Manual ProcessesKill Margins.AI Fixes That.

A retrieval-augmented system tuned to your content type — chunking strategy, vector DB, reranking layer, evaluation harness, and a chat or API surface. Ships with an answer-quality dashboard so you can see what's working and what's hallucinating.

A multi-step agent with tool use, memory, and human-in-the-loop checkpoints. Replaces the workflow — doesn't just summarize it. Built with rails, not vibes: every step logged, every tool scoped, every failure traceable.

A Flutter app with the AI feature your product is built around — chat, voice, vision, or recommendation — plus auth, backend, and store submission handled end-to-end. From kickoff to App Store in under 60 days.

A FastAPI or Node service designed for LLM workloads from the ground up — async inference queues, response caching, cost ceilings per user, fallback chains across providers, and retry logic that doesn't melt your budget. Deployed to AWS, monitored from day one.

An admin panel that shows you the metrics that matter — model cost per user, agent success rates, RAG hit/miss ratios, prompt versioning, and content moderation flags. Role-based access, real-time data, the controls a CTO actually uses.

A two-week deep dive: codebase review, infrastructure audit, cost model, scaling forecast, and a written roadmap with prioritized fixes. Often the cheapest way to save $30k of wrong-direction work.