AI Integration — Add AI Features to Your App or Build AI-Native Products.
We add LLM-powered features to existing products and build AI-native apps from scratch. Using GPT-4, Claude, Gemini, and open-source models — integrated into your stack, ready for real users.
What's our refund policy for annual subscriptions?
Based on your knowledge base: annual subscribers are eligible for a pro-rated refund within 30 days of purchase. After 30 days, no refunds are issued but the plan remains active until renewal. 📄
AI Features We Build
From adding a single AI feature to building a fully AI-native product
AI Chatbots & Assistants
LLM-powered chatbots trained on your docs, product knowledge base, and support history. Accurate answers — not hallucinations.
Tech Stack:
What You Get:
- ✓Trained on your data (RAG)
- ✓Human escalation fallback
- ✓CRM / helpdesk integration
AI-Powered App Features
Smart summaries, AI writing assistants, auto-tagging, content generation, and intelligent recommendations wired into your existing app.
Tech Stack:
What You Get:
- ✓Plug into your existing stack
- ✓Prompt engineering layer
- ✓Cost-optimised API usage
AI-Powered Search
Replace keyword search with semantic search using vector embeddings. Users find what they mean, not just what they type.
Tech Stack:
What You Get:
- ✓Intent-aware search results
- ✓Filters + ranking control
- ✓Integrates with existing DB
AI Workflow Automation
Automate repetitive tasks using AI agents — document processing, email triage, data extraction, report generation, and classification pipelines.
Tech Stack:
What You Get:
- ✓End-to-end automation flows
- ✓Human-in-the-loop triggers
- ✓Audit trail & monitoring
AI-Native SaaS Products
Build a product where AI is the core feature — not a bolt-on. Writing tools, research assistants, document analysers, and custom AI applications.
Tech Stack:
What You Get:
- ✓Full SaaS with billing
- ✓AI as the core differentiator
- ✓Investor-ready architecture
Fine-Tuning & Custom ML
Fine-tune open-source LLMs on your data for domain-specific accuracy, or train custom ML models for classification, prediction, and anomaly detection.
Tech Stack:
What You Get:
- ✓Domain-specific accuracy
- ✓On-premise deployable
- ✓No API cost per query
Complete AI Tech Stack
LLMs & Models
- • OpenAI GPT-4o / GPT-4
- • Anthropic Claude 3.5
- • Google Gemini Pro
- • Meta Llama 3
- • Mistral / Mixtral
AI Frameworks
- • LangChain
- • LlamaIndex
- • Vercel AI SDK
- • Hugging Face Transformers
- • Instructor (structured output)
Vector & Data
- • Pinecone
- • pgvector (PostgreSQL)
- • Weaviate
- • OpenAI Embeddings
- • Cohere Embeddings
Cloud & Infra
- • AWS SageMaker
- • AWS Lambda / EC2
- • Google Cloud Vertex AI
- • Azure OpenAI (private)
- • Ollama (on-premise)
Which LLM Is Right for Your Use Case?
We recommend the right model after understanding your requirements
| Criteria | GPT-4o | Claude 3.5 | Gemini Pro | Llama 3 (OSS) |
|---|---|---|---|---|
| General Tasks | Excellent | Excellent | Good | Good |
| Long Documents | Good | Best (200k ctx) | Good | Limited |
| Coding | Excellent | Excellent | Good | Good |
| Multimodal (Vision) | Yes | Yes | Best | Limited |
| Privacy / On-Premise | API only | API only | API only | Self-hosted |
| API Cost | Medium | Medium | Low | Free (self-hosted) |
| Best For | Most products | Docs, writing, coding | Vision + search | Private / cost-sensitive |
AI Use Cases We've Shipped
Real AI features for real products across industries
Healthcare
Clinical note summarisation, symptom triage chatbots, patient intake automation, and HIPAA-compliant document processing.
See Case Study →Fintech
Transaction anomaly detection, AI-powered financial report generation, KYC document extraction, and fraud classification models.
See Case Study →EdTech
AI tutors, personalised learning path generation, essay grading assistants, and course content summarisation tools.
See Case Study →Ecommerce
AI-powered product descriptions, semantic product search, personalised recommendations, and review sentiment analysis.
See Case Study →SaaS & B2B
AI writing assistants inside SaaS tools, smart data insights, automated reporting, and internal knowledge base chatbots.
See Case Study →Marketing & Content
AI content generation platforms, SEO brief writers, ad copy generators, and brand voice fine-tuned writing assistants.
