Stop finding documents.
Start finding answers.
Traditional enterprise search returns a list of files. Enterprise AI search returns the answer -- grounded in your content, respecting your permissions, and cited back to the exact source. ClarityArc designs and deploys AI search systems that work the way your people actually think.
Your search returns files. Not answers.
Keyword-based enterprise search matches on terms, not meaning. A query about vacation carry-over policy returns every document that contains the words "vacation" and "carry-over" -- not the paragraph that answers the question.
Your knowledge is distributed across too many systems.
SharePoint, Teams, Confluence, ServiceNow, Salesforce, email -- enterprise knowledge is fragmented across a dozen platforms. Your search tool indexes one. Your people search all of them manually, every time.
Consumer AI tools are not the answer either.
ChatGPT and Copilot without grounding hallucinate. They answer confidently from training data that may predate your business, your policies, and your products. Accuracy requires grounding in your actual content -- not general AI knowledge.
Enterprise AI search that understands meaning, not just keywords
ClarityArc builds enterprise AI search on a hybrid retrieval foundation -- combining semantic vector search with keyword matching to capture both meaning and exact terminology. The result outperforms either approach alone by 15 to 30% on real enterprise knowledge bases.
Every deployment is scoped to your specific knowledge environment -- your sources, your document types, your access control model, and your user query patterns. There is no off-the-shelf configuration that works for enterprise knowledge retrieval at scale.
Keyword Search
Matches on exact terms. Returns a list of documents ranked by term frequency. No understanding of query intent. No ability to synthesize an answer. Returns noise alongside signal.
Vector Search Only
Understands semantic meaning but can miss exact terminology -- critical for technical, legal, and regulatory content where specific terms matter. Better than keyword alone, still incomplete.
Hybrid Retrieval with Answer Synthesis
Semantic vector search combined with keyword matching, re-ranked by relevance, and synthesized into a grounded answer with source citations. Outperforms either single method by 15-30% on enterprise content.
The knowledge problems AI search solves best
Internal Policy & HR Knowledge
Employees ask natural language questions about benefits, policies, and procedures. AI search returns the specific policy clause that applies -- not a link to a 40-page PDF. HR teams spend less time answering repetitive questions. Employees get accurate answers faster.
Technical & Engineering Documentation
Engineers query across technical manuals, specifications, maintenance records, and CAD documentation using natural language. AI search finds the relevant section across thousands of documents -- without the engineer needing to know which document contains the answer.
Sales & Competitive Intelligence
Sales teams query across case studies, product documentation, competitive intelligence, and proposal libraries. AI search surfaces the most relevant precedents and talking points for the specific deal -- without requiring manual research before every customer call.
Compliance & Regulatory Search
Compliance teams query across regulatory filings, audit documentation, and internal control frameworks. AI search finds the applicable regulation or control for the specific scenario -- with citations linking back to the source document for every answer.
How enterprise AI search works under the hood
Ingest & Govern
Content pulled from approved sources, classified, chunked by document type, and indexed into the vector store
Query Processing
User query embedded into vector space and issued as both a semantic search and a keyword search simultaneously
Hybrid Re-ranking
Results from both retrieval methods merged and re-ranked by combined relevance score -- the best of both approaches
Permission Filter
Retrieved candidates filtered to content the requesting user is authorized to access before passing to the LLM
Answer & Cite
LLM synthesizes a grounded answer from retrieved content only -- with source citations returned alongside every response
Enterprise AI search is evaluated against real metrics -- not gut feel
We measure the percentage of queries for which the correct answer source appeared in the top retrieval results. Recall below 85% indicates a retrieval architecture or governance problem.
We measure whether the synthesized answer is fully grounded in the retrieved content -- with zero content invented by the LLM. Faithfulness must be 100% in governed enterprise deployments.
We measure whether the answer correctly interprets and responds to the user's intent -- not just whether it contains related content. Relevance is evaluated against a structured test set drawn from real user queries.
What enterprise teams ask before implementing AI search
We already have SharePoint search and Microsoft Search. Why do we need something different?
Microsoft Search and SharePoint search are keyword-based indexes -- they find documents that contain your search terms, not documents that answer your question. They have no semantic understanding, no answer synthesis, and no mechanism to combine results across sources into a coherent response. Enterprise AI search replaces the query-response model, not just the ranking algorithm. The difference in user experience is significant and measurable.
Can enterprise AI search work across multiple systems -- not just SharePoint?
Yes -- multi-source retrieval is one of the primary value drivers. ClarityArc deploys AI search that indexes across SharePoint, Teams, ServiceNow, Salesforce, Azure SQL, and other enterprise systems via connectors and custom pipelines. The unified search experience means employees query once and get an answer synthesized from across all approved sources -- regardless of where the content lives.
How do you handle documents that are updated frequently?
Automated incremental indexing ensures the knowledge base stays current as source documents change. We configure the indexing frequency based on how often your content changes -- typically near-real-time for frequently updated sources and nightly batch for more stable content. Stale content in the knowledge base is one of the most common production failure modes, and we address it explicitly in the architecture design phase.
What platforms do you deploy enterprise AI search on?
ClarityArc's primary deployment stack is Azure AI Search with Azure OpenAI, operating within your existing Azure tenant. For Microsoft-committed organizations, this is the natural choice -- it integrates with your existing Microsoft 365 permissions, runs inside your trust boundary, and connects natively with Microsoft Copilot surfaces. We also have experience with Elasticsearch and open-source vector stores for organizations with different infrastructure constraints.
How long does an enterprise AI search implementation take?
A single-domain AI search deployment -- one knowledge domain, defined sources, access controls, and tested accuracy -- typically takes 8 to 12 weeks from scoping to production. Multi-source, multi-domain implementations run 14 to 20 weeks. We scope the timeline explicitly in Phase 01 so there are no surprises mid-engagement.
Intelligent Knowledge Systems
View the full practice →Your organization's knowledge exists. Let's make it findable.
Whether you are replacing a failing search tool or building AI search from scratch, we start with a focused scoping conversation. Bring your current search pain points and your knowledge environment -- we will show you what enterprise AI search actually looks like in production.