AICamp

Manages multi-user chat sessions within team workspaces using role-based access control (RBAC) to segment conversation visibility and edit permissions. Implements team-level isolation at the data layer, allowing administrators to control who can view, contribute to, or export conversations. Conversations are indexed by team ID and user role, enabling efficient permission checks on read/write operations without requiring per-message ACL evaluation.

Indexes team conversations using full-text search or semantic embeddings to enable discovery of past discussions by keyword, topic, or semantic similarity. Likely implements a search index (Elasticsearch, Milvus, or similar) that tokenizes conversation content and metadata (timestamps, participants, tags) for fast retrieval. Search results are filtered by user permissions to prevent unauthorized access to restricted conversations.

Automatically generates summaries and extracts key insights (decisions, action items, questions) from team conversations using LLM-based summarization. Likely uses prompt engineering or fine-tuned models to identify structured information (who decided what, what needs to be done, what remains unresolved) and stores these as metadata for quick reference. Summaries are regenerated on-demand or cached with TTL to balance freshness and compute cost.

Enables exporting team conversations in multiple formats (Markdown, PDF, JSON) and integrating with external tools (Slack, email, project management platforms) via API or webhook. Likely implements format converters that transform internal conversation representation into standard formats, and provides OAuth/API key authentication for third-party integrations. Exports respect permission boundaries — users can only export conversations they have access to.

Tracks and visualizes team conversation metrics (number of conversations, average length, response time, participant engagement) using aggregation queries over conversation metadata. Likely implements a metrics pipeline that computes statistics on a schedule (hourly, daily) and stores results in a time-series database for efficient dashboard queries. Analytics respect team boundaries — each team sees only its own metrics.

Provides reusable conversation templates and prompt libraries that teams can customize and share. Templates likely include pre-filled system prompts, example conversations, and parameter placeholders for common use cases (code review, documentation, brainstorming). Teams can create custom templates, version them, and control access via role-based permissions. Templates are stored in a template registry with metadata (use case, author, creation date, usage count).

Enforces content policies on team conversations using automated moderation (keyword filtering, LLM-based content classification) and manual review workflows. Likely implements a moderation pipeline that flags conversations violating policies (e.g., confidential data, inappropriate content) and routes them to administrators for review. Moderation rules are configurable per team, and violations are logged for audit purposes. Flagged conversations can be quarantined, redacted, or deleted based on policy.

Abstracts underlying LLM providers (OpenAI, Anthropic, local models) behind a unified interface, allowing teams to switch providers or use multiple models simultaneously. Likely implements a provider adapter pattern where each provider (OpenAI, Anthropic, Ollama) has a standardized interface for chat completion, embedding, and moderation. Includes fallback routing — if the primary provider fails, requests automatically route to a secondary provider. Model selection can be per-conversation or per-team.