Himanshu H.

I design and build AI SaaS systems end to end: the architecture, the multi tenant data model, the AI layer and the code that ships. Most teams do not need more models, they need a system that stays cheap, fast and correct as customers arrive. That is the part I own. 🧭 Where I come in Early: you have a product idea with AI at the centre and need the architecture decided before anyone writes code Middle: the prototype has customers now and is buckling, and someone has to redesign it without stopping the business Late: the system works but costs too much, moves too slowly, or fails an enterprise security review Rescue: a build has stalled, the original team is gone, and you need someone to read the code and tell you the truth about it 🧱 What I architect Multi tenant SaaS with real isolation between customers, at the database and at the retrieval layer AI pipelines: ingestion, chunking, embedding, retrieval, generation, evaluation, each replaceable on its own Model routing that sends cheap requests to cheap models and only escalates when it needs to Background job systems for the work that cannot happen inside a web request Billing and usage metering, so an AI feature has a price you can defend Role and permission models that hold up when a client asks who can see what 🔧 How I approach a build Decisions written down with the tradeoff and the reason, so nobody relitigates them in six months Boring technology at the base and interesting technology only where it earns its place Every AI step observable: inputs, outputs, latency, cost, per request An evaluation harness before scale, because prompt changes without measurement are guesswork Migrations planned as reversible steps rather than one large release everyone dreads 📦 Deliverables An architecture document with diagrams, data model and the decisions behind both Working code in your repository, structured so a new developer finds their way in a day Infrastructure as code, environments and a deploy pipeline that anyone on the team can run A cost model per tenant and per feature, with the levers that move it Load and failure notes: what breaks first, at roughly what volume, and what to do about it Handover sessions recorded, so the knowledge does not leave with me ⚡ Stack AI: OpenAI, Anthropic, open weight models, embeddings, RAG, agent and tool calling patterns Backend: Python, FastAPI, Node, TypeScript, Django Data: Postgres with pgvector, Redis, Pinecone, Qdrant, ClickHouse for usage analytics Frontend: React, Next.js when the product needs one person across the whole stack Infrastructure: AWS, GCP, Docker, Kubernetes where it is warranted, Terraform, GitHub Actions Queues and orchestration: Celery, BullMQ, Temporal, plain cron when that is genuinely enough 💬 What I will ask on the first call What the system has to guarantee, and what it is allowed to get wrong How many customers you expect, and how uneven their usage is What a single request is allowed to cost you Which parts already exist, who built them, and whether anyone still understands them What the deadline is attached to, because a funding round and a wish are different constraints 🤝 How we would work A short paid discovery first on anything substantial, so the plan is based on your code rather than my assumptions Weekly demo of something running, not a percentage complete I argue with you before the build, agree during it, and document either way Comfortable overlap with European and US working hours I will tell you when a piece of this should be bought rather than built ✅ Good fit if You are building a product, not a proof of concept, and expect it to carry paying customers You want architecture and implementation from the same person, so the design survives contact with the code You would rather spend a week on the data model than a quarter undoing it You want someone who will say no to a feature that will cost more to run than it earns Send me what exists today, even if it is embarrassing. A messy repository and an honest description of the problem is a far better starting point than a polished brief.