Transform information chaos into a semantic search system that lets your team find any document, procedure, or knowledge asset in seconds—not hours.
Organizations drowning in documents, struggling with information silos, or spending too much time searching for procedures, contracts, and knowledge assets. Particularly valuable for knowledge-intensive industries where finding the right information quickly is a competitive advantage.
90% faster
Average time to find information drops from hours to seconds with semantic search
40-60% increase
Team members discover and reuse existing knowledge instead of recreating it
95%+ success rate
Users successfully locate documents on first search attempt
30-50% improvement
Faster first-contact resolution when support teams have instant knowledge access
Semantic knowledge graph that connects documents, procedures, people, and concepts with relationships and metadata
Natural language search that understands intent, context, and synonyms—not just keyword matching
Automated connectors to SharePoint, Confluence, file servers, databases, and other knowledge sources
Structured tagging system for consistent categorization and improved discoverability
Deployed search interface integrated with your existing tools and workflows, plus user training materials
We inventory your knowledge sources (documents, databases, wikis), identify key use cases, and map information flows. We interview users to understand search patterns and pain points.
We design the knowledge graph schema, build ingestion pipelines, configure semantic search models, and create the search interface. We test with sample queries and refine based on relevance.
We deploy the system, integrate with your tools, train users on search best practices, and establish governance for ongoing content management. We monitor usage and optimize based on feedback.
8-12 weeks
From discovery through deployment and initial training
2-4 hours/week
Stakeholder interviews, content review, user acceptance testing, and training sessions
Timeline factors:
$30,000 - $75,000 + managed service
Project-based pricing for implementation, with optional monthly managed service for ongoing maintenance, content updates, and optimization.
Our KnowledgeOps solutions directly impact information management and knowledge transfer metrics across your organization.
Document findability rate (%)
Average search time (minutes)
Knowledge reuse rate (%)
Time to find information (minutes)
Expert network connectivity score
Knowledge transfer effectiveness (%)
Records compliance rate (%)
Version control violations (#/month)
First-contact resolution rate (%)
Support ticket resolution time
Training material access time
SOP compliance rate (%)
We use modern semantic search technologies and integrate with your existing knowledge management systems. Our approach is tool-agnostic—we select the best-fit solution for your environment.
Risk: Sensitive documents exposed to unauthorized users through search results
Safeguard: We implement role-based access control (RBAC) that respects existing permissions from source systems. Search results are filtered by user permissions, and we audit access logs. We can integrate with your identity provider (Azure AD, Okta) for seamless access control.
Risk: Search results are irrelevant or miss critical documents, reducing user trust
Safeguard: We use iterative testing with real user queries, implement relevance feedback loops, and fine-tune semantic models based on your domain. We provide search analytics to continuously improve result quality and user satisfaction scores.
Risk: Outdated or incorrect information surfaces in search results, leading to poor decisions
Safeguard: We implement automated content freshness checks, version control integration, and metadata validation. We can flag stale documents and provide governance workflows for content owners to review and update information regularly.
Challenge: Attorneys spent 2-3 hours per case searching through 50,000+ case files, contracts, and legal precedents stored across multiple systems. Critical precedents were often missed, leading to weaker arguments.
Solution: Built a semantic search system that indexed all case files, contracts, and legal documents. Implemented natural language queries like "similar breach of contract cases with non-compete clauses" that returned relevant precedents in seconds.
Impact: Reduced precedent search time by 85% (from 2-3 hours to 15-20 minutes). Attorneys discovered 40% more relevant precedents per case, improving win rates. ROI: $180K annual value from time savings alone.
Challenge: Clinical staff couldn't quickly find treatment protocols, drug interaction guidelines, and procedure documentation scattered across EHR systems, SharePoint, and paper archives. This delayed patient care decisions.
Solution: Created a unified knowledge graph connecting clinical protocols, drug databases, procedure manuals, and research papers. Deployed semantic search that understood medical terminology and context.
Impact: Reduced information lookup time from 15-20 minutes to under 2 minutes. Improved protocol compliance by 35% as staff could quickly access current guidelines. Enhanced patient safety through faster access to drug interaction data.
Traditional search uses keyword matching—you need to know the exact words in the document. Semantic search understands meaning, context, and intent. For example, searching "employee termination process" will find documents about "firing procedures," "offboarding," and "separation protocols" even if those exact words aren't in the query. It also searches across all your systems in one place, not just one platform.
That's exactly what we solve. We build connectors to all your knowledge sources and create a unified search interface. Users search once and get results from SharePoint, Confluence, file servers, databases, and any other source we connect. The system maintains the original permissions from each source, so users only see what they're authorized to access.
Initial deployment takes 8-12 weeks, but you'll see working prototypes within 4-6 weeks. We start with a pilot on a subset of your documents to validate the approach, then scale to all sources. Users typically see 80%+ improvement in search time within the first month of deployment.
Security is built-in from day one. We respect existing permissions from your source systems (SharePoint permissions, file server ACLs, etc.). Search results are filtered by user identity, and we can integrate with your identity provider (Azure AD, Okta) for single sign-on. We also provide audit logs of who searched for what, when.
No restructuring required. We work with your documents as they are. The semantic search and knowledge graph make sense of your existing structure. However, we may suggest adding metadata tags to improve discoverability, but this is optional and can be done gradually.
Yes, we offer managed service options for ongoing maintenance, content updates, search optimization, and user support. This typically costs $2K-$5K/month depending on volume and complexity. Many clients start with project-only, then add managed service once they see the value.
Absolutely. KnowledgeOps is a natural fit for support knowledge bases. We can integrate with Zendesk, ServiceNow, Salesforce Service Cloud, and other support platforms. Support agents get instant access to troubleshooting guides, FAQs, and product documentation, improving first-contact resolution rates by 30-50%.
Let's discuss your knowledge management challenges and explore how semantic search can reduce search time by 90% for your team.
If your data quality is the root issue, we'll structure and normalize your data first before building search capabilities.
Train your team on using the new knowledge system effectively and building a knowledge-sharing culture.
Last updated: November 2025