We build intelligent systems that automate processes, enhance decision-making, and improve operational efficiency using modern AI technologies.
Where AI is actually worth building
Most AI projects fail not because the model is not good enough but because they are treated as experiments instead of products. A chatbot that demos well in a notebook is not a production system. We build AI features that are integrated into real workflows, tested against real data, monitored in production and maintained like the software they are.
The use cases that create real value are the ones where the AI has access to proprietary data, understands domain context, or automates something that previously required expensive human time. Document processing is a genuinely strong use case. Extracting structured data from contracts, invoices, medical records, compliance documents. Accurate, at volume, faster than any human team. The same applies to any workflow where a person currently reads information and makes a routing or classification decision.
Customer-facing assistants that actually know your product documentation, your pricing, your specific policies are valuable when built correctly. A system that confidently gives wrong answers is worse than no system at all. Getting this right requires proper retrieval architecture, not a bigger context window.
How RAG pipelines actually work
Retrieval-augmented generation solves the core problem with language models for production use. Their knowledge is frozen at training time and they have no awareness of your specific data. RAG gives the model access to your documents, your database, your product information at query time so the answers are grounded in your actual content.
The pipeline has more moving parts than it looks from the outside. You need to chunk documents in a way that preserves context without diluting relevance, generate embeddings and store them, write retrieval logic that finds the right content for a given question, and construct a prompt that uses the retrieved context without confusing the model with noise.
The part most teams underestimate is evaluation. Knowing whether the pipeline actually gives accurate answers requires a test set of real questions and expected answers with automated checks running against it. Without this you find out about quality problems from users instead of from your own testing.
Models, tools and how we make choices
GPT-4o and Claude are our main tools for tasks that need complex reasoning, structured output from messy input or multi-step logic. For high-volume tasks where latency and cost matter more, classification, summarization, extraction, we use smaller faster models that are a fraction of the cost and still accurate enough for the job.
Structured output has changed how we build AI features. Instead of trying to parse a free-form response, you define a schema and the model fills it in. Reliability is much higher and the downstream code is much simpler. We use this pattern everywhere the model needs to return data that another part of the system will consume.
We do not recommend AI for problems that do not need it. If a rule-based system or a simple classifier solves the problem, we use that. The goal is a useful product.