LLM Product Engineering
End-to-end GenAI products on multi-tenant SaaS — from prompt design and structured outputs to streaming UIs, guardrails, and per-tenant token metering.
GPT-4oClaudeGeminiVercel AI SDK
Available for new AI consulting & build partnerships
I help businesses become AI-native — with products that ship and pay for themselves.
I’m Akshay, an AI consultant & GenAI engineer. I partner with teams to design, build, and ship GenAI that works in production — RAG systems, AI agents, and streaming LLM apps — backed by 9+ years scaling systems at Licious, Allo Health, and Practo.
Core stack
ClaudeGPT-4oLangGraphpgvectorNext.jsAWS
5
GenAI products shipped end-to-end
41%
Lift in sales-accepted-lead precision
38%
Reduction in LLM spend
9+
Years engineering at scale
9+ years building at scale, trusted across unicorns and 0-to-1 teams
LiciousAllo HealthPractoHousejoyNoticeboardIntellora AITiVoLiciousAllo HealthPractoHousejoyNoticeboardIntellora AITiVo
About
A partner who treats GenAI like a product, not a demo.
Most AI demos never survive contact with real users. I help teams pick up where the demo ends — the evals, cost controls, guardrails, and reliability that let a GenAI feature run in production in front of paying customers.
At Intellora AI I shipped five GenAI products across a multi-tenant SaaS platform, driving a 41% lift in lead precision and a 38% cut in LLM cost. Underneath it is 9+ years of distributed-systems engineering — the kind of foundation that turns an AI idea into something that actually holds up, and a business into one that runs on it.
Ships end-to-end
Prompts, RAG, agents, evals, and the streaming UI — I own the whole lifecycle, not a slice of it.
Measures everything
Golden datasets, CI regression gates, cost & latency tracking. If it can't be measured, it doesn't ship.
Built at scale
Unicorn-scale traffic at Licious, 0-to-1 as a founding engineer at Allo Health. The platform instincts are earned.
What I do
Depth across the full GenAI stack
From the retrieval layer to the streaming UI — and the evals and cost controls that keep it reliable in production.
RAG & Vector Search
Retrieval pipelines that stay grounded — hybrid BM25 + dense retrieval, semantic chunking, reranking, and answers with citations over Pinecone / pgvector.
PineconepgvectorCohere RerankHybrid Search
AI Agents & Orchestration
Multi-step agents that actually finish the job — LangGraph state machines, function calling, tool use, MCP, and human-in-the-loop fallbacks.
LangGraphReActMCPTool Use
Evals, Cost & Observability
The unglamorous work that makes GenAI reliable — golden-dataset evals, CI regression gates, model routing, prompt caching, and cost/latency tracking.
LangSmithLangfuseModel RoutingEval Harnesses
Full-Stack & Distributed Systems
The platform under the AI — NestJS + Next.js services, event-driven Kafka / Pub-Sub, streaming over SSE / WebSockets, OAuth2 / JWT, and cloud-native AWS.
Next.jsNestJSFastAPIAWS
AI Safety & Guardrails
Shipping GenAI you can trust in front of customers — moderation, prompt filtering, output validation, PII & jailbreak mitigation, and read-only NL-to-SQL.
GuardrailsModerationPII MitigationValidation
Consulting
Ways we can work together
Fixed-scope engagements for teams that want GenAI shipped right — from strategy to a production build to a cost & reliability rescue.
01
Service 01
GenAI Product Strategy & Build
From a fuzzy idea to a shipped, revenue-driving LLM feature. I scope the use case, design the architecture, and build the first production version with your team.
- Use-case & ROI scoping
- Reference architecture
- Production MVP
- Team enablement
02
Service 02
RAG & Knowledge Assistants
Turn your docs, tickets, and databases into an assistant that answers accurately with citations — not a demo that hallucinates in front of your customers.
- Retrieval pipeline design
- Grounding & citations
- Eval harness
- Latency tuning
03
Service 03
LLM Cost & Reliability Audit
Already live but burning cash or drifting in quality? I audit your prompts, models, and pipelines and hand back a plan that cuts spend without losing quality.
