What is generative AI integration, as opposed to a chatbot widget?

A widget wrapped around a public model will invent answers from the open internet. Integration means retrieval from your corpus, auth, citations, logging, and a UI that sits in the product people already use. Zupitrix builds that system — not a skin on ChatGPT.

Do we have to use OpenAI?

No. We are model-agnostic. OpenAI, Anthropic, Google Gemini, Azure OpenAI, or a hosted open model in your VPC are all options. We choose against data residency, cost, and the quality of answers on your evaluation set — not a vendor slide.

How do you stop the model from leaking data between teams?

Retrieval is filtered by the signed-in user’s roles and the ACLs on each document. Embeddings and chunks carry the same permission tags as the source. We do not build a single shared index that everyone can query unless that is explicitly the design.

How long does a RAG assistant take to ship?

A focused internal assistant on a clean corpus can reach a usable staging build in 4–6 weeks. Production with SSO, audit logs, connectors, and evals is typically 6–12 weeks. Messy file shares, multiple tenants, or write-actions into CRM add time because permissions and evals are the work.

Who owns the prompts, indexes, and code?

You do. Repositories, prompt templates, evaluation sets, and vector indexes we create for you transfer to your accounts. Third-party model APIs stay on your keys. We deploy into your cloud so there is no lock-in after handover.

What if our documents are not ready?

We will say so on the first call. Duplicate drives, unnamed scans, and no owners will not produce a trustworthy assistant. Sometimes the right first step is a search or taxonomy project, not a model. We would rather delay a training run than ship confident nonsense.

Custom RAG assistantsProduction-ready in 6–12 weeks

Generative AI integration

Assistants that answer from your documents

We connect GPT, Claude, Gemini, or an open model to your files, tickets, and product data — with citations, permissions, and an interface your team will actually use.

GPT-4o
Claude
Gemini
Open models

Start Project See the pipeline

Your corpus, not the webAnswers come from the files, tickets, and systems you approve — not an unbounded public model.
Permissions follow the userRole and folder access apply to every retrieval. Finance does not see HR, and vendors do not see customers.
Citations you can auditEach answer points at the source chunk so people can verify before they act.

Live retrieval

ACL filtered

Retrieve · Rank · Generate3 sources · Verified

Retrieve, generate, and rank documents into a verified cited answer

How it works

From your files to a verified answer

A tight loop — connect, retrieve, generate, cite — so staff and customers get answers they can check.

Connect sources

Docs, wikis, tickets, and SQL land in a permissioned index — not a paste into a public chat.

Retrieve & rank

The query pulls only chunks the signed-in user can see, then ranks them for relevance before the model runs.

Generate answer

GPT, Claude, Gemini, or an open model drafts from those chunks — not from the open web.

Cite & verify

Every reply links sources, refuses when retrieval is thin, and leaves an audit trail for review.

Retrieval pipeline

What generative AI integration includes

We design the retrieval pipeline, the product around it, and the evals that prove it is safe enough to put in front of staff or customers.

Knowledge

Document & data RAG

Ingest PDFs, wikis, tickets, and structured records. Chunk, embed, and retrieve with filters that match how your teams already permission files.

Connectors for Drive, Confluence, SharePoint, and SQL
Incremental re-index on new documents
Tenant and folder isolation

Trust

Cited answers

The assistant quotes sources, refuses when retrieval is thin, and logs the prompt, retrieved chunks, and output for review.

Source links in the UI
Refusal when confidence is low
Audit trail for regulated teams

Product

Assistants inside the workflow

A web or in-app assistant next to the work — not a generic chat window on a marketing page. Streaming, history, and file upload where they help.

Web and in-product chat
Streaming responses
File upload and follow-up questions

Tools

API and CRM actions

When an answer is not enough, the assistant can look up an order, open a ticket, or draft a reply — with confirmation on irreversible steps.

Tool-calling into internal APIs
Human confirm on writes
CRM and ticketing adapters

Quality

Evaluation sets

We score retrieval and answers on your real questions before launch, then keep a regression set so a prompt change cannot silently break support.

Golden questions from your team
Hallucination and refusal tests
Weekly quality on staging

Ops

Cost, latency, and models

Model-agnostic architecture. We pick GPT, Claude, Gemini, or a hosted open model against your latency budget and data-residency rules.

Swap models without a rewrite
Token and cache budgets
PII redaction on the way in

Trust by design

Built so answers stay inside your walls

Permissions, citations, and cloud ownership are part of the architecture — not a slide after launch.

Your corpus, not the web

Answers come from the files, tickets, and systems you approve — not an unbounded public model.

Permissions follow the user

Role and folder access apply to every retrieval. Finance does not see HR, and vendors do not see customers.

Citations you can audit

Each answer points at the source chunk so people can verify before they act.

Your cloud and accounts

Model keys, indexes, and apps sit in your AWS, GCP, or Azure — we transfer the repositories.

Use cases

Where generative AI pays for itself

The useful deployments are narrow: a corpus, a user, and a decision that used to take a search through five tools.

Internal knowledge

Staff ask policy, product, and process questions and get an answer with the page it came from.

Support deflection

Tier-1 questions resolve from your help center and past tickets, with a clean handoff when they should not.

Product and API docs

Developers and customers query current docs instead of a stale PDF or a Slack thread from 2023.

Contract and policy search

Legal and ops retrieve clauses across repositories without dumping the corpus into a consumer chatbot.

Connectors & stack

Plugs into the tools you already run

We wire sources and models into your cloud — then hand over the repos so you are not stuck on our SaaS.

Sources

Google Drive
Confluence
SharePoint
Notion
Zendesk
Salesforce
Postgres
S3

Runtime

LangChain / LlamaIndex
pgvector / Pinecone
OpenAI · Anthropic · Gemini
SSO / SAML
AWS · GCP · Azure
Eval harness

Case studies

Related work we have shipped

LLM · Language learning

Fluent Fox

Conversational practice with virtual AI tutors — short mobile sessions, not a desktop chatbot wrapped in a WebView.

LLM: Tutor conversations
Mobile: Practice loops
Live: Learning product

Education · Retrieval + matching

AI Learning Platform

Students find experts, book live sessions, and learn on their schedule — Next.js, Python, and AWS behind the marketplace.

RAG: Knowledge + matching
AWS: Production cloud
Web: Full-stack app

Why Zupitrix

Why teams ship generative AI with us

Senior engineers only

The people who scope the project build it. You are not handed off from a sales deck to a rotating bench.

Honest feasibility

If a SaaS tool or a rules engine is enough, we say so on the first call. Custom AI has to earn the spend.

Your code and IP

Repositories, prompts, eval sets, and models we train for you sit in your accounts. No vendor lock-in tax.

Direct communication

Backlog, pull requests, and weekly demos. You always know what shipped, what is blocked, and why.

Development process

From the first call to code handover

Discuss requirements

A short call on the decision you need, existing systems, data, and whether custom AI is the right spend. Mutual NDA first.

Define the solution

Architecture, scope, timeline, and cost in a written proposal — including evals, cloud, and what you will own at handover.

Develop the solution

Engineers build and test with regular working releases on shared staging. You see retrieval quality, vision accuracy, or agent tool use on real examples.

Deploy and handover

We deploy into your cloud, transfer repositories, documentation, and IP, and leave monitoring so the system does not silently drift.

FAQ

Questions about generative ai integration

Need generative ai integration?

Tell us what the assistant should know

We will look at the corpus, the users, and whether retrieval is the right spend — then send a written approach and timeline.

Discuss your project

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