Add Real AI to Your Product. Not Just a Chatbot Widget.
Document analysis, retrieval-augmented generation, function calling, and recommendation engines — built on GPT-4o, LangChain, and pgvector. We build AI features that users actually rely on.
OpenAI is Vxplore's primary mobile framework — and it earned that position.
We didn't adopt OpenAI because it was new. We adopted it because the data was clear: our Flutter apps were shipping faster, scoring higher on app stores, and costing clients significantly less than the native alternatives we had been using before.
Flutter uses its own rendering engine (Skia, now Impeller) instead of bridging to native UI components. That single architectural choice is what gives it pixel-perfect consistency across iOS and Android, 60fps performance, and hot reload that updates the UI in under a second. For startups who need speed and quality simultaneously — there's no better cross-platform choice in 2026.
GPT-4o — Best General-Purpose LLM
GPT-4o is OpenAI's most capable multimodal model — text, image, and audio in a single model. Its function calling, JSON mode, and structured output capabilities make it reliable for production integrations, not just conversational demos. For most AI product features, GPT-4o is the right default.
RAG Solves the Hallucination Problem
Raw LLMs hallucinate because they answer from training data, not your actual content. Retrieval-Augmented Generation (RAG) grounds every response in documents you control — your knowledge base, product catalogue, contracts, or support docs. The LLM becomes a reasoning engine over your data, not a guesser.
Function Calling Enables Real Actions
GPT-4o's function calling lets the model trigger structured API calls — search your database, book appointments, update records, send notifications — based on natural language input. This is how AI agents do real work inside your product, not just answer questions.
LangChain + pgvector — Production Stack
LangChain handles LLM chain orchestration, memory management, and tool integration. pgvector adds vector similarity search directly to PostgreSQL — no separate vector database. Together they're the most practical production stack for AI features that need to stay grounded and maintainable.
What We Build with OpenAI
OpenAI development capabilities — all under one team.
AI Chatbots & Support Assistants
GPT-4o-powered chatbots grounded in your product documentation, knowledge base, or support history via RAG. Answers questions accurately, escalates when confidence is low, and integrates into your existing product UI.
Document Analysis & Intelligence
Upload contracts, invoices, medical records, or reports — GPT-4o extracts structured data, answers specific questions, flags anomalies, and summarises key information. Built on FastAPI with async processing.
Semantic Search & Recommendations
Replace keyword search with embedding-based semantic search — users find what they mean, not just what they typed. Product recommendations, content discovery, and "similar items" powered by OpenAI embeddings + pgvector.
AI Agents & Function Calling
Multi-step AI agents that can query your database, call your APIs, and take actions based on natural language instructions. CRM automation, booking agents, data analysis agents — GPT-4o function calling as the reasoning layer.
Content Generation Pipelines
Automated content generation — product descriptions, SEO meta tags, personalised emails, report narratives — with brand voice controls, output validation, and human review workflows. Batch processing via queues.
LLM Integration into Existing Products
Add AI features to an existing SaaS or mobile app — define the right use cases, build the API endpoints, and integrate into your current stack without a full rebuild. Scope includes LLM cost monitoring.
OpenAI Tech Stack at Vxplore
The full ecosystem behind every Vxplore OpenAI app — not just Flutter itself.
