AI DEVELOPMENT
PROPELOO builds production AI systems — generative AI products, LLM-powered agents, RAG pipelines, AI-native SaaS platforms, and multimodal applications — engineered for reliability, latency, and cost efficiency at scale.
AI development requires more than calling an API. Production AI systems need prompt engineering discipline, RAG architecture, vector database design, inference cost optimization, evaluation pipelines, and observability infrastructure. PROPELOO brings all of this as a unified engineering practice.
GPT-4o, Claude 3.5, Gemini Pro, Mistral, LLaMA — model selection, fine-tuning, and inference optimization for real production workloads.
Multi-step reasoning agents with tool use, memory, and structured output — production-grade orchestration using LangChain, LlamaIndex, and custom agent frameworks.
Retrieval-augmented generation pipelines with semantic search, hybrid retrieval, reranking, and hallucination reduction for enterprise knowledge systems.
End-to-end AI SaaS platforms from model inference through multi-tenant API, billing, and user-facing product — not just proof-of-concept demos.
LLM-powered products, content generation, code assistance, and multimodal AI applications.
Autonomous agents, multi-agent systems, and AI-driven workflow orchestration.
Enterprise RAG pipelines, semantic search, and AI knowledge bases.
Full-stack AI SaaS products with inference APIs, multi-tenancy, and usage billing.
Custom model training, fine-tuning, and MLOps pipeline engineering.
AI-powered trading signals, fraud detection, risk engines, and on-chain analytics.
Most AI prototypes fail to scale because inference cost, latency, and context window management were not considered at architecture time — PROPELOO designs for these constraints from the start.
LLM hallucination and factual accuracy are engineering problems, not just model selection problems — RAG architecture, prompt engineering, and evaluation pipelines are required for production reliability.
AI agent reliability requires structured output enforcement, retry logic, tool use error handling, and human-in-the-loop escalation design — not just a simple function call chain.
AI SaaS products need multi-tenant inference routing, usage-based billing, rate limiting, and observability — not just a wrapped API endpoint.
PROPELOO designs AI systems that can be evaluated, monitored, and improved in production — not just demonstrated in a controlled demo environment.
LangChain, LlamaIndex, LangGraph, Haystack, and custom agent frameworks. For vector databases: Pinecone, Weaviate, Qdrant, and pgvector. For inference: OpenAI, Anthropic, Google Vertex AI, AWS Bedrock, Together AI, and self-hosted open-weight models.
Yes. PROPELOO designs fine-tuning pipelines using LoRA/QLoRA, RLHF, and instruction tuning on open-weight models (LLaMA, Mistral, Falcon). We also support supervised fine-tuning via OpenAI, Anthropic, and Google Vertex AI APIs.
Through a combination of RAG with grounded retrieval, prompt engineering guardrails, structured output enforcement, confidence scoring, and evaluation pipelines that continuously test factual accuracy before and after deployment.
Yes — PROPELOO handles the full product stack: model selection and fine-tuning, backend inference API, RAG pipeline, vector database, auth, multi-tenancy, billing, and the user-facing web or mobile interface.
FinTech (fraud detection, credit risk, trading signals), Web3 (on-chain analytics, MEV, automated DeFi), SaaS (AI-native product features), healthcare (clinical data extraction), and enterprise (document processing, knowledge management, workflow automation).