AI integration and RAG that answers from your data, for New York companies.
Retrieval-augmented generation (RAG) lets a language model answer from your own documents and systems instead of guessing. We integrate it into the software New York companies already use, with permissions, citations and deletion in mind. 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 AI integration and RAG mean in practice
A general-purpose model knows nothing about your policies, your contracts, your archive or your client agreements. RAG fixes that by retrieving the relevant passages from your own sources at the moment of the question and asking the model to answer only from them, with citations. Integration is the surrounding work: connecting those sources, respecting who is allowed to see what, keeping the index in step with the originals and delivering answers inside tools people already use. In a city where decisions get reviewed, the citation is the point: a supervisor can open the passage behind any answer and confirm it.
Integration use cases by New York industry
These are illustrative scenarios for New York companies, not results from earlier projects. Real scope depends on your content, permissions and required accuracy.
| Industry | Typical use case | Sources and systems |
|---|---|---|
| Financial services | Policy and procedure assistant for operations and compliance staff | Procedure manuals, policies, regulatory notices |
| Media and publishing | Archive and rights search for editors and licensing teams | Content archive, contracts, license records |
| Healthcare | Internal policy and payer-rule assistant for administrative staff | Approved policies, payer guidelines, forms |
| Real estate | Lease and building-document question answering | Leases, building rules, vendor contracts |
| Professional services | Internal knowledge search across prior work and templates | Document repository, precedent libraries, proposals |
Policy and procedure search for regulated firms
Operations staff at banks, insurers and asset managers search the same procedure manuals and policy updates every day, usually through folder trees. A permission-aware assistant can answer across those sources, cite the paragraph and respect entitlements so a user only receives answers built from content they may see. Retrieval logs record the question, the passages used and the answer given, which gives a supervisor something concrete to review. Anything client-facing is a draft for a person to approve before it leaves the firm.
Archives, rights and editorial search in media
Publishers sit on decades of articles, images and agreements that nobody can search well. A retrieval layer can index the archive with its metadata, so an editor can ask what the publication has already covered on a topic and receive linked results, and a licensing manager can ask which agreements cover a given territory. Contracts and unpublished drafts are restricted by group, so the assistant never surfaces material a user should not see. When the sources do not contain an answer, the system says so rather than guessing.
Leases, building documents and internal knowledge
Property teams answer questions that depend on a specific lease clause, a house rule or a vendor contract. An assistant connected to those documents can quote the clause with its page reference and leave the interpretation to the manager. Law, accounting and consulting firms can search prior work, templates and proposals while respecting matter-level confidentiality, so a user on one engagement never sees another client's material. Connections are read-only unless a task requires more, and access follows your existing roles.
Permissions, deletion and third-party oversight
RAG systems copy data into indexes, and each copy must be explainable to your risk team. We design so that every indexed chunk traces back to its source record, access rules apply at retrieval time and corrections or deletions at the source can propagate to the index. Sensitive fields can be masked or excluded before indexing, and the system runs in your cloud accounts under your keys. Documentation of data flows and access supports your vendor-oversight process, including for firms subject to 23 NYCRR 500. After launch, AgentOps adds evaluation sets, monitoring and index refreshes. We hold no certification and build to your review's controls.
Scope, price and ownership
AI integration and RAG projects start at $15,000, with the AI Readiness Sprint at $4,500 to confirm sources, permissions and accuracy targets first. AgentOps is $1,500/month after launch. Contracts and invoicing in USD through Cloudsoft Industries LLC (Delaware). Clients own 100% of the code and IP once each milestone is paid. Code lives in the client's repositories from day one. Mutual NDA available.
Frequently asked questions
What is RAG in plain terms?
Retrieval-augmented generation searches your own content for the relevant passages and asks the model to answer from them, with citations, instead of relying on what the model remembers.
Can the system respect who is allowed to see what?
Yes. Access rules are applied at retrieval time, so each user only receives answers built from content they are allowed to see.
How do deletions and corrections reach the index?
Each indexed chunk is traceable to its source record, so a change or deletion at the source can propagate to the index. We design that from the start and review it with your compliance team.
What systems can you connect?
Common targets are document stores, knowledge bases, ticketing and CRM tools, databases and internal portals. Scope depends on available APIs and permissions, which the Readiness Sprint confirms.
What does AI integration cost?
AI integration and RAG projects start at $15,000, the AI Readiness Sprint is $4,500 and AgentOps is $1,500/month.