- Cost & latency audit
- Model routing plan
- Eval & regression gates
- Caching strategy
04
Service 04
AI Agents & Automation
Multi-step agents that call your tools and APIs to complete real workflows — with the orchestration, validation, and human-in-the-loop safety to run in production.
- Agent architecture
- Tool / MCP integration
- Safety & fallbacks
- Observability
Selected work
Five GenAI products, shipped to production
Each one owned end-to-end — prompts, retrieval, agents, evals, and streaming UI. These are the outcomes, not the demos.
Intellora AI2024
Advanced WhatsApp AI Chatbot
A conversational GPT-4o agent on the WhatsApp Business API with deep chat-session management — context persistence, token optimisation, and per-tenant metering — orchestrating tools via LangGraph.
+35%
Qualified leads
−28%
Low-intent traffic
GPT-4oLangGraphWhatsApp APIGuardrails
Intellora AI2024
AI Lead Qualification System
An LLM lead-scoring agent that fuses structured CRM features with conversation embeddings in pgvector, using JSON-mode structured outputs and a custom eval harness on a 5K-row golden dataset.
+41%
Lead precision
5K
Golden-dataset rows
EmbeddingspgvectorJSON modeEvals
Intellora AI2024
AI Chat Summarisation
A Claude Sonnet pipeline over long, multi-day customer conversations — hierarchical map-reduce summarisation, topic extraction, sentiment scoring, and action-item generation.
12m → 90s
Review time per thread
8×
Faster triage
Claude SonnetMap-ReduceSentimentNLP
Intellora AI2024
AI Travel Itinerary Agent
A multi-step planning agent on LangGraph state machines that decomposes intent, calls flight / hotel / POI APIs as tools, validates with Pydantic, and streams the itinerary over SSE.
<6s
p95 generation latency
Parallel
Tool execution
LangGraphFunction CallingPydanticSSE
Intellora AI2024
AI-Powered CRM
LLM features woven across the CRM — auto-drafted follow-ups, deal summaries, next-best-action, and a natural-language query interface that generates guarded, read-only SQL.
100%
Sales adoption in 4 weeks
NL → SQL
With guardrails
NL-to-SQLNext-Best-ActionRAGGuardrails
Intellora AI2024
LLM Cost & Reliability Platform
The evaluation and cost backbone under every product — model routing across Haiku / Sonnet / Opus, prompt caching, structured-output retries, per-tenant budgets, and CI regression gates.
−38%
LLM spend
CI-gated
Every prompt change
Model RoutingLangSmithPrompt CachingEvals
Want the architecture behind any of these? Ask me about it
Track record
From unicorn scale to 0-to-1 AI
Nine-plus years across consumer platforms, real-time systems, and founding-engineer builds — the hard-won foundation your GenAI initiative can lean on.
Intellora AI
CurrentAI Engineer•Jan 2024 — Present
- Designed and shipped 5 production LLM products end-to-end across a multi-tenant SaaS platform; defined the 12-month AI roadmap across 4 product squads.
- Built RAG pipelines and vector search over tenant knowledge bases (Pinecone + pgvector, hybrid retrieval, Cohere reranking, citations).
- Cut LLM spend ~38% via model routing, prompt caching, and per-tenant token budgets — quality held on golden-dataset evals.
LLMRAGLangGraphNestJSNext.js
Allo Health
Full-Stack Engineer — Founding Engineer•Jun 2022 — Dec 2023
- Built the full consumer telehealth platform (Next.js SSR + TypeScript + Node.js + PostgreSQL) — booking, payments, diagnosis flows — from 0 to production in 4 months.
- Grew engineering from 1 to 6; established unit, integration, and E2E testing (Jest, Cypress 120+, Playwright 150+) wired into CI.
- Led A/B testing on checkout via Next.js Middleware, lifting conversion 22%.
Next.jsTypeScript0-to-1Testing
Licious
Software Development Engineer II•Aug 2020 — Jun 2022
- Engineered a distributed consumer platform at unicorn scale (50K+ concurrent users) on Next.js SSR + Node.js + PostgreSQL.
- Ran A/B testing infrastructure with 8+ concurrent experiments via feature flags — the same discipline that underpins prompt / model evaluation.
- Scaled engineering from 3 to 11 and authored architecture RFCs adopted across 5 product teams.
