Agentic AI & Advanced Intelligence
Autonomous AI systems, context-aware LLMs and chatbots, and predictive analytics that turn data into decisions.
The demo is not the hard part. Almost any team can get a model to look impressive on ten hand-picked examples. The distance between that and something you can put in front of a customer, or let write to a production system, is where these projects are won or lost.
We build AI that runs in production: grounded in your own data, measured against a test set agreed beforehand, and bounded so that a wrong answer is contained rather than expensive.
What this includes
- Retrieval over your own documents and systems, so answers cite something real instead of being fluent and wrong
- Agentic workflows that call internal tools and APIs, with explicit limits on what they are allowed to change
- Customer and internal assistants that know when to stop and hand over to a person
- Evaluation harnesses – a fixed test set, scored on every change, so quality is a number rather than an impression
- Predictive and forecasting models, where the pattern is genuinely present in the history
- Cost and latency control: routing, caching, and using a smaller model when a smaller model is enough
How we run it
Evaluation before build. We agree what good looks like and write the test set first. Without one there is no way to tell an improvement from a change, and every release becomes an argument about impressions.
Grounded, not generative alone. Answers are anchored to retrieved sources and shown with them. That is the difference between something staff will trust twice and something they stop opening after a month.
Narrow write access. An agent that reads widely and writes to almost nothing is both useful and safe. The blast radius gets designed before the capability does.
What you get at the end
- A service running against your data, with the evaluation suite in CI
- Scores on the agreed test set, including the cases where it fails
- Logged, reviewable traces of what was retrieved and why the system answered as it did
- A cost model per thousand requests, so scaling up is a decision rather than a surprise
Where this usually starts
A proof of concept that impressed everyone and then could not be deployed. A support queue growing faster than headcount. Or a board asking what the AI plan is, and a need to answer with something that ships.
If there is a model or cloud AI platform you standardise on, say so here. Also worth stating your position on where customer data goes during inference – enterprise buyers in Thailand ask this early, and a clear answer is a differentiator.