AI products that actually ship — not slide-deck demos.
LLM-integrated apps, chat assistants, voice workflows, and automation systems built on Gemini, OpenAI, and Claude with the surrounding product engineering done right.
Problems I solve
- Prompt prototypes that never become a real product
- Hallucinations, prompt injection, and cost spikes in production
- No retrieval layer, no evals, no observability around the model
- Vendor lock-in to a single provider with no fallback
What I can build
- Conversational assistants (chat + voice) with grounded retrieval
- Multi-model fallback chains (Gemini → Claude → OpenAI)
- Tool use, function calling, and agentic workflows
- RAG pipelines + vector databases
- Prompt + cost observability, eval harnesses
- Streaming UX with proper error / timeout handling
Process
- 01
Define the AI surface
Decide which problems the model actually solves versus what stays deterministic. Spec the inputs, outputs, and failure modes.
- 02
Build with guardrails first
System prompt, scope rules, fallback chain, and evals land before the feature ships — not after the first incident.
- 03
Productionise
Cost dashboards, latency budgets, prompt version control, and a rollback story for prompt or model changes.
Deliverables
- Production AI feature integrated into your product
- Model-fallback + timeout + retry behaviour configured
- Prompt + eval test suite
- Cost and usage observability
Tech stack
- Gemini
- OpenAI
- Claude
- Vercel AI SDK
- Next.js
- NestJS
- PostgreSQL
- pgvector
Relevant case studies
Enterprise Auth Platform
Production-grade authentication and authorization infrastructure for modern SaaS and enterprise applications — featuring multi-tenancy, RBAC + ABAC, refresh-token rotation, session management, queue-driven architecture, and tenant-scoped security.
Enterprise Frontend PlatformEnterprise Telecom Business & Ecommerce Portal
Enterprise Angular 16 telecom platform for a Canadian telecommunications company, featuring business-side portal flows, ecommerce checkout, payment gateway integration, address lookup, SEO improvements, and Contentful-driven dynamic page and component rendering.
AI AppAuto SaaSAppAuto AI Platform
AI-powered AppAuto platform for vehicle owners and dealerships, combining mobile garage management, VIN decoding, recalls, service history, admin analytics, and a Claude-powered text and voice assistant connected to real vehicle data.
FAQ
Frequently asked questions
- Which model do you recommend?
- It depends on the workload. Most production systems benefit from a multi-tier fallback (cheap-fast primary, capable fallback, last-resort recovery). Lock-in to one provider is a risk, not a feature.
- Can you build something like the assistant on this site?
- Yes — the chatbot on owais-ali.com is built with the same patterns: server-only API key, lazy-loaded UI, deterministic intent classification, lead-form integration, full analytics.
- How do you control AI costs?
- Per-message size caps, per-conversation history caps, output-token budgets, temperature tuning, and dashboards on token spend. No surprises at month-end.
Ready to scope your ai app project?
Share a few details and I'll come back with a focused read on scope, architecture, and timeline.