AI integration and RAG that answers from your Ohio company's own documents and systems.
We connect language models to your documents, databases and internal tools so people get answers grounded in your data, with permissions respected. We build these remotely for organizations across Ohio 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 here
RAG, short for retrieval-augmented generation, has a language model fetch the relevant passages from your own material before it answers, and show the source. Integration is the connective work: links to document libraries, ERP and policy systems, ticketing tools and the identity layer that decides who may see what. Many Ohio organizations hold decades of knowledge in procedures, specifications, filings and correspondence, scattered across drives, portals and inboxes. A grounded search-and-answer layer makes that knowledge usable without asking everyone to learn where each file lives. It also avoids the habit of pasting company documents into public tools, because the system runs under your control.
Integration use cases for Ohio industries
These are illustrative use cases we scope for Ohio organizations, not results from past work. The Readiness Sprint checks whether your content and permissions suit each one.
| Industry | Use case | Sources connected |
|---|---|---|
| Manufacturing | Work-instruction and quality-record search for supervisors | Work instructions, nonconformance records, ERP |
| Healthcare | Credentialing and policy assistant for administrators | Policy library, payer manuals, credentialing files |
| Insurance and finance | Underwriting-guideline and procedure assistant | Guidelines, forms library, policy system |
| Logistics | Carrier and customer terms lookup for dispatchers | Contracts, rate sheets, service-level documents |
| Retail | Store-operations and merchandising knowledge assistant | Playbooks, planograms, vendor agreements |
Manufacturing: the answer is in a report nobody can find
A shift supervisor facing a recurring defect wants to know what was tried last time. The answer sits in a nonconformance report from two years ago, linked to a corrective action and a revised work instruction. A retrieval layer connected to those records can answer in plain language and cite each source, and a link to the ERP can add current part revisions and open orders. Document permissions flow through, so customer-confidential drawings stay restricted to those cleared for them. For plants supplying regulated industries, the citation trail also helps show that an instruction was current when it was used.
Insurance and finance: guidelines that change quarterly
Underwriting guidelines, filing requirements and servicing procedures change regularly, and staff often work from a remembered version. An assistant grounded in the current guideline library answers a specific question, quotes the passage and states which document and version it came from. Because Ohio's insurance data security expectations extend to the vendors a carrier or agency uses, we document how the retrieval system handles data, where indexes live, what is logged and how long it is retained, so your compliance team has material to review. The assistant supports staff decisions; it does not make coverage or credit decisions itself.
Healthcare and logistics: policies, payers and contracts
Hospital administrators field questions about payer requirements and internal policy that change by contract and by year. A grounded assistant over payer manuals and internal policies shortens the search, with citations so staff verify the original. Dispatchers and account managers in freight have a parallel problem: rate sheets, accessorial rules and customer service terms differ for every account. A retrieval layer over those contracts lets them answer a customer without paging through PDFs. In both settings we start read-only, limit the corpus to approved documents and expand only after the evaluation results justify it.
Preparing your content before we index it
Retrieval quality depends less on the model than on the material it searches. Before building, we review a sample of your documents with you: which are authoritative, which are drafts, which have been superseded, and which should never be indexed at all. Ohio organizations with long histories often discover several competing versions of the same procedure, scanned images with no searchable text and folders whose permissions nobody remembers setting. Resolving those is unglamorous but decisive. We also agree on who judges answer quality, since a subject-matter expert reviewing twenty real questions teaches us more than any benchmark. The result is a smaller, cleaner corpus and a system your staff actually trust.
Evaluation, privacy and operations
We build an evaluation set from real questions before launch and rerun it after any change to content, prompts or models, because a system that cannot be measured cannot be trusted. Logs and vector indexes carry retention limits, and deletion is designed to reach every copy. The system runs in your cloud accounts under your keys. We hold no certifications and do not imply any. Eastern-time demos and incident coverage are agreed in week one, helped by the fact that Colombia's clock matches yours in winter and trails it by one hour in summer.
Frequently asked questions
Why not just use a public chatbot with our documents?
Pasting company documents into public tools bypasses permissions, retention and review. A RAG system runs under your control, respects document permissions and cites its sources.
Can the assistant connect to our ERP?
Yes, usually read-only at first. Combining a document passage with live ERP data lets answers include current revisions and open orders.
How do you handle permissions?
Source-system permissions carry into the retrieval layer, so a person only receives answers drawn from content they could already open.
How is accuracy measured?
We assemble an evaluation set from real questions, score answers and citations 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.