Design applications around large language models with clear architecture: prompt versions, tool contracts, retrieval, memory boundaries, observability, and evaluation.
An LLM feature becomes a product when prompts, tools, memory, retrieval, logging, and evaluation are designed together. Otherwise you get demos that break under real users.
I help teams build LLM applications with those pieces explicit: versioned prompts, safe tool contracts, memory boundaries, optional RAG, and observability for cost and quality.
Based in Ahmedabad, I deliver across Laravel, Node, React, and FastAPI so the LLM layer fits the product instead of becoming a side experiment.
If your focus is content-style GenAI features, see Generative AI Development. If knowledge grounding is central, see RAG Development.
What I use to ship this kind of AI work.
What you get when we build this AI workflow for your product.
Version prompts, separate system and user roles, and keep templates reviewable by product owners.
Define tool contracts so the model can look up or act without open-ended chaos.
Decide what is session memory, what is durable user memory, and what must never be stored.
Add RAG when answers must follow your corpus, with citations and weak-hit behavior.
Trace requests, measure latency and spend, and keep privacy-safe telemetry.
Maintain golden examples, regression checks, and human review signals as the app evolves.
Project, MVP sprint, or dedicated hire depending on roadmap urgency.
Guides connected to production AI delivery.
A practical, 1,700+ word engineering rollout strategy for introducing AI features to an existing, revenue-generating Laravel SaaS application — covering risk classification, feature flagging with Laravel Pennant, shadow mode testing, human review gates, and production monitoring.
A comprehensive, 1,700+ word architectural decision guide comparing FastAPI and Laravel for AI backends — evaluating hosted API orchestration, local ML ecosystem access, database integration, async performance, and hybrid architectures.
What AI Developer Ahmedabad means in practice: local collaboration for Gujarat/India teams plus remote delivery habits for global SaaS products.
Common questions about scope, architecture, and how to start.