Applied AI products
LLM and multimodal capability embedded in software where it creates a measurable advantage for the user.
AI product development
Capability is easy. Reliability is the product.
We turn AI capability into systems people can actually use: grounded in the workflow, evaluated against the task, and engineered with explicit boundaries.
AI where it belongs.
Agentic systems
Tool-using systems with explicit state, permissions, evaluation and recovery rather than unconstrained autonomy.
MCP servers and integrations
Model Context Protocol servers that expose your systems to AI clients through scoped tools, confirmation for consequential actions and a full audit trail.
Evaluation
Task-level evaluation, adversarial cases, regression suites and operational metrics that reveal whether the system works.
AI governance by design
Architecture, controls and evidence aligned to the consequence of failure, not bolted on at the end.
Use AI for uncertainty. Use software for certainty.
Strong AI products are rarely all-AI. We keep eligibility rules, permissions, calculations, safety gates and other deterministic functions deterministic. Models are used where language, interpretation, synthesis or uncertain reasoning creates genuine value.
AI product development with Digitalis
What is AI product development?
AI product development is the work of turning model capability into a usable product. It includes problem definition, workflow design, data and tool integration, evaluation, guardrails, user experience, observability and the software engineering needed to run the system reliably.
Does every AI product need an agent?
No. Agentic architectures are useful when a system genuinely needs to plan, choose tools, operate across multiple steps or recover from changing state. Many products are better served by deterministic software with a narrow AI component.
Does Digitalis build MCP servers?
Yes. Digitalis designs and builds Model Context Protocol (MCP) servers that let AI clients use an organisation's systems through narrowly scoped tools, explicit permissions, confirmation for consequential actions and an audit trail of every call.
How does Digitalis approach AI safety?
Safety is designed into the product architecture. We separate deterministic and probabilistic functions, define failure modes, evaluate behaviour against real tasks, constrain tool access and make uncertainty visible where it affects decisions.
Can Digitalis evaluate an existing AI product?
Yes. We can assess product architecture, workflows, model behaviour, evaluation design, failure modes and the gap between a prototype and a dependable production system.
Build what matters.
Tell us what you are trying to build, change or understand. A first conversation is about the problem, not the feature list.