Enterprise knowledge
SAB helps organizations connect SharePoint, Confluence, Jira, GitHub, ServiceNow, documents, tickets, chats, and internal systems into knowledge platforms people can trust — with permissions, source trust, AI assistants, and production ownership designed from the start.
Search takes too long
Employees move between disconnected systems to answer basic operational questions.
Knowledge is hard to trust
Important documents are duplicated, outdated, or disconnected from source ownership.
AI assistants stall
Permissions, evaluation, source quality, and ownership remain unresolved.
Schedule a Knowledge Assessment
The problem
Common symptoms
Knowledge is trapped in systems and people
Critical information exists, but employees do not know where to find the reliable version.
AI demos do not become operating systems
Without permissions, source quality, citations, and evaluation, internal assistants remain prototypes.
Search quality becomes a productivity constraint
Poor relevance creates duplicated work, expert interruptions, and slow decisions.
Use cases
Internal knowledge search
Find trusted answers across documents, tickets, chats, and internal systems.
Engineering documentation assistant
Help teams locate architecture decisions, runbooks, incidents, and code context.
Support and operations assistant
Reduce escalation time by making operational procedures and known issues easier to retrieve.
Legal and compliance document search
Locate policies, contracts, evidence, and regulated documents with source trust.
Consulting firm knowledge reuse
Make past proposals, deliverables, research, and project lessons reusable without exposing the wrong content.
Insurance and regulated document retrieval
Search policies, claims, procedures, records, and regulated knowledge with auditability.
What SAB designs
Source mapping
Which systems matter, which sources are trusted, and who owns them.
Search architecture
How content is indexed, ranked, filtered, retrieved, and evaluated.
Permission model / RBAC
How access rules follow enterprise permissions without leaking knowledge.
Retrieval strategy
How the system finds useful knowledge before generating or summarizing answers.
RAG architecture
Where AI Search, retrieval, context, prompts, and citations fit into production use.
LLM cost control
How usage, latency, context size, and model choice affect operating cost.
Answer evaluation
How to measure relevance, accuracy, source quality, and user trust before rollout.
Source citation and trust
How answers show provenance, freshness, and confidence rather than hiding uncertainty.
Production rollout path
How to move from prototype to adoption without exposing fragile or untrusted answers.
What you receive
Knowledge source map
Access and permission model
AI Search / RAG architecture
Prototype scope
Evaluation framework
LLM cost and latency review
Security and governance recommendations
30/60/90-day roadmap
Schedule a Knowledge Assessment