Build · Practical intelligence
Private AI & Document Search for Michigan Businesses
CoreNet helps Michigan businesses make internal information easier to find and repetitive work easier to manage. From our Trenton base, we scope private AI systems, document search and AI features around the people, data and software involved. Start with one useful task: finding an answer in a document library, summarizing information for review, or connecting an AI feature to an existing application. We assess whether a model, an integration or straightforward software is the right fit.
Discuss an AI projectPrivate knowledge search in practice
A real estate organization needed an easier way to work with hundreds of pages of internal and legal/reference information. CoreNet deployed a private RAG environment so staff could ask natural-language questions against the document library. This was knowledge assistance, not legal advice.
Private AI infrastructure
A private AI environment is configured for an organization’s use and document access needs. We can help scope private or local model deployment, infrastructure, and integration. Hosting location, permissions, and data handling need to be decided for the particular system; the word private alone is not a security guarantee.
RAG & internal knowledge systems
Retrieval-augmented generation (RAG) brings relevant material from a document library into a model’s context when answering a question. It can support internal document Q&A and knowledge search. PDF contents must first be readable or extracted; answers still need checking against their source material.
- Company document search
- Internal knowledge retrieval
- Document Q&A
- Private/local LLM integration
AI-assisted workflows
Useful features can include summarization, classification, information retrieval, and workflow assistance. We consider what the team needs to review, which systems must connect, and where automation can reduce repetitive work.
Document search, automation, or a custom application?
Start with the work your team needs to complete. These examples describe possible project scopes, not features that every business needs.
- Find the source of an answer: search a defined document library and show the supporting passage.
- Move information between systems: use an integration when the steps and rules are predictable.
- Summarize a document: use AI to draft a summary with a person checking the source.
- Manage approvals or customer submissions: build a workflow or portal when records, permissions and status tracking are the main need.
A clear scope before custom AI development
A useful first release starts with a defined workflow and an agreed way to evaluate its answers. We review the source material, user access, existing systems and hosting requirements before choosing the technical approach. Project planning can include:
- One document collection or workflow to address first
- Who can access the system and the information it uses
- Questions and source material to use during evaluation
- Application integrations, deployment and ongoing ownership
What to test before staff rely on an AI answer
A useful evaluation includes questions with known answers, missing answers, conflicting documents and restricted information. Agree on the sources and expected behavior before testing. A confident answer is not evidence that the answer is correct.
- Can a staff member open the source passage and verify the answer?
- Does the system say when the documents do not support an answer?
- Can each user retrieve only the information they are allowed to see?
- What happens when a document changes, is withdrawn, or becomes outdated?
- Who reviews uncertain answers and maintains the document collection?
- What hosting, model, storage and support costs continue after launch?
Choose the tool around the problem
Sometimes the right answer is a large language model. Sometimes it's an API, an automation, a database, or better software. We care more about solving the problem than attaching a label to it.
Questions about working together
What is private AI for business?
Private AI is an environment scoped for your organization and its access requirements. It can use local infrastructure or private cloud services. Hosting, model providers, retention, permissions and data flows need to be checked for the chosen design; the label alone does not guarantee confidentiality.
What is retrieval-augmented generation (RAG)?
RAG connects an AI model to your organization's document library. When a question is asked, the system retrieves relevant excerpts from your documents and provides them to the model to generate an answer based on source material.
Can AI search internal PDFs and documentation?
Yes, provided the documents are readable text or can be extracted. RAG systems can index internal policies, manuals, reference materials, and contracts to answer questions with citations back to the source pages.
Can a company host or run models on private infrastructure?
Yes. Depending on performance and privacy needs, models can be hosted in private cloud instances or on-premises servers so data does not leave your designated boundary.
When is an API or automation better than an AI model?
When the task requires predictable logic, structured data transfer, or calculations, traditional software, APIs, and automations are usually faster, cheaper, and more reliable. We use AI only where language understanding or text synthesis is genuinely required.
What should we bring to an initial AI project conversation?
Bring one workflow, the people who use it, the systems involved and a description of the document collection. Start with non-sensitive examples. We can then discuss access requirements, hosting, evaluation and what a useful first release would include.