A demo is not a product
A prototype answers well in a meeting, then fails on real users, real data and real load because nobody engineered it for production.
Software development company in the Philippines
AI built into your software, by engineers who ship.
We design, build and run AI features, private LLMs and new products, working with AI agents every day so you get more tried, faster, with every change still reviewed by a person.
Why AI development projects go off track
Your data cannot leave
Customer records, contracts and source code cannot go to a public AI service, so off-the-shelf tools are ruled out.
No way to tell if it works
Without tests for the AI’s answers, every model update or prompt change is a gamble that users discover first.
AI bolted on, not built in
An AI feature that ignores your existing systems, permissions and data model creates more work than it saves.
Custom AI development, end to end
One team for the AI, the software around it and the infrastructure it runs on.
AI features in your software
Search, summaries, document reading, assistants and agents built into the web apps, internal tools and APIs you already have.
Private LLMs on your own data
Models that run in your own cloud or servers and answer from your documents, so sensitive data never leaves your control.
New AI products
From first prototype to a product in production, for companies launching something new and needing an engineering team to build it.
Evaluation and operations
Test sets for the AI’s answers, monitoring, cost tracking and security, so the system keeps working as models and usage change.
How we build
Define and prototype
The problem, the data and how success will be measured, then a working prototype on real examples.
Build for production
The AI and the software around it, with permissions, logging and tests for both the code and the answers.
Launch
Released in stages to real users, with monitoring in place from the first day.
Run and improve
We keep improving quality, speed and cost, and update the system as better models arrive.
Examples of what we build
Internal knowledge assistant
Before: staff search shared drives and ask colleagues. After: a private assistant answers from company documents, citing its sources.
Document reading in an existing app
Before: users type data from uploaded forms. After: the app reads the document and fills in the fields for review.
Semantic search for a catalog
Before: search only matches exact words. After: customers describe what they need and find the right product.
An AI product from scratch
Before: an idea and a slide deck. After: a product in production, with users, monitoring and a team to keep improving it.
Questions we often hear
What is a private LLM?
A large language model that runs in an environment you control, such as your own cloud account or servers, instead of a public AI service. It lets you use AI on sensitive data without sending it to third parties.
Can you add AI to software another team built?
Yes. We start by reading the code and understanding the data, then add AI features alongside your existing team or take over the work if you prefer.
Which models do you use?
Whichever fits the task, budget and data rules: commercial models through providers with appropriate data terms, or open models hosted privately. We test candidates on your own examples before choosing.
How do you make sure the AI gives correct answers?
We build test sets from real examples, measure answers against them on every change, ground answers in your own data, and design for a person to review anything with real consequences.
Build AI into your product.
Book a discovery call. Tell us what you want to build, and we will show how we would approach it and what it takes.