ScaleA/B TestingDistributed SystemsRFCs
Noticeboard
Senior Software Engineer•Jul 2018 — Aug 2020
- Built a WhatsApp-style real-time chat app on WebSockets (React + Node.js + PostgreSQL), plus an LMS and live geolocation attendance system.
- Conversational, stateful, low-latency message-streaming patterns that transfer directly to modern LLM chat; maintained 82%+ test coverage.
WebSocketsReal-timeReactNode.js
Housejoy
Software Engineer•Jul 2017 — May 2018
- Built a consumer home-services e-commerce platform (React.js, migrated from PHP Laravel) serving 40K+ monthly users — improved page load 30% via code splitting.
ReactE-commercePerformance
Practo
Software Development Engineer•Sep 2016 — Jun 2017
- Developed internal healthcare tools and doctor-facing dashboards (React.js + Python Flask) — early Python web-stack exposure now central to LLM application development.
ReactPythonHealthcare
M.Tech, Computer Science— IIIT Hyderabad
Toolkit
The stack I reach for
Deep in the modern GenAI toolchain, grounded in production backend and frontend engineering.
LLMs & Frameworks
GPT-4oClaude (Opus/Sonnet/Haiku)GeminiLlama 3LangChainLangGraphLlamaIndexVercel AI SDK
RAG & Retrieval
PineconepgvectorWeaviateQdrantHybrid SearchCohere RerankEmbeddingsSemantic Chunking
Agents & Safety
ReAct AgentsFunction CallingMCPStructured OutputsGuardrailsModerationPII MitigationHuman-in-the-loop
Evals & MLOps
LangSmithLangfuseHeliconeGolden DatasetsPrompt A/B TestingCI Regression GatesModel RoutingPrompt Caching
Backend & Data
Node.js / NestJSPython / FastAPIPostgreSQLRedisKafkaPub/SubWebSocketsSSE
Frontend & Cloud
Next.js (App Router)React 18TypeScriptAWS (Bedrock/Lambda)GCPDockerKubernetesCI/CD
Signal
Outcomes people remember
Akshay took our AI lead-qualification from a rules-based guess to a system we actually trust. The 41% precision lift showed up directly in sales-accepted leads.
Product Lead
Intellora AI
He shipped our entire telehealth platform from zero to production in four months and grew the team around it. Rare combination of speed and engineering discipline.
Founding Team
Allo Health
The LLM cost audit paid for itself almost immediately — 38% off our model spend with no drop in quality, backed by evals we could actually see.
Engineering Manager
SaaS Platform
Representative feedback from teams & stakeholders across recent engagements.
FAQ
Questions, answered
What clients and partners usually want to know before we start working together.
We're not sure where AI fits yet — can you still help?
That's often the best place to start. I run a short discovery to map where GenAI creates real value in your business, cut the hype from the genuine opportunities, and prioritise a first build that pays for itself. You leave with a clear, costed roadmap — whether or not we build it together.
What kind of engagements do you take on?
Focused consulting and build partnerships: AI strategy and discovery, GenAI product builds, RAG and agent systems, and LLM cost & reliability audits. Engagements run from a two-week audit to a multi-month build embedded with your team.
Can you work with our existing team and stack?
Yes. I embed with product and engineering, work in your codebase, and leave your team able to own and extend what we build. My stack centres on Next.js, NestJS/FastAPI, and the major LLM providers, but the patterns transfer.
How do you keep LLM features reliable and on-budget?
Golden-dataset evals wired into CI, model routing across model tiers, prompt caching, structured-output validation, and per-tenant token budgets with cost/latency observability. Reliability is designed in, not bolted on.
How do we work together — and from where?
Most delivery is remote — I'm based in Bengaluru and collaborate with distributed teams across time zones. For clients in Bengaluru, I'm also happy to work on-site from your office; for teams elsewhere, occasional on-site when it helps. We can start small with a discovery or audit and scale up from there.
Let’s build
Ready to make your business AI-native?
Whether you’re exploring where AI fits or need a GenAI feature shipped and made reliable, tell me what you’re working on. I’m happy to help — and I reply to every serious message.
Email
akshay.dharphale@gmail.com
Based in
Bengaluru, India • Remote-friendly
Availability
Available for new AI consulting & build partnerships