Artificial Intelligence
AI capability built into your product where it actually earns its place.
The problem
Most AI implementations are a wrapper around a foundation model with a product logo on it. They work well in a demo and poorly in production, because the gap between "call the API" and "solve the user's actual problem reliably" is where most of the engineering lives.
We have seen products launch an AI feature that worked on clean inputs and failed on everything a real user typed. The problem was never the model — it was the layer between the model and the product that nobody built.
How we get there
Use-case audit
We find the places in your product where AI creates genuine value before we choose a model or write a line of prompt code.
Model selection and fine-tuning
The right model for the task — not the most prominent one. Where fine-tuning earns its cost, we do it.
Integration and testing
Evaluation sets built before the feature ships so degradation is measurable, not anecdotal.
Ownership
The models, prompts, and evaluation infrastructure stay with you — not locked to a vendor.
Deliverables
- Use-case specification and model selection
- Integrated AI feature in production
- Evaluation and testing framework
- Prompt and configuration ownership documentation
Stack
- OpenAI
- Anthropic
- Gemini
- LangChain
- Pinecone
- Whisper
Frequently asked
Which foundation model do you use?
Whichever fits the task — we evaluate on your actual use case rather than picking a default.
Can you work with our existing AI implementation?
Yes. If it already ships, we audit what is failing and fix the layer causing it — we do not rebuild from scratch unless the existing implementation is unfixable.
How do you prevent hallucination or bad outputs in production?
Evaluation sets, structured outputs, and retrieval-augmented approaches where the task requires factual grounding. There is no single fix — it is a product layer, not a model setting.
