OpenAI & LLM API Integration
Production-grade integration with OpenAI, Claude, Groq, and other LLM providers - function calling, structured output, streaming, and cost-aware model routing.
OpenAIClaudeGroqOllama
Build AI Products That DeliverReal Business Value
Whether you're building an AI-native startup, adding intelligent capabilities to an existing product, or launching a new AI-powered platform, we help you design, engineer, and scale production-ready AI products. From product strategy and architecture to LLM integration, RAG, AI agents, and deployment, we build AI solutions that create measurable business value.
AI MVP DevelopmentLLM ApplicationsRAG & Knowledge AIProduction-Ready AI
14+
Years industry experience
15+
Countries served
170+
Projects delivered globally
5+
AI apps in production
What We Build With Generative AI
Nine core offerings, all engineered for production - not proof-of-concept demos.
Automation & Workflow Setup
AI-powered automation that removes manual work - document processing, approvals, notifications, and multi-step workflows triggered by real events.
n8nMake.comLangChainPython Pipelines
Customer Support Chatbots
RAG-based support chatbots trained on your policies and product docs - resolving routine queries and escalating to humans with full context.
RAGLive EscalationMulti-channel
Voice AI
Real-time voice agents that listen, understand, and respond naturally - inbound and outbound calls, multilingual, sub-second latency.
WhisperElevenLabsRetell AIGroq
Student Assistance Chatbots
Curriculum-trained AI study assistants that answer student questions 24/7, grounded in your actual course content - not generic AI knowledge.
Curriculum RAGGPT-4opgvector
Knowledge Base Chatbots
Chatbots trained on your internal docs, wikis, and knowledge bases - turning institutional knowledge into instant, cited answers.
Vector SearchNotion / ConfluenceSemantic Caching
AI Coding Assistants
Custom AI coding tools and agentic workflows that speed up engineering - code review, generation, and refactoring built into your dev process.
Claude CodeCustom AgentsGitHub Copilot
Design-to-Code Tools
AI pipelines that turn Figma designs and mockups directly into production-ready components - cutting handoff time from days to hours.
Figma APIComponent GenerationDesign Tokens
Feature-Level AI
Targeted AI features added into your existing product - smart search, content generation, summarization, and recommendations, shipped without a rebuild.
API IntegrationIncremental Rollout
Large Language Model Providers
We are model-agnostic. We select the right LLM for your specific task, not the one with the best marketing.
OpenAI - Chat Completions
Industry StandardBest general capability, function calling, structured output
GPT-4o / GPT-4o-miniFunction CallingJSON ModeVision InputStreaming
We build with it:
- Curriculum AI chatbots (RAG)
- Document analysis & summarization
- Structured data extraction
- Multi-turn conversation
OpenAI - Responses API
Stateful AgentsStateful AI agents with built-in tools: file search, web search, computer use
Built-in File SearchWeb SearchConversation StateBackground Runs
We build with it:
- Persistent AI study assistants
- Customer support agents
- Document Q&A agents
- AI onboarding guides
Claude (Anthropic)
Long Context200K context, nuanced writing, safety-critical applications
200K Token ContextClaude 3.5 SonnetVisionConstitutional AI
We build with it:
- Full-document legal/clinical analysis
- Essay feedback & scoring
- Compliance document review
- Safety-critical EdTech
Groq
Fastest Inference10-20x faster than GPU inference - essential for real-time voice AI
Llama 3.1 70BMixtral 8x7B300+ tokens/secOpen Source Models
We build with it:
- Voice AI backends
- Real-time AI tutoring
- High-throughput classification
- Cost-optimized inference
DeepInfra
Wide Model CatalogServerless inference for specialized and fine-tuned open-source models
Serverless PricingFine-tuned ModelsEmbedding ModelsImage Generation
We build with it:
- Domain-specific model access
- Embedding inference at scale
- Multi-model experimentation
- Cost-optimized batching
Together AI
Fine-tuning PlatformFine-tuning open-source models in production without managing GPU clusters
Llama Fine-tuningCustom Dataset TrainingProduction DeploymentServerless API
We build with it:
- Domain-specific fine-tuned models
- Consistent JSON output formatting
- Brand tone fine-tuning
- Task-specific model training
Hugging Face
Open Source HubAccess to 500,000+ models - the source for SOTA embedding, reranking & domain models
BGE / E5 EmbeddingsCross-encoder RerankersDomain NLP ModelsInference API
We build with it:
- SOTA embedding model access
- Reranking model integration
- Medical / legal NLP
- Data-private local inference
Ollama
On-Premise / PrivateFull data sovereignty - runs LLMs locally, data never leaves your infrastructure
Llama 3.1 (8B/70B)Mistral / Phi-3OpenAI-Compatible APIAir-Gapped Deployment
We build with it:
- Regulated industry AI (healthcare, fintech)
- Private RAG systems
- On-premise enterprise chatbots
- Dev/test without API costs
Giving LLMs Access to Your Knowledge
RAG is the difference between an AI that says something plausible and one that says something accurate and specific to your content.
