LuppiAI
Prepaid expert hours for building, fine-tuning, and deploying AI systems: LLM applications, retrieval-augmented generation, agentic architectures, MLOps pipelines, and responsible AI guardrails.
About LuppiAI
Generative AI has moved from experimentation to production faster than most organisations anticipated. The gap between a working prototype and a reliable, cost-efficient production system is substantial — it spans prompt engineering, retrieval architecture, evaluation frameworks, safety guardrails, cost controls, and operational monitoring. LuppiAI bridges that gap with senior AI engineers who have shipped production systems, not just notebooks.
Our practitioners build across the major frontier model providers — OpenAI, Anthropic, and Google Gemini — as well as open-weight models on local and cloud infrastructure. We design RAG pipelines using vector databases including Pinecone, Weaviate, Chroma, and pgvector, and we build agentic systems using frameworks such as LangChain, LlamaIndex, CrewAI, and custom orchestration. Fine-tuning engagements cover supervised instruction fine-tuning, RLHF, and parameter-efficient methods (LoRA, QLoRA) on both proprietary and open-weight models.
Beyond building, we help organisations evaluate and govern their AI systems rigorously. Every production AI application needs an evaluation framework, a safety layer, and an operational observability setup before it is trustworthy at scale. We implement these as first-class engineering concerns — not afterthoughts — so that your AI investment delivers consistent, measurable value rather than fragile demos.
Common use cases
What's covered
LLM application development
OpenAI GPT-4o, Anthropic Claude, Google Gemini integration
Retrieval-augmented generation (RAG)
chunking strategy, embedding models, hybrid search
Vector database configuration
Pinecone, Weaviate, Chroma, pgvector, Qdrant
Agentic system design
tool calling, multi-agent orchestration, LangChain/LlamaIndex/CrewAI
Fine-tuning
supervised fine-tuning, LoRA/QLoRA, instruction tuning, RLHF
MLOps
model registry, CI/CD for ML, experiment tracking (MLflow, W&B), serving infrastructure
Computer vision
object detection, classification, OCR, vision-language model integration
AI evaluation frameworks
automated evals, LLM-as-judge, benchmark suites, regression testing
Guardrails and safety
content moderation, output validation, hallucination mitigation
Cost optimisation
model routing, caching strategies, token budget management
Deliverables
- 1
Production-ready application code with tests, deployment configuration, and dependency documentation
- 2
Architecture decision records (ADRs) for all major design choices — model selection, retrieval design, guardrail strategy
- 3
Evaluation framework with baseline benchmarks and regression test suite
- 4
Cost model and token budget analysis with optimisation recommendations
- 5
Operational runbook — monitoring alerts, error handling procedures, model update process
- 6
Session logs and hour consumption report confirmed by client before deduction
How it works
How it works
Three steps from purchase to delivery — no surprises.
Buy hours
Choose a service line and a tier that fits your scope. Pay once — no retainer, no commitment beyond the block.
We deliver
Our senior practitioners get to work. Every session is logged in detail and submitted for your review before hours are touched.
Recharge
Running low? Top up instantly from the portal. Each recharge is a fresh block with its own 12-month validity.
Terms & Conditions
These terms govern all prepaid hour engagements with Luppiter Tech Solutions & Services Pvt Ltd.
Frequently asked questions
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