When Your AI Demo Works But Production Doesn't
We stabilise, scale, and harden AI product builds with rapid AI development tools — so they work under real traffic, real users, and real expectations.
No sales pitch. We review your stack and failure mode.
Most AI products fail not because the idea is weak — but because they break under load, cost too much to run, hallucinate under edge cases, or behave unpredictably in production.
What Success Looks Like
- Reliable responses under real user load
- Predictable latency and scaling
- Controlled inference costs
- Stable agent behaviour
- Production-grade deployment architecture
Recent Stabilisation Work
FAQ
What happens during a free technical triage?
We review your architecture, discuss the specific failures or bottlenecks you're experiencing, and outline a clear path to resolve them. There is no sales pitch—just practical engineering advice.
How long does an AI stabilisation engagement take?
It depends on the scope of the problem. Many critical latency or cost issues can be addressed in a few days to a couple of weeks, while building entirely new robust architectures might take longer.
What stacks do you support?
We typically work with Python and Node.js ecosystems, utilizing frameworks such as LangChain, LlamaIndex, custom ReAct implementations, and specialized vector databases like pgvector, Pinecone, and Weaviate.
Do you work with early-stage startups?
Yes, we frequently help early-stage startups transition their experimental AI features into robust, production-grade systems ready for scaling.
Can you optimise OpenAI, Anthropic, and open-source models?
Yes. We implement routing, caching, and fallback strategies that can incorporate a mix of proprietary APIs (like OpenAI and Anthropic) alongside fine-tuned open-source models.
Ready to stabilise your AI product?
Book a Free 30-Minute Technical Triage