🐦 LLMs
- • OpenAI GPT-4o
- • Claude 3.5 (Anthropic)
- • Gemini 1.5 Pro
⬡ Orchestration
- • LangChain
- • LlamaIndex
☁ Vector Search
- • pgvector (PostgreSQL),
- • optional: Pinecone / Qdrant
🔬 API Backend
- • Python
- • FastAPI
- • Pydantic v2
🐦 Embeddings
- • OpenAI text-embedding-3-large
- • Cohere
💰 Async
- • Celery / ARQ for background LLM jobs
🚀 Caching
- • Redis (semantic caching for repeat queries)
⬡ Monitoring
- • LangSmith (LLM tracing)
- • Sentry
- • custom token dashboards
🤝 Deployment
- • AWS ECS
- • Docker
GPT-4o vs Claude 3.5 vs Gemini 1.5 — Which LLM for Your Product
We work with all three — here's how we choose
| Feature | ⚡ GPT-4o | Claude 3.5 Sonnet | Gemini 1.5 Pro |
|---|---|---|---|
| General reasoning | ✓ Best overall | Excellent | Very good |
| Instruction following | ✓ Best — JSON mode, function calling | Very good | Good |
| Document understanding | Very good | ✓ Best (200K context) | ✓ 1M context window |
| Code generation | ✓ Best | Very good | Good |
| Cost per 1M tokens | $5–$15 (input/output) | $3–$15 | $3.50–$10.50 |
| India availability | ✓ Full API access | ✓ Full API access | ✓ Full API access |
| Best for | Agents, function calling, general | Long docs, summarisation | Long context, multimodal |
AI Integrations We've Built
LLM features across these verticals — grounded in real data, not demo hallucinations
Healthcare & MedTech
Clinical document analysis, patient query assistants grounded in clinical guidelines, and medical record summarisation. Accuracy and source grounding are non-negotiable — RAG + validation is mandatory.
Legal & Compliance
Contract analysis, clause extraction, compliance checklist generation, and legal document Q&A. GPT-4o with long-context processing and structured output extraction. Built for PagarAI and similar compliance tools.
SaaS & B2B Tools
AI features inside existing SaaS — smart search, automated data entry, natural language reporting, and intelligent onboarding assistants. Added to existing products via API without full rebuilds.
eCommerce & Retail
Product description generation, semantic search, personalised recommendations, and AI-powered customer support bots trained on your product catalogue and FAQs.
EdTech
Adaptive content generation, student Q&A assistants grounded in course material, automated essay feedback, and personalised study plans generated from performance data.
Analytics & Reporting
Natural language interfaces for business data — "show me last month's top 10 products by margin" — translating plain English queries into structured database queries via function calling.
How We Build AI Integrations
From use case selection to production LLM feature — in 5 stages
Use Case Scoping
Identify which features genuinely benefit from LLM vs which are better served by traditional logic. Not everything should use GPT-4o. We evaluate accuracy requirements, data availability, cost implications, and failure modes before writing any code.
Data Pipeline & Embedding Setup
Prepare the data the LLM will reason over — chunking strategy, embedding model selection, pgvector indexing, and retrieval configuration. For RAG systems, this stage determines answer quality more than the LLM choice.
LLM Chain / Agent Development
Build the LangChain chain or agent — prompt engineering, output validation (Pydantic), function definitions, memory management, and fallback logic. All chains are tested against edge cases and adversarial inputs.
API Integration & Testing
Expose the AI feature as a FastAPI endpoint consumed by your frontend or mobile app. Load testing, latency benchmarks, and cost-per-query analysis. Semantic caching via Redis for repeat queries.
Monitoring & Iteration
LangSmith for LLM call tracing, custom dashboards for token usage and cost, and accuracy monitoring. LLM features need ongoing prompt iteration — we set up the tooling so you can measure and improve.
AI Pricing & Engagement Models
Fixed-scope AI features — ongoing LLM API costs are separate (OpenAI/Anthropic billing)
AI Feature Integration
A single focused AI feature integrated into your existing product — chatbot, document analysis, semantic search, or content generation pipeline.
- ✓ Use case scoping + LLM selection
- ✓ FastAPI endpoint for AI feature
- ✓ RAG pipeline (if applicable)
- ✓ Pydantic output validation
- ✓ LangSmith tracing + monitoring
- ✓ Prompt engineering + testing
- ✓ Token cost dashboard
AI-Powered Product
Multiple AI features or a complete AI-powered product layer — RAG pipeline, semantic search, AI agents, and content generation — fully integrated into your SaaS or mobile app.
- ✓ Everything in AI Feature plan
- ✓ Full RAG pipeline with pgvector
- ✓ Semantic search + recommendations
- ✓ Function calling / AI agent setup
- ✓ Multi-LLM routing (GPT-4o + fallback)
- ✓ Redis semantic caching
- ✓ Human review workflow (if required)
Enterprise AI / Custom
Complex AI systems — multi-agent workflows, fine-tuned models, high-volume LLM pipelines, or AI features requiring compliance review and regulated industry deployment.
