Knowledge is scattered
Connect files, wikis, CRM, support systems, intranets, and databases into one retrieval layer.
AI & Automation
Enterprise AI Knowledge Base Development
Give teams answers they can trust from the knowledge your company already has. Shinetech builds private AI knowledge bases with source links, permissions, integrations, and evaluation workflows.
Source-linkedanswers tied back to approved business knowledge
Permission-awaredesigned around access rules and user roles
Integratedbuilt around your files, systems, and workflows
Problems we solve
Turn scattered company knowledge into trusted answers.
Enterprise knowledge lives across documents, tickets, CRM notes, SharePoint folders, policies, product specs, and individual teams. The challenge is making it searchable without losing control.
Answers need sources
Return citations and source context so users can verify answers instead of trusting unsupported summaries.
Permissions are complex
Respect user roles, departments, customer restrictions, and sensitive document access rules.
Support teams repeat work
Help employees and customers find approved answers before tickets, emails, or escalations are needed.
Onboarding is slow
Give new employees a guided way to find process, product, and client knowledge with context.
AI quality is hard to measure
Add evaluation sets, feedback loops, and review workflows to improve retrieval and answer quality.
What we deliver
A private knowledge system built around your sources and access rules.
We design the full path from source systems to user-facing answers, including ingestion, permissions, retrieval, UI, and monitoring.
Source inventory
Identify the documents, databases, systems, and knowledge repositories that should feed the knowledge base.
Connectors and ingestion
Build pipelines for files, SharePoint, Google Drive, CRM, support tickets, websites, databases, and custom systems.
Permission design
Model user access, source-level rules, sensitive content handling, and audit requirements.
Retrieval and answer layer
Implement retrieval, ranking, prompts, source citations, fallback behavior, and answer quality controls.
User interfaces
Deliver chat, search, portal, Slack/Teams, intranet, or app-embedded experiences for different users.
Evaluation and governance
Track answer quality, source gaps, feedback, usage, and review workflows for continuous improvement.
Source-to-answer architecture
The value of an AI knowledge base depends on how carefully the source path is designed. Every answer should know where it came from and who is allowed to see it.
SourcesFiles, CRM, docsApproved repositories, tickets, specs, policies, and databases.
ConnectIngestionConnectors, sync jobs, metadata, chunking, and indexing.
ProtectPermissionsUser roles, source access, sensitive data rules, and audit trails.
RetrieveAI answer layerSearch, ranking, generation, citations, and fallback behavior.
UseTeam channelsPortal, chat, Slack, Teams, support tools, or embedded app UI.
Knowledge sources, industries, and cases
Knowledge base value depends on the systems and domain content behind the answers.
Use these pages when your AI knowledge base depends on CRM data, healthcare knowledge, education content, or support-heavy operational processes.
Delivery approach
Start with high-value knowledge, then expand safely.
We help teams avoid a broad, uncontrolled rollout. The first version should answer a valuable class of questions well, then improve through feedback and measurement.
Select knowledge scope
Choose the team, process, sources, and question set for the first useful release.
Connect and protect
Build ingestion, metadata, permissions, source links, and access controls.
Build the answer experience
Develop search, chat, citations, feedback, and escalation flows.
Evaluate and expand
Measure quality, close source gaps, tune retrieval, and roll out to more teams.
Related services
Connect knowledge to automation and systems.
FAQ
AI knowledge base questions we hear often.
Can the knowledge base use our private documents?
Yes. We can connect private repositories and design ingestion around permissions, access rules, and source tracking.
Will answers include sources?
Yes. Source-linked answers are a core design principle because users need to verify important business information.
Can different users see different answers?
Yes. The system can respect source-level permissions and user roles so restricted content is not exposed to unauthorized users.
How do we know if the AI answers are accurate?
We use evaluation questions, source coverage checks, user feedback, and review workflows to monitor and improve quality.
Ready to make company knowledge easier to use?
Start with the sources, users, and question types where trusted answers would save the most time.