Language models grounded in your enterprise, not generic chat

We design and build applications on top of large language models — knowledge assistants, retrieval-augmented systems, and content generation — grounded in your organization's own documents and workflows, with the evaluation and guardrails enterprise deployment requires.

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What this covers

Enterprise Generative AI Strategy

Identifying where generative AI genuinely fits an organization's workflows and data.

Enterprise Knowledge Assistants

Internal assistants that answer from an organization's own knowledge, not the open internet.

Retrieval-Augmented Generation

Architecture that grounds a language model's output in real, current enterprise data.

Document & Knowledge Intelligence

Extracting and structuring knowledge from large, unstructured document sets.

Conversational Enterprise Applications

Chat and assistant interfaces built into enterprise workflows, not standalone demos.

LLM Application Engineering

The engineering work of turning a model into a reliable application: orchestration, retrieval, and integration.

Evaluation, Guardrails & Governance

Measuring accuracy and enforcing boundaries before and after a system reaches production.

Grounded, not generic

A knowledge assistant is only useful if it answers from the right documents. Retrieval architecture is what connects a language model to an organization's actual knowledge.

Where this applies

Internal policy and knowledge assistants for large document sets

Customer support augmentation grounded in product documentation

Contract and document summarization for legal and compliance teams

Content generation pipelines with human review built in

Enterprise search across previously siloed knowledge sources

Generative AI engagements typically start with a scoped pilot to validate accuracy against real queries, before committing to full production rollout.