Product case study
Private deploymentWedefin Sentio
A natural-language analyst over your own data, and the open web.
A retrieval-augmented analyst that answers questions in plain language across structured and unstructured sources, and runs web research when the answer is not already in house.
Now break it out by region
The challenge
Retrieval eats the day before the analysis starts.
Analysts spend more time finding evidence than weighing it. It sits in databases, in documents nobody has indexed, and on pages across the open web, each in a different shape and none of it answering a question directly. When the material is sensitive, pasting it into a public assistant is not an option.
Our response
One interface over every source, wherever it has to run.
One natural-language interface over structured records and unstructured documents, and web research that brings in external evidence when the answer is not held internally. The model layer is swappable: hosted frontier models, open-source agents, or a local LLM. Security posture becomes a deployment decision, not a limit on what you can ask it.
What we built
A working product, not a concept deck.
- A natural-language query layer spanning structured records and unstructured documents
- An agent harness that plans a job, calls its tools, checks each step, and retries what fails
- Web research that brings in external evidence on demand
- Dashboards and automations built from an answer, not just the answer itself
- A swappable model layer: hosted frontier models, open-source agents, or local LLMs
- Deployment profiles for hyperscaler, neocloud, and on-prem, each with its own security posture
Capabilities demonstrated
What this proves we can bring to client work.
Agent harnesses and tool use
Retrieval-augmented generation
Natural-language query interfaces
Web research and source gathering
Structured and unstructured data pipelines
On-prem, local LLM, and private-cloud deployment
Where this applies
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