LLMs in production, not just demos
OpenAI, Anthropic Claude, and open-source LLMs wired into your app with RAG, structured outputs, evals, and the discipline that keeps it cheap and reliable at scale.
A demo with GPT-4 takes an afternoon. An LLM feature that doesn't hallucinate on edge cases, doesn't leak prompts, costs less than your hosting bill, and doesn't break when the model is deprecated - that's a real engineering project. That's the project I take.
60%
Average token cost reduction via model switching + caching
Promptfoo
Automated evals on every PR
<1s
TTFT (time to first token) targeted on streamed responses
0
Prompts leaked in production endpoints
Trusted by founders & teams in
Smit Parekh
Full-Stack Developer
Gujarat, India · available worldwide
I'm the only person who touches your code. You talk directly to the senior developer writing every line - no account managers, no juniors, no handoffs. React, Next.js, Node.js, TypeScript and PostgreSQL, end to end.
- AWS Certified Solutions Architect
- 4+ years shipping production web apps
- 20+ live systems across FinTech, SaaS & AI
Everything included in every engagement
No upsells. No surprise change orders. One scope, one price.
Model selection that fits the job
GPT-4o, Claude Sonnet, Haiku, Llama 3.1, Mistral - picked on cost, latency, and the actual task. Often Haiku or Llama 70B in production with GPT-4 reserved for retries.
RAG done right
Chunking strategy, embedding model selection, reranking, hybrid (BM25 + vector) search. Pinecone, pgvector, or Weaviate - picked by data size and ops capacity.
Structured outputs & function calling
Tool use, JSON schema enforcement, OpenAI structured outputs, Claude tool_use. No more 'parse the markdown the LLM hopefully returned'.
Prompt injection & safety
Input sanitisation, output filtering, rate limiting per user, abuse detection. Your LLM endpoint isn't a back door to your prod database.
Evals & regression testing
Golden dataset, automated eval suite, A/B between models on every PR. You upgrade the model only when the evals say it's safe.
Streaming + token cost optimisation
Server-Sent Events for token-by-token streaming, prompt caching, context-window discipline. Bills that don't surprise you.
Results that build trust
The numbers behind the work - measured on real production data, not demos.
60%
Average token cost reduction via model switching + caching
Promptfoo
Automated evals on every PR
<1s
TTFT (time to first token) targeted on streamed responses
0
Prompts leaked in production endpoints
The tools I actually use in production
Modern, battle-tested, and chosen for fit - not hype.
Models
- GPT-4o
- Claude 3.5
- Llama 3.1
- Mistral
RAG
- pgvector
- Pinecone
- Weaviate
- Cohere Rerank
Orchestration
- LangChain
- LlamaIndex
- Vercel AI SDK
- Inngest
Quality
- Promptfoo
- LangSmith
- Braintrust
- Helicone
How we'll work together
Predictable, written-down, no surprises.
- 01
Feature scoping
Where does the LLM actually help vs hurt? Some 'AI features' should not exist. We answer that first.
- 02
Prototype + eval set
Working prototype + a golden dataset to measure quality. You can compare models objectively from day one.
- 03
Productionise
Streaming, retries, fallback model, cost budget, observability - the boring stuff that makes the demo a product.
- 04
Ship + monitor
Cost dashboards, eval dashboards, prompt versioning. New model? Re-run evals, deploy if green.
AWS Certified
Solutions Architect
I'm Smit Parekh - a full-stack developer who writes every single line of your code
With 4+ years of experience shipping production systems for FinTech, SaaS, and AI startups, I work as a senior individual contributor - no juniors on your project, no account managers between you and the work. Every commit, every architecture decision, every deployment lands on my machine first.
I specialise in the TypeScript ecosystem - React & Next.js on the frontend, Node.js / NestJS on the backend, PostgreSQL for data, and AWS for infrastructure. I've built headless e-commerce stores, multi-tenant SaaS platforms, real-time dashboards, AI-powered tools, and performance-first marketing sites. The common thread: clean code, zero tech-debt handover docs, and measurable business results.
10+ production apps shipped
FinTech, SaaS, AI startups & e-commerce
95+ Lighthouse scores, guaranteed
Performance baked in from day one
AWS Certified Solutions Architect
Infrastructure decisions you can trust
Direct, async-first communication
You talk to who writes every line of code
My daily stack
Pricing that matches the work
Starting prices. Final quote in writing after a 30-minute scoping call.
Prototype
Validating one AI feature
$2,500starting
- Single feature, single model
- Golden dataset + basic eval
- Delivered in 1-2 weeks
Production AI
Shipping AI to real users
$8,500starting
- RAG + structured outputs
- Streaming, retries, fallback model
- Cost + eval dashboards
Retainer
Ongoing LLM evolution
$3,000/mostarting
- Model migrations + evals
- Prompt iteration
- Cost watch + optimisation
Me vs. an agency vs. hiring in-house
Three ways to get this built. Here's the honest comparison.
Best value Solo Dev (me) $80-$120 /hr or fixed | Agency $150-$300 /hr blended | In-house hire $80-$120K /yr + benefits | |
|---|---|---|---|
| Start date | 1-2 weeks from quote | 4-8 weeks onboarding | 8-16 weeks to hire |
| Who writes the code | Senior dev - every single line | Junior assigned to your account | Whoever you manage to hire |
| Communication | Direct - you talk to who codes | Via account manager first | Direct, but management overhead |
| Flexibility | Scale up or down any time | Locked to contract length | Fixed headcount, hard to change |
| Code ownership | 100% yours, full handover docs | Depends on contract terms | Yours, but bus factor risk |
| Risk | Weekly demos, fixed scope | Scope creep & handoff gaps | Wrong hire = months lost |
Questions I get asked first
OpenAI or Anthropic?+
Depends on the task. Claude is currently stronger at long-context reasoning and tool use; GPT-4o at multimodal and tight latency. I'll benchmark both on your golden dataset.
Can you build a ChatGPT for our docs?+
Yes - that's a classic RAG project. Embedding pipeline + vector store + grounded retrieval + citations in the UI so users know where answers come from.
How do you control costs?+
Smaller model by default, GPT-4 / Claude Opus only on retry. Prompt caching, response caching where safe, streaming so you bail early. Monthly budget alerts.
What about open-source / self-hosted LLMs?+
Yes - Llama 3.1, Mistral, Qwen via Together AI, Groq, or self-hosted on AWS. Right when privacy, cost, or compliance demands it. Often slower to integrate than OpenAI/Anthropic, so we measure tradeoffs honestly.
Let's scope your project
Tell me what you're building. I'll reply with a written estimate within 24 hours - no sales call required.
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