLM in an air-gapped environment and built an extensible application layer over it, shipping a dealer-broker assistant and behavior-aware marketing automation as the first two functions.

Outcome

AI adoption without regulatory exposure — and a reusable foundation that takes on new functions without re-architecting the platform.

Read the story
Sports technology · Alley Analytix

Physics-first machine learning inside a bowling ball

Problem

Professional-grade motion metrics had to come from a sensor riding inside a rolling ball — a hostile measurement environment where naive readings distort and results must stand up to coaching and patent scrutiny.

Approach

We anchored every metric in deterministic, physics-grounded signal processing, validated predictions against simulation with known ground truth, and layered a context-aware AI assistant that turns telemetry into coaching guidance.

Outcome

Measurement became decision support: coaches get explainable analytics and contextual answers rather than raw numbers.

Read the story