Mammoth helps organizations build knowledge experiences that retrieve relevant information from governed sources and make it accessible within familiar workflows. Retrieval-augmented generation, or RAG, may be one component when natural-language answers are useful.
The hard work is often organizing source content, setting access rules, handling outdated material, and proving that retrieval works. We focus on source quality, permissions, citations, and evaluation before treating any language-model output as authoritative.
Inventory documents, repositories, and source systems and identify gaps in ownership or quality.
Design indexing, search, filtering, and retrieval patterns suited to the information involved.
Combine retrieval and language-model generation when natural-language assistance improves access.
Respect access boundaries and enable users to inspect the sources behind an answer.
Test coverage, relevance, unsupported claims, and failure scenarios using representative questions.
Plan synchronization, document lifecycle controls, observability, and feedback mechanisms.
We define the questions people need answered, identify authoritative content, and test retrieval quality before layering on generation. The production design accounts for identity, changing documents, answer evaluation, and the operational effort of keeping knowledge current.
These projects illustrate experience organizing extensive content and information-rich workflows, not a claim that their published implementations used RAG.
A behavioral personalization platform organized more than 20,000 educational assets, increased organic search visibility by 240%, and reduced bounce rate from 72% to 13%.
A multi-tenant digital platform served more than 650,000 members across 95 county chapters, reduced publishing cycles to two hours, and eliminated over $200,000 in annual print costs.
If valuable institutional knowledge is scattered across systems, Mammoth can help design a more useful and trustworthy way to access it.