Production RAG Pipeline Architecture
01IngestPDF, Word, HTML, DB
02ParseUnstructured.io ETL
03ChunkSemantic / structural
04EmbedBGE / OpenAI / E5
05RetrieveVector + BM25 hybrid
06RerankCross-encoder filter
07GenerateLLM + cited context
Embedding Models
Convert text to dense vectors for semantic search. Model choice is foundational - it determines retrieval quality.
text-embedding-3-large (OpenAI)
BGE-M3 (open-source)
E5-large-v2 (multilingual)
Jina v3 (8K token context)
PubMedBERT (medical domain)
Reranking Models
Cross-encoders that re-score retrieved chunks for true relevance. Consistently improves RAG accuracy by 15-30%.
Cohere Rerank (API-based)
BGE Reranker v2 (self-hosted)
Jina Reranker (long context)
Custom fine-tuned cross-encoders
Unstructured.io
ETL platform for complex real-world documents. Converts scanned PDFs, PowerPoints, tables, and HTML into clean LLM-ready text.
PDF (scanned + digital)
Word / PowerPoint / Excel
HTML page extraction
Image OCR + table extraction
How We Choose the Right Stack
Five factors drive every technology decision: task requirements, latency, data privacy, cost, and existing infrastructure.
Best general capability, function callingOpenAI GPT-4o
Lowest cost for simple tasksGPT-4o-mini or Llama 3.1 8B via Groq
Long-document analysis (200K context)Anthropic Claude
Fastest inference for voice AILlama 3.1 70B via Groq
Data privacy, on-premiseLlama / Mistral via Ollama
Domain fine-tuning neededTogether AI fine-tuning
Stateful AI agent with built-in toolsOpenAI Responses API
Highest quality TTS voiceElevenLabs
Full conversational voice agentRetell AI
Speech-to-text, multilingualOpenAI Whisper
Generative AI We've Shipped
Not prototypes. Production systems that real users depend on every day.
Pranthora
Multilingual Voice AI Agent
Full generative AI voice stack: real-time conversations in 10+ languages with sub-second response latency.
Whisper ASRGroq + LlamaElevenLabs TTSRetell AI
MedEntry MAI
AI Study Assistant
RAG-based AI study assistant trained on the full UCAT curriculum. 24/7 subject-specific guidance.
OpenAI EmbeddingspgvectorGPT-4oUnstructured.io
Satark AI
AI Compliance Agent
LLM-powered compliance monitoring with long-context regulatory document analysis and gap report generation.
Claude 3.5Custom RAG PipelineStructured OutputRegulatory DB
Frequently Asked Questions
Common questions before starting an engagement.
Generative AI refers to AI systems that generate new content - text, speech, images, code, or structured data - rather than just classifying existing content. In software, it most commonly refers to LLMs like GPT-4 and Claude that generate text, voice AI systems, and RAG-based knowledge systems.
We evaluate five factors: task requirements (reasoning complexity, context length, structured output needs), latency (real-time voice vs async), data privacy (cloud API vs on-premise Ollama), cost (token pricing vs query volume), and existing infrastructure. We often use multiple models in a single application, routing different tasks to the most appropriate model.
Claude excels at long-document analysis (200K token context), nuanced writing quality, safety-critical applications, and vision tasks with complex document images. GPT-4o is generally better for function calling, structured output, and the broadest integration ecosystem. Many production systems use both.
Yes. For applications where data cannot be sent to third-party APIs - regulated healthcare, financial data, student PII - Infynno deploys generative AI on-premise using Ollama with open-source models (Llama, Mistral, Phi-3). Full data sovereignty with the same generative AI capabilities.
Yes. We build AI-powered automation using n8n, Make.com, and custom Python pipelines - document processing, approval routing, notifications, and multi-step workflows triggered by real events. LLMs handle the parts of the workflow that require judgment: classification, extraction, and generation.
Yes. We build custom AI coding assistants and agentic workflows (built on tools like Claude Code) that speed up code review, generation, and refactoring inside your existing dev process. We also build design-to-code pipelines that convert Figma designs directly into production-ready components, cutting handoff time from days to hours.
Yes. Most of our generative AI work is feature-level - smart search, content generation, summarization, or recommendations added into an existing product via API integration. No rebuild, no rip-and-replace. We scope, build, and ship the feature on your existing architecture.
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Ready to Get Started With Generative AI Solutions?
Start with a free discovery call. NDA before we go further.
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