Give people a faster path to the information they need
We build applications around large language models (LLMs) to search approved documents, summarize information and turn unstructured material into something your team can use. That might mean a product-support assistant, a searchable technical knowledge base or a first draft of a document for review.
We can connect answers to your own sources through retrieval-augmented generation (RAG), carry source references into the interface and respect the user’s access permissions. The goal is less time searching and re-entering information, with a way to inspect where an answer came from.
Bring predictions into the product experience
An image classifier, a signal-analysis model or a risk prediction needs more than an inference endpoint. It needs the right inputs, a useful presentation of the output and a clear next step for the person using it.
We help integrate AI/ML models into mobile, web and cloud applications. The work can include data preparation pipelines, model serving, version tracking and monitoring. We design how people review outputs and limitations, with model-performance and clinical-validation responsibilities agreed with your team and qualified specialists.
Build agents that can carry a workflow forward
Agents can use tools and coordinate steps across your systems. We can build them to gather supporting records, prepare draft release material, triage requests or hand work to the right person. They can reduce repetitive coordination while keeping consequential decisions with the people responsible.
The boundaries are part of the design. We define which tools an agent can use, what information it can access, when approval is required and how its actions are recorded. Retries, exception handling and a human handover make the workflow supportable.
Automate the repetitive parts
Some work needs AI judgment; some needs a dependable sequence of rules. We combine the two where useful, connecting APIs, cloud services and existing applications to reduce manual copying, filing and status checks.
Examples include extracting document fields for review, preparing eQMS records, routing work and confirming that an integration completed. The output can be reviewed and traced back to its source. Required quality-system approvals remain in your process.
Design for outputs that can vary
Generative models can produce different answers to the same request. A working demo is not enough to establish that a feature will behave acceptably across the situations your users encounter.
We build evaluations around representative inputs, unsupported answers, edge cases and attempted misuse. We test source grounding and tool permissions, set escalation or fallback behavior, and track model and prompt changes. For predictive models, we agree performance measures and examine errors and uncertainty with the relevant specialists. Review and monitoring continue after release.
Start with a workflow worth improving
We begin with the task, the people doing it and the result you want to improve. A focused proof of concept can measure usefulness, quality, latency and cost before a larger commitment. You get a working slice of the product, evaluation findings and a practical path to the next stage.
An internal document assistant and a clinical decision-support feature have different intended uses and risks. We scope the data controls, validation and regulatory responsibilities accordingly. Our team can deliver the application and infrastructure, work alongside your specialists and support the product as it evolves.