How does Linkence protect sensitive enterprise data?

Security is built into every layer. Linkence uses end-to-end encryption at rest and in transit. Role-based access controls ensure users only see documents they are authorized to access. Enterprise customers can deploy in their own VPC or on-premise infrastructure.

What AI models can we use with Linkence?

Linkence supports 15+ LLM providers including OpenAI, Anthropic Claude, Google Gemini, Mistral, Cohere, and more. You can also connect to Azure OpenAI, AWS Bedrock, or self-hosted solutions like Ollama. Bring your own API keys and switch models anytime with no vendor lock-in.

Which tools does Linkence integrate with?

50+ popular tools including Gmail, Slack, Notion, Jira, Confluence, Google Drive, Salesforce, HubSpot, Zendesk, GitHub, and more. Custom API connectors are also supported for internal systems.

How long does Linkence implementation take?

Most teams are up and running in under a week. Initial connector setup takes minutes, access control configuration can be done in hours, and rollout to teams happens in days. Enterprise deployments with custom requirements typically take 2-4 weeks.

What authentication methods does Linkence support?

Linkence supports enterprise-grade authentication methods including email/password, passwordless OTP, Google OAuth, Microsoft OAuth, Azure Active Directory, and generic OAuth/OIDC for providers like Okta and Auth0. Just-in-time provisioning auto-creates accounts on first login.

Can Linkence be deployed on-premise or in a VPC?

Yes. Enterprise customers can choose VPC deployment in their cloud account or fully on-premise installation. Linkence supports AWS, GCP, and Azure with flexible deployment models to meet security and compliance requirements.

Linkence for Engineering, search across repos, issues, pull requests and wikis

Natural-language search for engineering context

Engineering decisions live across tickets, repository docs, PR discussions, wiki pages, and chat. Linkence is built for teams who need to bring all of that into a single, cited answer.

GitHub and Bitbucket supply repository documentation, issues, pull requests, and review context. Jira supplies execution state. Confluence stores agreed design. Slack and Teams often hold the incident decision. AI Chat fuses them with hybrid retrieval so keywords and natural language both work.

Safe engineering actions

Actions can prepare Jira updates, channel status notes, or repository-related follow-ups with approval. Engineering teams get workflow support without turning every chat prompt into an unreviewed write.

Example engineering questions

“Why was this architecture decision made?”
“Which PR changed this behaviour?”
“Which Jira ticket explains this bug?”
“Summarise the incident timeline from Slack, Jira, and Confluence.”
“Find the runbook for this service.”
“Which release risks are still open?”

Best for

Engineering managers, platform and SRE teams, security engineers, tech leads, and developers who need ticket, pull request, wiki, repository, and incident context in one cited search surface.

Common workflows

Summarise incidents and releases

Combine channel messages, Jira work, Confluence runbooks, and pull-request context into a timeline or release-risk summary.

Find architecture decisions

Ask why a system behaves a certain way and retrieve the Confluence page, PR discussion, or Jira ticket that explains it.

Prepare Jira follow-ups

Use Actions to draft issue updates or follow-up tasks from chat context, with approval before any write.

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