AI Product Development —
From Prototype to Production
Most AI prototypes never make it to production. We've shipped 12+. We take scrappy LLM demos and build the infrastructure they need to handle real users: RAG pipelines that don't hallucinate, backends that don't fall over, and costs that don't spiral.
What We Build
Production RAG Systems
Retrieval-augmented generation pipelines with real retrieval accuracy, freshness strategies, and observability. Not just a demo.
LLM-Backed APIs
High-throughput APIs wrapping LLMs with caching, routing, fallbacks, and cost controls. Built for production SLAs.
Agentic Platforms
Multi-agent systems with reliable tool use, state management, and human-in-the-loop checkpoints.
AI Observability Infrastructure
ClickHouse-backed logging and metrics for token costs, latency distributions, and accuracy drift.
Vector Search Infrastructure
Production vector databases with hybrid search, re-ranking, and sub-50ms P99 at scale.
Model Serving Infrastructure
vLLM / TensorRT-LLM on Kubernetes with autoscaling, spot instance support, and cost-per-token optimization.
Who This Is For
- → Technical founders who built a working prototype and need it to survive real users
- → CTOs at Series A–C companies where the AI backend is the bottleneck
- → Engineering teams that shipped an MVP but lost control of cost and latency
- → Non-technical founders with budget and a clear product vision, needing full technical ownership
How It Works
Technical Audit (Week 1)
We baseline your current system: architecture, query patterns, cost breakdown, failure modes. You get a written roadmap with specific targets.
Build (Weeks 2–8)
We own the architecture and implementation. Weekly check-ins. You can see every decision in the codebase. No black boxes.
Handover + Runbook
We ship working infrastructure and hand over documentation your team can actually use. On-call is yours from day one — we train you on it.
FAQ
What's your minimum engagement size?
Our engagements typically start at $30,000. We work best with companies that need serious production infrastructure, not quick demos.
How long does prototype-to-production take?
Typically 6–12 weeks depending on scope. Most clients are in production within 8 weeks of the technical audit.
Do you work with non-technical founders?
Yes. We handle full technical ownership during the engagement and hand over a working system with documentation.
What AI stack do you use?
vLLM or TensorRT-LLM for inference, ClickHouse for observability, Kubernetes for orchestration, and whichever LLM fits the workload — Claude, GPT-4o, DeepSeek, Llama.
Can you take over an existing codebase?
Yes. We've rescued several AI products that hit production walls. The technical audit identifies what to keep, what to rewrite, and what to throw out.
Ready to Build?
Tell us what you're working on. We'll review it and tell you honestly if we can help — and what it would take.
Schedule a Call