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AI Engineering, Evaluation & Agentic Document Retrieval
We build AI systems that survive contact with production - and we prove they work.
Most AI projects stall in the gap between a convincing demo and a system people actually trust. Closing that gap is what we do: retrieval architectures that hold up on real corpora, evaluation that catches regressions before your users do, and agentic document retrieval that finds what vector search alone misses.
We work hands-on with your engineers rather than delivering slideware, and we hand over systems your team can run without us.
Contact us to schedule a non-binding expert call.
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AI Engineering
Taking a working prototype to production is where most of the engineering actually lives: retrieval architecture, orchestration, latency and cost budgets, failure handling, and the operational plumbing that keeps an assistant answering correctly on a Monday morning. We design and build these systems with your team, on your infrastructure and your data.
Read more about AI conversational search.
AI Evaluation
An AI system you cannot measure is one you cannot safely change. We build evaluation into the system itself: groundedness and citation checks, regression suites that run on every change, human-in-the-loop review where judgement is required, and alignment and stability metrics that tell you whether last week's fix quietly broke something else.
Read more about evaluating RAG and LLM systems.
Agentic Document Retrieval & Knowledge Extraction
Standard RAG retrieves a handful of passages and hopes the answer is among them. That fails the moment a question spans an entire corpus, or when the answer lives in a table, a spreadsheet, or a document nobody chunked well. We combine vector, keyword, and structured retrieval with corpus-wide extraction - hierarchical prompting and dynamic attribute mapping - so questions get answered from everything you have, not from the top five chunks.
Read more about agentic document retrieval and large-scale knowledge extraction.
How We Work
We form long-term partnerships rather than one-off engagements: understanding your constraints, co-creating solutions with your people, and staying available as the systems and the models underneath them change.
Data Science & Machine Learning
Not every problem needs a language model. Where classical methods fit better, we use them: statistical models, machine learning, and predictive analytics to turn raw data into decisions. We help teams develop a data strategy, implement data-driven solutions, and build the skills to maintain them.
