You will get an OpenAI / Gemini integration in your NestJS backend
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Project details
You want AI in your NestJS backend (chatbot, RAG, summarization, classification, embeddings) but the LLM provider plumbing is the part that always gets ugly: silent failures, runaway token costs, no streaming, no per-tenant limits, prompts scattered across files.
In 7 days I drop a clean integration into your codebase: provider abstraction (OpenAI, Gemini, or both), streaming responses, exponential backoff with retries, structured prompt templates, and per-tenant rate limiting plus token-budget caps. RAG with pgvector if you need it.
What ships: a NestJS module with unit + integration tests, error handling, observability hooks (token counts, latency, failures by provider), and a README so anyone on your team can extend it.
This is built from production NestJS systems running OpenAI, Gemini, and Whisper pipelines, not a tutorial wrapped in a Promise.
In 7 days I drop a clean integration into your codebase: provider abstraction (OpenAI, Gemini, or both), streaming responses, exponential backoff with retries, structured prompt templates, and per-tenant rate limiting plus token-budget caps. RAG with pgvector if you need it.
What ships: a NestJS module with unit + integration tests, error handling, observability hooks (token counts, latency, failures by provider), and a README so anyone on your team can extend it.
This is built from production NestJS systems running OpenAI, Gemini, and Whisper pipelines, not a tutorial wrapped in a Promise.
Programming Languages
JavaScript, TypeScriptWhat's included $400
These options are included with the project scope.
$400
- Delivery Time 7 days
- Number of Revisions 0
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About Sherif
Backend Engineer | NestJS, TypeScript, AI Integration
100%
Job Success
Banha, Egypt - 10:21 am local time
🚀 What I've Shipped:
- Multi-app monorepo backends coordinating API + Discord/Twitch services + Next.js + Electron desktop apps, with clean architecture and a layered JWT/2FA/HMAC auth stack
- Multi-tenant SaaS systems with per-tenant schema isolation and atomic cross-tenant migrations
- AI pipelines using OpenAI, Gemini, and Whisper with response caching, retry/timeout patterns, and per-tenant cost tracking
- A commercial UXP plugin shipped end-to-end with a Cloudflare Workers licensing API
✨ Technical Stack:
- Frameworks: NestJS, Node.js, TypeScript
- Databases: PostgreSQL, Prisma, TypeORM, Redis, MongoDB
- Infra: Docker, BullMQ, WebSockets, Pusher, Pino, Sentry
- AI: OpenAI, Google Gemini, Whisper
📊 Industries: SaaS, HR Tech, Streaming/Community, Creator Tools.
How we start: share scope → I send a free technical plan → small first milestone.
Steps for completing your project
After purchasing the project, send requirements so Sherif can start the project.
Delivery time starts when Sherif receives requirements from you.
Sherif works on your project following the steps below.
Revisions may occur after the delivery date.
You share goals and your NestJS backend
Send the brief, repo link, and any prior LLM code or prompt drafts. We can do a 15-min Zoom to align on UX, fallback behavior, and budget per call.
I implement the integration with retries, streaming, and guardrails
Provider abstraction across OpenAI and Gemini, streaming responses, exponential backoff with retries, structured prompt templates, RAG with pgvector if needed, and per-tenant rate limiting plus token-budget caps.