DinhNVIndependent AI consultantHi, I'm Dinh.
I advise businesses on AI and lead delivery. For AI development projects, a team handles implementation while I guide the scope, technical direction, and review.
Direct advice from me. Development delivered with a team.
Start with a real problem. Give the technology a clear job.
Work directly with me to find where AI fits your business, assess your data, and turn an idea into a practical implementation plan.
A private enterprise knowledge service in development, designed to run on your infrastructure with local AI and source-backed answers.
I lead the project, and a delivery team implements the agreed scope. Build AI tools and integrations around your business workflows.
Local Knowledge AI is being developed to turn complex documents into searchable knowledge, with the entire processing and inference pipeline running inside your organization.
The local deployment goal: document content and AI requests stay inside your environment during operation. Network isolation and offline requirements will be assessed and tested during the pilot.
Planned capabilities, to be validated during development and pilots
Parse text, scanned pages, and mixed layouts while keeping connections to the original document.
Extract text from scans and use local vision models to interpret images, charts, and diagrams.
Combine keyword and semantic search, rerank results, and link answers to source pages. Flag missing evidence.
Batch ingestion, background processing, progress tracking, retries, and index updates as documents change.
Knowledge-base permissions enforced during retrieval, with access and administrative audit logs.
Run parsing, OCR, vision, embeddings, retrieval, answer generation, storage, and logs in your environment.
This is a target for total document-collection size, not a verified benchmark. Capacity, speed, and accuracy depend on document complexity, models, and hardware, and need to be validated in a pilot.
Explore how text, tables, and a diagram could support an answer in a private knowledge assistant.
Illustrative interface with fictional documents and prewritten answers. This is not a live AI demo or evidence of completed capabilities.
What should a new vendor submit?
In this fictional policy, a new vendor submits a company profile and a service description. The procurement owner checks both before starting the review.
| Document | When to submit |
|---|---|
| Company profile | Before review |
| Service description | Before review |
The procurement owner checks that both documents are present.
Know what we're testing, and what success looks like.
Start with your team, workflow, data, and the outcome that would make a difference.
Agree on representative documents, access rules, infrastructure, and questions to test.
Assess parsing, retrieval, source accuracy, processing speed, and local operation.
Use the pilot findings to scope rollout, ownership, updates, and ongoing maintenance.
Dinh Van · Independent AI consultant
DinhNV is my personal home on the web. I work independently as an AI consultant, so you work directly with me on strategy, discovery, and technical direction.
When a project moves into AI development, I bring together a team for the agreed scope and stay involved in planning and review. Team roles, deliverables, and support are defined for each engagement.
Local Knowledge AI is the private enterprise knowledge service I'm developing. Its local deployment, document capabilities, and scale will be validated through pilots.
Let's start with your problem.
Tell me about your team, your workflow or documents, and the outcome you have in mind. For Local Knowledge AI, include your local infrastructure and privacy requirements.