System Analysis & Design
We analyze your business needs and design efficient, scalable, and future-ready solutions.
Services
We provide a wide range of services
From initial analysis to long-term support, we help you plan, build, and run software that fits your business — not the other way around.
How can we help you?
With over 20 years of experience in software development and AI, we cover every stage of your project — from understanding your challenges to delivering and maintaining the right solution.
Custom Software Development
Tailored software built to fit your unique requirements — from web and mobile apps to enterprise backends.
AI & Agentic Automation
We build AI agents and automate workflows to reduce manual effort and unlock new productivity.
Cloud Migration & Integration
Move to the cloud and integrate AI into your existing systems with a secure, reliable architecture.
Digital Transformation
We guide your organization through digital change with strategy, technology, and hands-on delivery.
Managed Services & Support
Ongoing maintenance, monitoring, and support so your systems stay reliable and up to date.
Training in Spec-Driven Development with AI
Hands-on training for software developers and teams in spec-driven development (SDD) with AI — from writing clear specifications to reviewing what the AI builds. We can also join your team as Forward Deployed Engineers (FDE) in AI, working alongside your developers and taking them to the limit of what software development with AI can do.
Our approach
How we build AI-powered applications
Every AI project we deliver follows the same path, from a business need to an application people can use. The steps are the ones good software has always needed — with the extra care AI requires: answers grounded in your own information, and behaviour that is tested, not assumed.
It follows our 6-D Process, with validation as a step of its own.
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Stage 01
Business Understanding
We start with the problem, not the technology: who will use the solution, which questions or tasks take up their time today, and what a good result looks like.
- What goes in
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- Your goals
- Your users
- Examples of real questions and documents
- What comes out
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- A shared description of the problem and the outcome you expect
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Stage 02
Requirements & Specification
We break the solution into small user stories, each with acceptance criteria that can be checked. Every story gets a written plan before development starts, so you can see what will be built and in which order.
- What goes in
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- The problem description
- Sample data
- What comes out
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- User stories with acceptance criteria
- A plan for each story
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Stage 03
Solution & Architecture Design
We decide how the application gets its knowledge, how your users reach it, and how it connects to what you already use. The design follows your information — connected facts, documents and spreadsheets each need a different approach.
- What goes in
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- User stories
- Your data sources and existing systems
- What comes out
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- An architecture
- The chosen channels and integrations
- Recorded design decisions
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Stage 04
AI & Application Development
We build in small, working increments: bringing your content in, preparing it as a knowledge base, connecting the AI model, and delivering through the channel your users prefer. The AI is set up to answer from your information and to say so when the answer is not there.
- What goes in
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- Plans
- Your content
- What comes out
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- A working increment for every story
- A knowledge base built from your own material
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Stage 05
Validation & Testing
The software is tested automatically. So is the AI: we check its answers against example questions with known answers, and check that it declines what it should not answer. Results are reviewed before a story is closed.
- What goes in
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- The working increment
- Example questions with expected answers
- What comes out
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- Test results
- Review findings
Feedback loop. Anything that falls short goes back to design and development before it reaches your users.
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Stage 06
Deployment & Operation
You receive a self-contained application, set up for your environment through configuration. Data sources, the AI model and limits can be adjusted without reprogramming, and conversations are recorded so that any answer can be traced.
- What goes in
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- The validated application
- Your environment and settings
- What comes out
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- A running application
- Setup documentation
- A record of use
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Stage 07
Continuous Improvement
Real use shows where answers are missing or weak. We look for the cause, fix it, and check the result — and new needs become new user stories.
- What goes in
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- Records of use
- Your feedback
- New business needs
- What comes out
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- Improvements
- New stories for the next round
Feedback loop. What we learn in operation becomes the next round of user stories.
How our engineers and AI coding agents work together is described in the next section.
How we work
Spec-driven development with AI
We build our solutions with AI coding agents — and with a written specification in charge. The specification says what is to be built and how it will be checked; the AI does the writing; people decide and accept. That keeps the speed of AI without losing control of the result.
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Specify
People + AI
A feature is discussed and written down as a user story with acceptance criteria that can be checked.
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Plan
AI drafts, people decide
An implementation plan is written for the story and refined before any code exists. It is kept with the code.
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Implement
AI agent
The AI coding agent writes the code and the tests from the plan, runs the tests and corrects what fails.
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Review
AI + people
Plan and code are checked against the story. Findings become fixes, or new stories of their own.
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Accept
People
A person runs the application and checks the feature. If it is right the story is closed; if not, it goes back to the step that needs it.
