Production-Grade AI Systems for Startups (Backend + LLM Infra)Aakash Aggarwal
I build production-grade AI systems with a strong backend foundation, not fragile demos or prompt-only prototypes. My focus is on reliable LLM integrations, explicit decision tracing, and scalable APIs so teams can understand, debug, and trust their AI in production.
If you’ve outgrown experiments and need an AI system that actually holds up, this is what I do.
What's included
AI System Architecture & Decision Flow
A documented architecture covering the AI components, backend services, data flow, and decision points. Clearly defines where AI is used, why it’s used, and how failures are handled in production.
Production-Ready AI Backend (API + Services)
A fully implemented backend (Python/FastAPI or equivalent) exposing stable APIs for AI functionality, with proper error handling, rate limits, and scalability considerations.
LLM Integration with Explicit Decision Tracing
LLM integrations with clearly logged inputs, outputs, candidates, and decisions. Enables debugging, evaluation, and understanding why an AI output was produced.
Evaluation & Failure Analysis Setup
Basic evaluation hooks to analyze incorrect or low-quality AI outputs, including logging, metrics, and structured feedback loops for iteration.
Deployment & Handoff Documentation
Clear documentation covering system setup, environment configuration, deployment steps, and ongoing maintenance considerations so internal teams can run and extend the system.
FAQs
Early-stage startups and product teams that already have users or are close to production. My work is most valuable when reliability, scale, and failure analysis actually matter.
Yes. I often work with teams whose AI works “sometimes” but fails unpredictably. I focus on identifying decision points, adding tracing, and making failures understandable and fixable.
We start with system understanding and architecture clarity, then move into implementation or refactoring. I prefer longer-term engagements where the system can be properly built and iterated.
Example work
Aakash's other services
Contact for pricing
Tags
Docker
FastAPI
LangChain
OpenAI
Redis
AI Engineer
Backend Engineer
Software Engineer
Service provided by
Aakash Aggarwal Panchkula, India
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- Followers
Production-Grade AI Systems for Startups (Backend + LLM Infra)Aakash Aggarwal
Contact for pricing
Tags
Docker
FastAPI
LangChain
OpenAI
Redis
AI Engineer
Backend Engineer
Software Engineer
I build production-grade AI systems with a strong backend foundation, not fragile demos or prompt-only prototypes. My focus is on reliable LLM integrations, explicit decision tracing, and scalable APIs so teams can understand, debug, and trust their AI in production.
If you’ve outgrown experiments and need an AI system that actually holds up, this is what I do.
What's included
AI System Architecture & Decision Flow
A documented architecture covering the AI components, backend services, data flow, and decision points. Clearly defines where AI is used, why it’s used, and how failures are handled in production.
Production-Ready AI Backend (API + Services)
A fully implemented backend (Python/FastAPI or equivalent) exposing stable APIs for AI functionality, with proper error handling, rate limits, and scalability considerations.
LLM Integration with Explicit Decision Tracing
LLM integrations with clearly logged inputs, outputs, candidates, and decisions. Enables debugging, evaluation, and understanding why an AI output was produced.
Evaluation & Failure Analysis Setup
Basic evaluation hooks to analyze incorrect or low-quality AI outputs, including logging, metrics, and structured feedback loops for iteration.
Deployment & Handoff Documentation
Clear documentation covering system setup, environment configuration, deployment steps, and ongoing maintenance considerations so internal teams can run and extend the system.
FAQs
Early-stage startups and product teams that already have users or are close to production. My work is most valuable when reliability, scale, and failure analysis actually matter.
Yes. I often work with teams whose AI works “sometimes” but fails unpredictably. I focus on identifying decision points, adding tracing, and making failures understandable and fixable.
We start with system understanding and architecture clarity, then move into implementation or refactoring. I prefer longer-term engagements where the system can be properly built and iterated.
Example work
Aakash's other services
Contact for pricing