See Case Study →Our AI Integration Process
From idea to AI feature in production — responsibly
Discovery
Understand the use case, data available, user expectations, and cost/latency constraints.
Model & Architecture
Select LLM, design prompt architecture, plan RAG pipeline or fine-tuning approach.
Build & Integrate
Build the AI layer, integrate with your existing backend, deploy to staging with real data.
Eval & Guardrails
Evaluate output quality, add hallucination guards, set fallback flows, and tune prompts.
Launch & Monitor
Go live, monitor latency + cost + quality, iterate on prompts and model version.
How to Work With Vxplore
Choose the model that fits your AI ambition
AI Feature Add-On
Add one or two AI features to your existing product. Fixed scope, fast delivery. Ideal for testing AI value before committing.
One-time project cost
- ✓1–2 AI features scoped
- ✓Integrates with existing stack
- ✓4–6 week delivery
- ✓Prompt engineering included
AI Product Build
Build a full AI-native product or a comprehensive AI layer across your platform. Includes architecture, RAG, and deployment.
Full AI product scope
- ✓Full AI architecture design
- ✓RAG pipeline + vector DB
- ✓Guardrails & eval framework
- ✓Cost & latency optimisation
AI Retainer
Ongoing AI development — iterating on prompts, adding features, monitoring quality, and keeping up with fast-moving LLM releases.
per month
- ✓Prompt & model iteration
- ✓Quality monitoring & evals
- ✓New AI feature sprints
- ✓LLM upgrade management
Why Choose Vxplore for AI Integration
What sets our AI engineering apart
Guardrails Built In
We don't ship AI features that hallucinate freely. Every AI feature has output validation, confidence scoring, and fallback flows before it reaches users.
Cost-Optimised by Design
LLM API costs can spiral. We implement caching, prompt compression, model routing (cheap model for simple queries, expensive for complex), and token budgets.
Privacy-First Architecture
PII stripped before LLM calls, on-premise options available with Llama, and GDPR-conscious data handling. We understand compliance requirements for AI.
Evaluation Framework
We build evals from day one — automated test suites that score AI output quality, catch regressions when prompts change, and measure improvement over time.
We Ship AI Products Ourselves
Our own products — ClinikPe and PagarAI — use AI features we built and run in production. We know what works, what fails, and what costs too much.
Model-Agnostic Advice
We're not tied to any LLM provider. We recommend the right model for your needs — which means sometimes telling you not to use GPT-4 when a cheaper model does the job.
Common Questions
Everything you need to know about AI integration — FAQ
Answers to the questions we get asked most often before a project starts.
We work with OpenAI GPT-4o, Anthropic Claude 3.5, Google Gemini, and open-source models (Llama 3, Mistral). For orchestration: Lang Chain and Llama Index. For vector search: Pinecone and pgvector.
Yes — this is our most common engagement. We integrate LLM APIs, build the prompt engineering layer, add vector search for RAG, and wire it into your existing backend without a full rebuild.
RAG (Retrieval-Augmented Generation) lets an LLM answer questions using your own data — documents, databases, knowledge bases. If you want an AI assistant that knows your product specifically, you need RAG.
We use RAG to ground responses in factual data, output validation layers, confidence scoring, fallback flows for low-confidence answers, and human review triggers for high-stakes decisions.
GPT-4o for most general tasks. Claude for long documents and nuanced writing. Gemini for multimodal (vision + text). Open-source (Llama, Mistral) when privacy, on-premise, or cost is the priority. We recommend after scoping.
Yes. We build AI chatbots trained on your product docs, knowledge base, and support history using RAG. Accurate answers, human escalation built in, and integration with your existing helpdesk or CRM.
We never send sensitive user data to public LLMs without DPAs. We use PII stripping before API calls, support on-premise deployment with open-source models, and advise on GDPR/HIPAA requirements for your use case.
A basic AI feature (chatbot, summarization, classification) starts from $3,000–$8,000. A full RAG system with chat UI ranges $8,000–$20,000. A custom AI-native SaaS product ranges $20,000–$60,000+. Plus ongoing LLM API costs based on usage.
Ready to Build?
Let's Add AI to Your Product.
Tell us what you want AI to do. We'll come back with an architecture recommendation and quote within 48 hours.
Proof of concept
Production feature
Full AI product
Enterprise / platform
Discovery first
Vikash will personally review your project and reply within 4 hours on a business day.
See our work →