What this means for you
- The specification, not a chat history, is the source of truth
- People decide what is built and accept the result
- Plans and reviews are kept with the code, so every decision can be traced
- Work arrives in small stories you can check one at a time
Building blocks
What we build, and how it reaches your users
Most AI applications combine the same three building blocks — a knowledge base, a chatbot and an agent — and reach people through one or more channels. This is how each one comes together.
RAG
Knowledge base
Retrieval-augmented generation, or RAG, means the AI looks up your information before it answers, instead of relying on what it happens to know.
- Collect. We connect the place where your content already lives: a shared folder, documents, spreadsheets.
- Prepare. Text is extracted, cleaned and split into passages that keep related facts together.
- Index. Each passage is stored so it can be found by meaning, together with the source it came from.
- Retrieve and answer. For every question, the most relevant passages are found and the AI answers from them, naming the source.
- Refresh. New and changed content is brought in again, so answers stay current.
Chatbot
Chatbot
A chatbot is the conversation on top of the knowledge base: it gives the answers a voice, a memory and limits.
- Define the role. Who the assistant speaks for, its tone and languages, and what it must not answer.
- Connect the knowledge. Every answer is drawn from the knowledge base, not from guesswork.
- Add memory. Earlier messages are remembered, so follow-up questions are understood.
- Set the fallback. What the assistant says when the answer is not in your information.
- Record. Conversations are kept for review, so weak answers can be found and fixed.
Agent
Agent
An agent does more than answer: it uses tools or runs a workflow — looking something up, producing a document, notifying the team.
- Choose the task. One clearly bounded job, with a clear line for what the agent may do on its own.
- Give it tools. Each tool is a defined function — search, calculate, generate — that the agent can call.
- Keep exact work in software. Searching and calculating are done by code, so results are not guessed by the AI.
- Contain failures. A step that fails is retried or reported, and does not stop the rest.
- Test each task. Every tool and workflow is tested on its own before the agent is trusted with it.
One assistant, three channels
The same assistant, with the same knowledge and the same rules, can be reached wherever your users already are. Adding a channel does not mean building a second assistant.
Telegram
A bot in an app your team or customers already use. Quick to set up, and well suited to internal teams and notifications.
Conversations on a business WhatsApp number, connected through a messaging provider, for customers who prefer to stay in WhatsApp.
Web chat
A chat page in your own branding, opened from your website with nothing to install.
How a channel is added
- Register. Set up the bot, the business number or the chat page.
- Connect. Link it to the assistant that already exists.
- Adapt. Fit replies to the channel: message length, formatting, waiting indicators.
- Decide access. Open to everyone, or limited to authorised users.
Technology
Built on Java, Spring Boot and Spring AI
The process does not depend on a particular technology. Our own solutions share one foundation, chosen because it is proven in business software and fits into the systems companies already run.
Java
A mature language and runtime with long-term support, widely used for business-critical systems and easy to operate and maintain.
Spring Boot
The application framework: web, data and configuration in one self-contained application that runs wherever Java runs.
Spring AI
Connects the application to AI models, knowledge search, conversation memory and tools — and lets the AI model be changed through configuration instead of a rewrite.
Around this foundation
- PostgreSQL
- Vector and graph search
- Versioned database migrations
- Cloud and local AI models
- Automated tests against real databases
One process, different domains
The process stays the same; the application is shaped by your business. Agents and chatbots for customer service in healthcare and e-commerce, for reporting, and for prominent personas such as presidents show how far apart the results can be.
Conversational AI
Healthcare customer service
An assistant that answers patients’ questions about doctors, schedules and services, in Indonesian and English, through web chat and a messaging app. Its knowledge comes from the hospital’s own documents, and it stays within clear limits on medical and personal topics.
Conversational AI
E-commerce customer service
An assistant that answers shoppers’ questions about products, prices and how to order, through web chat and a messaging app. Its knowledge comes from the shop’s own product information, so customers get help at any hour without waiting for staff.
AI-powered reporting
Business reporting
An assistant that turns sales spreadsheets into a ready-to-read management report, tells the team when it is available, and answers follow-up questions in a messaging app. The figures are calculated by the software; the AI explains them and names the report each answer comes from.
Persona chat
Prominent personas
A chatbot built around a prominent public figure, such as a president. People ask about that person through web chat and a messaging app, and the answers draw on a knowledge base prepared for that persona.
What stays the same
- Your own information is the source of every answer
- The assistant says when it does not know
- Answers name where they come from
- Follow-up questions are understood in context
- Conversations are recorded for review
- Settings change through configuration, not reprogramming
What changes with your domain
- The kind of information: documents, images and video, or structured data
- How knowledge is organised and searched
- The channel: web chat, messaging app, generated reports
- The safeguards: topic limits, access for authorised users, protection of figures
- What the AI produces: answers, documents, reports
Our Valued Partners
We take pride in working alongside trusted partners who share our vision for innovation.
