AI integration and RAG that answers from your Pennsylvania organization's own documents.
We connect language models to your documents, databases and internal systems so people get answers grounded in your data, with your permissions respected. We build these remotely for organizations across Pennsylvania on Eastern hours. Cloudsoft Industries LLC, a Delaware limited liability company. We serve clients worldwide; our engineering team works from Colombia (UTC-5).
Book a scoping call →What integration and RAG mean in practice
Retrieval-augmented generation, usually shortened to RAG, lets a language model look up relevant passages in your own material before it answers, and cite where the answer came from. Integration is the plumbing around that: connecting to the document stores, databases, ticketing tools and identity systems where your information actually lives, and making sure a person only sees what they are already permitted to see. The result is a search-and-answer layer over content that was previously scattered across shared drives, portals and inboxes. For many Pennsylvania organizations, where knowledge is spread across decades of policy manuals, protocols, specifications and contracts, that is more valuable than a standalone chatbot.
Integration use cases for Pennsylvania industries
These are illustrative use cases we scope for Pennsylvania organizations, not results from past work. The Readiness Sprint checks whether your content and permissions suit each one.
| Industry | Use case | Sources connected |
|---|---|---|
| Healthcare and life sciences | Policy, protocol and procedure assistant for staff | Policy library, protocol documents, intranet |
| Education | Policy and program-requirement assistant for advisors | Catalogs, handbooks, student-services knowledge base |
| Manufacturing | Specification and work-instruction search for engineers | Drawings index, quality documents, maintenance logs |
| Energy | Regulatory and contract lookup for operations teams | Permits, lease files, filing archives |
| Financial services | Servicing guidance and procedure assistant | Procedure manuals, product documents, case history |
Healthcare and life sciences: search over sensitive content
A hospital or research organization usually has policies, clinical protocols, training material and study documents that exist in several versions across several systems. A grounded assistant can answer a staff member's question about the current version of a procedure and show the source paragraph, while ignoring documents the person is not cleared to open. Indexing is where care matters most: we decide with you which repositories are in scope, how document permissions carry into the search layer, what is logged and how long, and which content must never leave your environment. Clinical judgment stays with clinicians, and the assistant is positioned as a finding aid, not an authority.
Education: one answer to a question asked by thousands
Advisors and student-services staff field the same questions repeatedly, and the correct answer lives in a catalog, a handbook, a policy page or a program sheet that changed last semester. A retrieval layer over those approved sources lets staff, and where appropriate students, get an answer that quotes the current policy and links to it. It also reveals gaps: questions that return nothing show where documentation is missing or contradictory. Integration with the student information system can be read-only at the start, which limits risk while the team learns how people actually use the tool.
Manufacturing: engineering knowledge that stays findable
Plants accumulate knowledge in work instructions, non-conformance reports, supplier correspondence and engineering change records. When a veteran retires or a line is transferred, finding the reason a tolerance was set a certain way can take days. A retrieval system over those documents lets an engineer ask in plain language and see the source reports. Connecting it to the ERP or quality system allows answers that combine a document with live data, for instance a part's current revision and the open orders that depend on it. Access rules from the source systems carry through so confidential customer drawings stay confidential.
What we need from you to start
A retrieval project succeeds or fails on the quality and accessibility of the source material, so the first conversation is practical. Which repositories hold the content, and who owns each one? Are documents current, or are old versions still circulating? How are permissions managed today? Who will judge whether an answer is correct? We ask for a small sample of real documents and a list of the questions people actually ask, then test retrieval on them before any build begins. That sample usually reveals the real work: cleaning up duplicate versions, agreeing on a single source of truth and deciding what should be excluded entirely.
Privacy, evaluation and operations
A RAG system is only as trustworthy as its measurement, so we build an evaluation set from real questions before launch and rerun it whenever content, prompts or models change. Retention limits apply to logs and vector indexes, and deletion is designed to reach every copy. The system runs in your cloud accounts, we do not claim certifications we do not hold, and we work inside the security review your organization already requires. Eastern-time demos and incident coverage are agreed in week one; Colombia matches your clock in winter and trails it by an hour in summer.
Frequently asked questions
What is the difference between RAG and fine-tuning?
RAG retrieves passages from your content at question time and cites them, so updating a document updates the answers. Fine-tuning changes the model itself and is rarely the right first step for knowledge search.
Can the assistant respect existing document permissions?
Yes. We carry source-system permissions into the retrieval layer so people see answers only from content they could already open.
Where does our data live?
In your cloud accounts and repositories. We design which sources are indexed, what is logged and how long it is kept.
How do you know the answers are accurate?
We build an evaluation set from real questions, measure accuracy and citation quality before launch, and rerun it whenever something changes.
What does AI integration and RAG cost?
It starts at $15,000, usually after an AI Readiness Sprint at $4,500. AgentOps after launch is $1,500/month.