AI development services for LLM apps, RAG systems, and workflow automation.

The gap between an AI demo and an AI product is entirely in what happens when the model gets something wrong. A prototype that works on five happy-path examples can fall apart the first time a user asks something unexpected — so we design for the failure case from the start: what the system does when retrieval comes back empty, when the model is uncertain, or when an action shouldn't be taken automatically without a human check.

We rarely build or fine-tune custom models, because for the large majority of commercial use cases it's the wrong lever to pull. Retrieval over your own content, combined with a strong existing model and well-structured prompts, gets better real-world results faster and is easier to maintain than a fine-tuned model that needs retraining every time your product or data changes. We'll say directly if your use case is one of the genuine exceptions.

Latency and cost compound in ways that aren't obvious from a single API call. A workflow that chains multiple model calls, or does retrieval before generation, needs real response-time budgets designed in from the start — otherwise a feature that felt instant in testing becomes noticeably slow once it's doing real work against real data volume.

Every AI feature we ship gets evaluation criteria before it gets a launch date — a defined way to measure whether outputs are actually good, not just plausible-sounding. Without that, teams end up shipping a feature, getting anecdotal complaints months later, and having no systematic way to know if it's actually improved or just changed. Evaluation is what turns 'the AI seems to work' into something you can actually manage.