Venzpire
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Add AI to the product you already have

Your users are asking for AI features. We integrate large language models into your existing web app, mobile app or internal tools, grounded in your own data and without rewriting what already works.

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The short answer

Venzpire integrates LLMs such as OpenAI GPT, Anthropic Claude and Google Gemini into existing products in 3–8 weeks, starting from $5,000. Typical integrations are AI assistants grounded in your data (RAG), document analysis, AI search, content generation and workflow automation, delivered with access controls, evaluation and cost monitoring.

What you get

AI features that feel native to your product, respect your users’ permissions and keep your margins intact.

Led by Venktesh RBuilt with Nithin and Deepak, the same senior team from first call to launch.

Investment
From $5,000, fixed quote after discovery
Timeline
3–8 weeks
Best for
Products that already have users
You own
All code, IP and cloud accounts from day one
After launch
30 days of support included
Apply for a strategy call How we price
  • Codebase and data reviewWe read your architecture and data first, then choose the integration that fits it rather than bolting on a separate system.
  • Retrieval over your data (RAG)Document ingestion, chunking and vector search with pgvector, Pinecone or Weaviate, respecting each user’s access rights.
  • The AI feature itselfAssistant, copilot, smart search, summarisation, extraction or generation, designed into your existing UI.
  • Model strategyThe right model per task, prompt and response caching, and fallbacks between providers, so cost and latency stay predictable.
  • Security and privacyData isolation per customer, PII handling, zero-retention API settings where available, and audit logs.
  • Evaluation and monitoringAccuracy tests on your real data, plus dashboards for quality, latency and cost per request.

Common integrations

  • An in-app assistant that answers questions from your documentation, records or knowledge base.
  • Document AI that reads contracts, tenders, invoices or reports and extracts structured data.
  • AI search that understands what users mean, not just the words they type.
  • Drafting tools that write replies, proposals or reports in your company’s voice.
  • Moving from expensive premium models to cheaper or self-hosted models as usage grows.

What it costs

Typical Venzpire price ranges. Every project starts with a $1,500 discovery sprint that ends in a fixed quote; the sprint fee is credited to the build.

ScopeTypical investmentTimeline
Discovery sprint: architecture review, prototype, fixed quote$1,500 fixed1–2 weeks
Single AI feature in an existing product$5,000 – $12,0003–5 weeks
Production RAG assistant over your data$12,000 – $35,0005–8 weeks
Several AI features with model routing and analyticsFrom $35,0008+ weeks

AI model usage and hosting are billed to your own accounts at cost. How we price

How the project runs

  1. Week 1–2 · $1,500 fixed

    Discovery sprint

    Users, data, model choice and cost per user, pressure-tested. You keep the prototype, architecture and fixed quote.

  2. Weeks 3–10

    Design & build

    A staging link in week one, a live demo every week and a short written update every day.

  3. Before launch

    Evaluate & harden

    Tested on real examples, with guardrails, cost limits and a security review.

  4. Launch & after

    Ship & improve

    Deployed to your cloud and the app stores, with 30 days of support included.

Related work

ReplyMagik — AI RFP response automation screenshot

ReplyMagik — AI RFP response automation

A platform that turns a company's previously approved RFP and security-questionnaire answers into a private knowledge base, then drafts new answers in the company's own voice.

The brief
Sales and security teams were rewriting the same RFP and questionnaire answers for every new deal, with no single source of approved wording.
What we built
Each customer account keeps its own isolated knowledge base, built with OpenAI embeddings, LangChain and a Chroma vector store, and uploads are validated before the AI learns from them. Admin and super admin panels manage knowledge and accounts, and every account gets an API key to generate answers from the tools teams already use. Built on AWS with exports to CSV, Excel and PDF.
  • B2B SaaS
  • OpenAI + RAG
  • Account-isolated knowledge
  • API access
  • AWS

Read the ReplyMagik case study

Bylder — AI fitness coaching chat illustration

Bylder — AI fitness coaching chat

A Flutter mobile app where users chat with an OpenAI-powered coach about training schedules, nutrition, motivation, and sleep.

The brief
Fitness app users wanted on-demand coaching advice without booking a human trainer for every question.
What we built
We built a Flutter app with Firebase authentication (Google, Apple, GitHub, email, anonymous) and topic-based chat screens for training, nutrition, motivation, and sleep, each sending the conversation to OpenAI's chat API and streaming the coach's reply back into the thread.
  • Netherlands
  • Flutter (FlutterFlow)
  • Firebase Auth
  • OpenAI chat API

Read the Bylder case study

Questions about LLM integration

What is LLM integration?

LLM integration means connecting a large language model such as GPT, Claude or Gemini to your own product and data so it can answer questions, analyse documents, generate content or automate tasks inside your existing user experience, with your security rules applied.

What is RAG, and do I need it?

Retrieval-augmented generation (RAG) lets the model answer from your own documents and data instead of only its training. You need it whenever answers must be accurate, current and specific to your business, which is most real products. It also lets you show sources for every answer.

Is our data safe when we send it to OpenAI or Anthropic?

Business API traffic to the major providers is not used to train their models by default, and zero-data-retention options are available for sensitive use cases. We add per-customer data isolation, redact personal data where needed, and can use self-hosted open-weight models when data must not leave your infrastructure.

How do you keep AI API costs under control?

We measure cost per request from the prototype stage, cache repeated work, route simple tasks to smaller and cheaper models, cap usage per plan and alert on spikes. Most clients see predictable monthly AI bills that scale with revenue rather than ahead of it.

Guides on LLM integration

Have an AI product worth building properly?

Tell us what you are building. Venktesh reads every application himself and replies within one business day.

Apply for a strategy call