Kaily vs Paritok: Features, Pricing & Which Is Better (2026)
A side-by-side comparison of Kaily and Paritok — features, pricing, and ideal use cases — to help you decide which AI tool fits your workflow.
Kaily
Kaily (formerly Copilot.live)
An AI teammate for helpdesk, website chat, voice calls and collaborative document Q&A that automates support and team workflows.
Key features
- Multi-Channel Support: Combines website chatbots, helpdesk automation and AI-driven voice calls to handle customer interactions, qualify leads, and escalate to humans when needed.
- Team-Shared Document Chat: Allows multiple team members to participate in a single PDF or document conversation concurrently, enabling group Q&A and collaborative review in real time.
- Multi-Source Ingestion: Consolidates information from PDFs, Google Docs, Notion pages, and website links to produce answers that span multiple sources.
- Citations & Traceability: Every generated answer includes exact page and section citations (e.g., page numbers and chapter pointers) so users can verify source material quickly.
- Integrations & Embeds: Connects with Slack, Chrome, Google Drive and supports embeddable website chat to fit into existing workflows and touchpoints.
- Workflow Automation: Automates repetitive support and sales tasks, issue resolution steps, and can be configured to trigger downstream actions based on conversations.
- Enterprise Customization: Offers team and enterprise-focused options with customizable pricing, onboarding, security settings, and integrations to meet organizational requirements.
- Team-shared document chats allowing multiple users to participate simultaneously in a single PDF or document conversation
- Supports ingestion of PDFs, Google Docs, Notion pages, and website links (multi-source data integration)
- Per-answer citations with exact page numbers and sections for traceability and verification
- Integrations: Slack connector, Chrome extension, Google Drive integration (embeds into existing workflows)
- Web-based interface optimized for collaborative workflows and document-centric Q&A
- Configurable for team/enterprise usage — pricing and plans customized by team size
- Can be embedded or used as an AI chatbot (used as a portfolio chatbot example)
- Focused on enterprise/team scenarios such as contract review, HR policy lookups, and research report analysis
Best for
- Contract review for legal teams: Multiple lawyers collaboratively query PDFs, get pinpointed answers with page citations, and discuss findings in a shared document chat.
- HR policy lookup: HR staff search across handbooks and policy documents in one place to answer employee questions and cite exact sections.
- Research and reports analysis: Research teams aggregate PDFs, Google Docs and web sources to extract insights, annotate pages, and hold shared Q&A sessions.
- Customer support automation: Deploy website chatbot and AI voice calls to answer common customer questions, create tickets, and escalate complex issues to agents.
- Sales qualification on websites: Use embedded chat to engage visitors, automate qualification flows, and route leads to sales reps with context and transcripts.
- Knowledge base consolidation: Unify scattered documentation (Docs, Notion, websites) into a searchable, source-cited assistant for internal teams.
- Legal teams collaboratively reviewing and querying contract PDFs with page/section citations
- HR teams searching and discussing internal policy manuals across documents
- Research teams analyzing reports and consolidating answers from multiple document sources
- Customer support or operations teams automating resolution workflows and knowledge lookup
- Embedding an AI chatbot on websites/portfolios to provide contextual information about content
P
Paritok
Paritok
Non-destructive compression gateway that drops between coding agents and LLMs to cut input tokens by up to 85% without changing the agent.
Key features
- Drop-In Gateway: One environment variable (ANTHROPIC_BASE_URL) reroutes your agent through Paritok — no agent, prompt, or tool changes.
- Tool Schema Compression: 46-schema tool blocks (~29K tokens) drop to ~8K per turn by keeping relevant tools and stubbing the rest, frozen per conversation for cache stability.
- Code-Native 4B Model: A 4B compression model trained on 45K real agent trajectories keeps identifiers, paths, and errors while shrinking file reads and outputs to ~26% of original.
- read_original Recall: Every compressed segment is tagged; the agent asks read_original(ref) and gets the exact bytes locally without spending an extra turn.
- Stale History Summarization: Turns beyond a configurable recent window get summarized once when your context budget fills, so recent turns stay pristine and overflows drop to zero.
- Multi-Agent Compatibility: Works today with Claude Code, Cursor, Codex, OpenHands, and any OpenAI-compatible upstream — Anthropic and OpenAI both supported.
- Compounding Savings: Saved share grows across a session — 25% at 1 turn, 54% at 10 turns, 63% at 20 turns — against a 96,500-token baseline.
- Open Weights and Benchmark: SWE-bench Lite floor of 86.5% quality retained at 25.7% compression rate, with weights and training pipeline published.
Best for
- MCP-Heavy Workflows: Cut input bills for agents that ship 70+ MCP tool schemas on every turn.
- Long Coding Sessions: Run 3× longer coding-agent sessions before context saturation forces a hard compact.
- Bill Reduction: Estimate 54% off input tokens on a 5-developer team at 20-turn Claude Sonnet sessions (~$6,550/year).
- Self-Hosted Privacy: Route agent traffic through your own hardware with no data leaving your network — 8GB GPU is enough.
- Enterprise Cost Governance: Add compression at the gateway layer so all coding agents on the team benefit without engineering per-agent.
- Cursor/Codex/Claude Code Fleet: Standardize compression across a mixed toolchain of coding agents behind one gateway.