AI Search Agents
Your Data. Instant Answers.
What We Do
Your team wastes hours every week searching for information that already exists inside your organization. Policy documents, previous project files, technical specifications, recorded decisions, and institutional knowledge scattered across email, shared drives, wikis, and siloed databases. AI search agents end that. A Retrieval-Augmented Generation system ingests all of that content, indexes it semantically rather than by keyword, and gives your team a single place to ask questions in plain language and get accurate, cited answers drawn directly from your own documents.
Not hallucinated. Not generic. Specific to your business, sourced from your content, updated as your knowledge base evolves.
How We Work
We begin by auditing your knowledge base: what documents exist, where they live, how frequently they change, and what questions people most commonly need answered. That audit shapes the ingestion strategy. We connect to your document sources, process and chunk the content appropriately for retrieval, build the vector database, and configure the retrieval pipeline that determines how documents are ranked and surfaced.
The language model layer is then configured to generate answers grounded strictly in retrieved content, with source citations for every response. We tune retrieval precision using sample queries, measure accuracy against known questions, and build a feedback mechanism that improves results over time. Deployment options include web interface, Slack integration, or API endpoint for embedding in your existing tools.
Why Running Start Digital
Frequently Asked Questions
PDFs, Word documents, web pages, knowledge base articles, Slack messages, emails, and structured databases. If it contains text, we can index and search it.
With proper retrieval tuning, they are highly accurate for questions within your knowledge base. We implement source citation so users can verify every answer against the original document.
It can sit on top of them. The AI agent searches your existing knowledge base and surfaces answers instantly, reducing the time your team spends searching for information.
We build automated ingestion pipelines that re-index updated documents. When a policy changes or a new article is published, the AI agent reflects that change within hours.
A single knowledge base with standard document types takes 3 to 6 weeks to ingest, configure, and tune. Larger deployments with multiple sources and custom integrations take 8 to 14 weeks.
Standard search returns a list of documents that might contain what you need. RAG reads those documents and synthesizes a direct answer with citations. You get the answer, not a list of places to look.
We constrain the model to answer only from retrieved content and return a confidence indicator when retrieved context is insufficient. The system declines to answer rather than guess when relevant documents are not found.