AI integration and RAG that answers from your Michigan plant's and clinic's own records.
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 Michigan 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 for an engineering-heavy company
RAG, retrieval-augmented generation, lets a language model look up relevant passages in your own material before it answers and cite them. Integration is the surrounding work: connecting to document libraries, product-data and quality systems, ticketing and identity so a person sees only what they may already see. Michigan's engineering-heavy companies accumulate years of specifications, test reports, corrective-action records and customer requirements. When a senior engineer retires, the knowledge of where the answer lives often leaves too. A grounded search layer keeps that institutional memory findable, and a citation beside every answer lets users verify the source rather than trust it blindly.
Integration use cases for Michigan industries
These are illustrative use cases we scope for Michigan organizations, not results from past work. The Readiness Sprint checks whether your content and permissions suit each one.
| Industry | Use case | Sources connected |
|---|---|---|
| Automotive | Customer-requirements and corrective-action search | Customer specifications, quality records, engineering notes |
| Manufacturing | Maintenance and tooling knowledge assistant | Manuals, maintenance logs, tooling records |
| Healthcare | Research-compliance and policy assistant | Protocol library, policy documents, training material |
| Financial services | Lending-policy and compliance procedure assistant | Credit policy, procedure manuals, regulatory notices |
| Agriculture | Buyer-specification and food-safety assistant | Buyer requirements, audit records, process documents |
Automotive: customer requirements that fill a hundred binders
Each vehicle manufacturer has its own supplier manual, its own quality requirements and its own way of naming things. A quality engineer who needs to know how a particular customer expects a deviation request to be documented can lose an afternoon in PDFs. A retrieval layer over the customer manuals, past corrective actions and internal procedures answers in plain language and cites the exact section. We keep customer-confidential material segregated, so an engineer assigned to one program does not see another customer's documents, and we log queries so a reviewer can trace how an answer was produced.
Manufacturing: maintenance knowledge on the shop floor
A technician on second shift facing an unfamiliar fault code wants the history of that machine, the relevant manual page and what was done last time. A grounded assistant connected to the maintenance system and the manuals can supply that, with sources, on a tablet or workstation. It is most useful in plants with many generations of equipment, where tribal knowledge is the real documentation. We start read-only and limit the corpus to approved manuals and verified maintenance entries, then widen the scope as evaluation results justify it. Safety-critical procedures are always linked to the controlled original rather than paraphrased.
Healthcare, finance and agriculture: policies and audits
Research offices and hospital administrators need the current version of a policy or protocol and the reason behind it. Lenders and credit unions need credit-policy and procedure answers that cite the governing document, and Michigan's insurance data security expectations for licensed carriers mean vendor documentation matters; we provide data-flow descriptions for your compliance staff. Food and agricultural businesses face buyer audits and need to retrieve specifications, certifications and process records quickly. In each setting the assistant is positioned as a finding aid with citations, not as the authority, and sensitive repositories are included only after permission handling is confirmed.
Start narrow, then widen
The most reliable path is a narrow first release: one program, one plant or one department, a defined set of sources and a small group of users who give blunt feedback. We watch which questions return weak answers, fix the content or retrieval settings behind them and only then widen the scope. This approach also keeps the permission model manageable, since each new source brings its own access rules. A Michigan team that starts with, for example, one customer's requirements library gets a working reference quickly and a clear picture of what the next source should be.
Preparing the corpus and measuring quality
Retrieval quality depends on the material being searched. Before building, we review a sample with you: what is authoritative, what is obsolete, what must be excluded. Michigan manufacturers frequently find multiple revision levels of the same instruction in circulation, which a retrieval system would otherwise treat as equally valid. We then build an evaluation set from real questions and rerun it whenever content, prompts or models change. Logs and vector indexes have retention limits, deletion is designed to reach every copy, and the system runs in your cloud accounts. We hold no certifications and do not imply any. Demos and incident coverage follow your Eastern schedule.
Frequently asked questions
Can we keep different customers' documents separate?
Yes. Source-system permissions and program-level segregation carry into the retrieval layer, so people only receive answers from content they may already access.
Will it replace our quality system?
No. It sits beside it, makes records easier to find and cites the controlled original. The quality system remains the system of record.
Where does the data live?
In your cloud accounts and repositories. We design which sources are indexed, what is logged and how long it is kept.
How is answer quality measured?
We build 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.