Engineering
We build AI systems on hosted language models with retrieval over your own data, rather than fine-tuning or self-hosting, because for most business processes the cost and maintenance burden of the alternatives is not repaid.
Context
The AI layer is where most budget gets wasted. Choosing the right tool (and knowing when the answer is a rule engine) is the difference between a working system and an expensive pilot.
Choices
Every choice below has a reason attached. A stack without reasons is a logo wall.
Where we apply it
Architecture
How a grounded AI system answers a question
A request is matched against your own documented content, an answer is composed from what was retrieved, and anything below the confidence boundary is escalated to a person with full context.
The questions we work through before recommending anything in this area.
Recommending something everywhere is the same as having no opinion.
Rarely. For most business use cases, retrieval over your own content produces better-grounded answers at lower cost, and does not need redoing every time the underlying model improves. Fine-tuning earns its place for narrow, high-volume, stable tasks.
We build AI systems on hosted language models with retrieval over your own data, rather than fine-tuning or self-hosting, because for most business processes the cost and maintenance burden of the alternatives is not repaid.
The AI layer is where most budget gets wasted. Choosing the right tool (and knowing when the answer is a rule engine) is the difference between a working system and an expensive pilot.
Deterministic, rule-based processes: a language model adds cost and unpredictability where a rule engine is correct every time; Decisions with legal or financial consequence that require an auditable, deterministic trail; Any workflow where a confident wrong answer is worse than no answer and no human reviews the output; and Data that cannot leave a regulated environment, unless a compliant deployment has been agreed first
Does the task involve language, or just rules? Rules do not need AI; What is the cost of a wrong answer, and who catches it?; and Where does the data live, and where is it allowed to go?
Capability improves without a migration project, and there is no GPU infrastructure to operate.
Answers are grounded in your documents and can cite them. Cheaper and more auditable than fine-tuning for knowledge tasks.
An agent should only reach the systems its task requires. Scope is a design decision made before the build.
Connected
Bring the existing system and the constraints around it. We will tell you where this fits, where it would be the wrong tool, and what we would leave exactly as it is.
