AI Applications
AI products built for the day after the demo
LLM-powered products that hold up in production — not demos that fall apart on real data.
Everyone can produce an impressive AI demo now. The distance between that demo and a product people rely on is where most AI projects die: retrieval that returns the wrong context, costs that spike with usage, outputs nobody is measuring, and a prompt that broke silently three model versions ago.
We build AI applications the way we build any production system — with architecture, testing, and observability — plus the discipline this field specifically demands: evaluation suites that score output quality, budgets for cost and latency, and fallbacks for when a model misbehaves. We have been shipping LLM-backed features since the earliest GPT-3 APIs, and our own products use the same patterns we build for clients.
If you have a prototype, we can harden it. If you have an idea, we can pressure-test it before you spend a quarter building the wrong thing. Either way, you get an AI product whose behavior you can see, measure, and trust.
Capabilities
AI Applications, in practice
LLM application development
Chat interfaces, copilots, and AI-native workflows built on Anthropic and OpenAI models — with the evaluation harnesses that keep them honest as models and prompts change.
Retrieval and RAG pipelines
Search over your own documents and data that returns the right passage, not a plausible one. Chunking, embeddings, reranking, and citations designed around your corpus.
Agentic systems
Multi-step agents that call tools, query systems, and complete real tasks — with the guardrails, timeouts, and human checkpoints that production demands.
AI feature integration
Summarization, extraction, classification, and generation added to your existing product, engineered so cost, latency, and failure modes are measured rather than discovered.
Questions we hear
Asked before, answered honestly
Can you work with our data without it leaving our infrastructure?
Yes. We regularly build on cloud model APIs with strict data-handling contracts, and where requirements demand it we deploy self-hosted or VPC-isolated models. Data boundaries are an architecture decision we make explicitly at the start, not a compliance surprise at the end.
How do you keep an LLM feature from degrading over time?
Every AI feature we ship includes an evaluation suite — a set of real cases scored automatically on each prompt, model, or code change. When a model provider updates or you switch models, you see exactly what improved and what regressed before users do.
We have an AI prototype that works in demos but fails with real users. Can you fix it?
This is one of the most common projects we take on. Usually the prototype's core idea is sound and the gaps are engineering: retrieval quality, error handling, cost control, and evaluation. We audit what exists, keep what works, and rebuild the parts that were never designed for production.
Do we need our own training data or a fine-tuned model?
Rarely at the start. Most products get further with strong retrieval, careful prompting, and good evaluation than with fine-tuning. When fine-tuning genuinely is the right call, we'll show you the evidence first — it's a cost we recommend into, not by default.
Next step
AI Apps on your roadmap?
Tell us where you are — an idea, a prototype, or a system under strain. You'll get an honest read on scope, sequence, and cost from the engineers who'd build it.
Start the conversation