Data and models inside the boundary
Inference, embeddings, vector store and suitable fine-tuning on local or segregated GPU infrastructure.
BronVast Local Intelligence
Local AI that never loses its connection to sources, rights and human knowledge.
BronVast adds AI after digital data has been made manageable against agreed archival standards. Rules, examples and human corrections created in that work can become task-specific models for the same collection and for other future data flows.
2dA BronVast
No larger model than necessary
Classification, extraction, description, routing and search with the right technique for each task.
Measure before trust
Evaluation sets, confidence thresholds, model cards, human review and drift monitoring.
The route from source to lasting organisational capability
Choose use case
question, risk and success measure
Bound the data
rights, quality and permitted examples
Measure baseline
rules, retrieval and model variants
Implement locally
model, embeddings, vector store and API
Keep evaluating
feedback, drift, versions and fallback
From archival work to dataset
Approved examples become a measurable knowledge base
Correctly described records, exceptions and human corrections do not remain loose project notes. They become managed training and evaluation examples for accountable task-model testing.
Processing knowledge becomes a deployable model
Making data archival creates controlled examples of document types, metadata, exceptions and correct human adjustments. That knowledge can become rules, retrieval, classification or extraction models and be reused later.
- document classification and routing
- field and entity extraction
- summaries and description proposals
- duplicate and similarity detection
- RAG question answering with source references
A local knowledge assistant for colleagues
The context layer can find documents and help determine who holds knowledge or responsibility.
- answer with citations to managed sources
- connect documents to process, department or responsible role
- block or escalate uncertain answers
- respect access per user and information class
- return expert feedback to evaluation and new versions
What the organisation retains
Quality before production
Approved examples, edge cases and measures per task.
Versionable and transferable
Model or configuration, prompts, taxonomy, model card, licences and results.
From pilot to workflow
API, vector store, logging, access rules, monitoring and fallback.
Example: local beside the DMS
The model helps as soon as new data arrives
Inside the organisation's infrastructure the model can classify, extract fields, propose metadata and send low confidence to a review queue. The authorised user remains in control.
Local by design
Fully local means the whole chain stays local
Not only chat, but also extraction, embeddings, vector index, inference, logs and optional fine-tuning can remain within agreed infrastructure. Hardware and model licences are assessed first.
01Managed data02Local embeddings03Vector store04Custom models05Chat/API
Technical reference points
We align the route with recognised standards and specifications where they fit the source, destination and customer responsibility. This is never presented as automatic certification.
Questions about this route
Can everything run without public cloud AI?
Yes, depending on the task, chosen models and hardware, extraction, embeddings, vector store, inference and optional fine-tuning can run locally.
Does 2dA always build a completely new model?
No. We choose the smallest demonstrably suitable solution: rules, retrieval, prompts, an existing local model, fine-tuning or a task-specific classifier or extractor.
Can local chat tell us which colleague to contact?
Yes, when roles, processes, sources and access rules are sufficiently recorded. The assistant can connect answers to controlled documents and responsible functions or teams.
Next step
Choose the right starting point
Start with one source, one process or a read-only assessment. We will define what should remain local, what evidence is required and which route creates value first.