- ✓ Everything in AI-Powered Product plan
- ✓ Multi-agent orchestration
- ✓ Custom model fine-tuning (if applicable)
- ✓ Compliance review for regulated industries
- ✓ High-volume async LLM processing pipeline
- ✓ Dedicated AI engineer
- ✓ Ongoing LLM cost optimisation retainer
Why Choose Vxplore for OpanAI
We've built LLM integrations for healthcare (ClinikPe clinical assistant), compliance (PagarAI document analysis), and SaaS products. We know where AI adds genuine product value — and where it adds latency and cost without improving the user experience.
We start with the use case, not the technology
Not every feature benefits from GPT-4o. We scope which problems need LLM reasoning (unstructured data, language generation, semantic understanding) and which are better solved with a simple classifier, a rule engine, or a database query. Honest scoping prevents expensive AI features no one uses.
RAG accuracy is an engineering problem, not a prompt problem
Poor RAG systems give confident wrong answers. The difference between a good and bad RAG pipeline is in the data preparation — chunking strategy, embedding model, retrieval ranking, and re-ranking. We've invested in understanding what makes retrieval accurate, because that's what determines whether users trust the AI feature.
LLM cost is a product metric we monitor
GPT-4o API costs can scale unexpectedly. We instrument every LLM feature with token usage dashboards, implement semantic caching for repeat queries, and build cost alerts into the monitoring setup. LLM cost per query is visible before it becomes a problem.
Flutter FAQs
RAG (Retrieval-Augmented Generation) is a technique that grounds LLM responses in your specific documents or data. Instead of the model answering from its training data (which can hallucinate), it first retrieves relevant chunks from your knowledge base, then generates a response based on what it found. For product features that require accurate, source-specific answers — support bots, document analysis, internal search — RAG is essential.
GPT-4o is the right choice for complex reasoning, function calling, JSON-structured output, and tasks requiring high accuracy. For simpler classification, summarization, or entity extraction tasks, GPT-3.5-turbo or Claude Haiku can reduce costs by 80–90% with acceptable accuracy. We benchmark multiple models on your specific task during scoping and recommend the most cost-effective option.
Yes — we work with GPT-4o (OpenAI), Claude 3.5 Sonnet (Anthropic), Gemini 1.5 Pro (Google), and open-source models (Llama 3, Mistral) via API or self-hosted. Many production systems use multiple models — GPT-4o for complex reasoning, a cheaper model for classification — with Lang Chain routing between them based on task type and cost thresholds.
Yes — this is the most common request we handle. We build a Fast API service that exposes AI features as API endpoints, which your existing frontend or mobile app calls. The AI layer is additive, not a replacement for your current backend. Integration typically takes 4–8 weeks per feature.
Multiple layers: (1) RAG grounds responses in authoritative source documents, (2) Pedantic output schemas validate that responses match the expected structure, (3) confidence scoring flags low-certainty responses for human review, (4) prompt engineering includes instructions for acknowledging uncertainty, and (5) Lang Smith tracing lets us identify failure patterns and improve prompts. We never ship an AI feature without a fallback path.
It depends on usage and model choice. GPT-4o costs $5/1M input tokens and $15/1M output tokens. A support chatbot handling 10,000 queries/month with average 2,000 tokens each costs roughly $100–$300/month in API fees. High-volume document processing can cost more. We provide a cost model during scoping and implement semantic caching to reduce repeat query costs by 30–60%.
GPT-4o fine-tuning is available via OpenAI’s API, but for most use cases, RAG with good prompt engineering outperforms fine-tuning for factual accuracy — at a fraction of the cost. Fine-tuning is most valuable for enforcing a specific output style, format, or terminology that the base model doesn’t follow consistently. We evaluate whether fine-tuning is worth it for your specific task before recommending it.
Single AI feature integrations (chatbot, document analysis, semantic search) start at $6,000. Full AI product layers with multiple features start at $12,000. Enterprise AI systems with multi-agent workflows or compliance requirements are scoped after discovery. Ongoing LLM cost optimization retainers are available from $2,000/month.
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