AI Agents
Software that plans a task, calls the tools it needs and reports what it did — built against your systems rather than a demo environment.
Systems that reason over your own information.
AGENTSLLMsRAGVISION
Software that plans a task, calls the tools it needs and reports what it did — built against your systems rather than a demo environment.
Language models applied to a defined job, with the prompts, evaluation and guardrails that make the behaviour repeatable.
Answers grounded in your documents and data, with the retrieval layer, indexing and citation path engineered as part of the system.
Extracting structure from images and video — detection, classification and inspection wired into an operational workflow.
Turning unstructured text into something a system can act on: classification, extraction, summarisation and routing.
Models and rules combined so a decision can be made consistently, explained afterwards, and adjusted as conditions change.
By treating retrieval as part of the system rather than a prompt trick: answers are grounded in passages retrieved from your own indexed documents, the citation path is engineered so a reader can check the source, and the prompts, evaluation and guardrails are what make the behaviour repeatable rather than a one-off demo.
We design for it. In Mindora, our AI journaling platform, the AI layer falls through Gemini to Anthropic to HuggingFace and finally to deterministic heuristics, so mood detection gets less precise during an outage but keeps working, and a request never hangs waiting on someone else's API.
Yes, with the controls built in from the start: scoped permissions, approval steps for consequential actions, and a record of what the agent did. Agents are built against your real systems rather than a demo environment, so those controls are tested where they matter.
No. The capability covers AI agents, LLM applications, retrieval-augmented generation, computer vision, natural language processing and intelligent decision systems that combine models with rules so a decision can be explained afterwards.
Tell us what you're trying to build, automate, or transform.