Concierge by Juniors AI vs Hexis: Features, Pricing & Which Is Better (2026)
A side-by-side comparison of Concierge by Juniors AI and Hexis — features, pricing, and ideal use cases — to help you decide which AI tool fits your workflow.
Concierge by Juniors AI
Herpenza Technologies Pvt Ltd
AI-first customer support agent for WhatsApp, email and social that answers from your knowledge base and runs on your own database and LLM keys.
Key features
- AI Resolution with RAG: Retrieval-augmented answers from your indexed knowledge base, with confidence-based escalation to a human.
- Multi-Channel Inbox: WhatsApp (via Gupshup and other adapters), email and social channels in one inbox.
- Real-Time Agent Console: Live chat, internal notes, saved replies with slash commands, park-and-resume and Slack alerts.
- Role-Based Access: Human agent, supervisor and admin roles enforced at middleware on every API call.
- Configurable SLAs: First-response, resolution and escalation targets per priority with breach alerts to Slack or email.
- Analytics Dashboards: My Performance, Team Performance, Organization and AI Resolution views filterable by period, channel, priority and assignee.
- Bring Your Own Infrastructure: Stores data in your own database, cache and object storage and works with any LLM.
- MCP and API Access: Read and write tools exposed through the Juniors AI MCP server plus an OpenAPI spec.
Best for
- An e-commerce support team lets the AI answer order-status questions on WhatsApp using shipping policy and order API data.
- A support lead sets a confidence threshold so uncertain conversations are routed automatically to the right human.
- An operations manager monitors SLA health and AI resolution rates in the analytics dashboards.
- A regulated company keeps all conversations in its own database while using AI-first support.
- A developer asks Claude through MCP to list open WhatsApp conversations and summarize recurring complaints.
- A supervisor gets Slack alerts when a department is blocked or an SLA is about to breach.
H
Hexis
Bevelites GmbH
Open-source git-backed control plane for enterprise AI agents — context, skills, tools and permissions as files in your own repo, served over MCP.
Key features
- Git-backed source of truth: Context, skills, tools, permissions, and agent identities live as Markdown/YAML files in your own repository — reviewed, branched, and diffed like any other code.
- Typed knowledge with provenance: Every fact is a typed node with source, owner, and last verification date, compiled into a graph you can traverse, mass-update, and dashboard.
- Markdown skills, not prompt fragments: Procedures live as readable Markdown files that domain owners can review, instead of prompt snippets buried in a vendor config.
- Tool manifests with vaulted secrets: Declare each tool once, hold secrets in a vault, and set file-level access rules that say which agent may read which file and call which endpoint.
- Per-agent identity: Each agent is a named actor with its own credentials and scope — no shared service accounts, and every action is attributable to a specific agent.
- Any runtime via MCP or UTCP: Serves the same governed surface to Claude Code, Cursor, ChatGPT, opencode, background agents, in-platform agents, and self-hosted models.
- Open-source Apache-friendly release: Hexis is the OSS core; Bevel (paid) adds hosted services, integrations, and enterprise support on top.
Best for
- Enterprises running AI agents across multiple vendors (Claude, Cursor, ChatGPT) who want context and skills defined once in their own infrastructure instead of re-uploaded per vendor.
- Procurement, GTM, and RFI teams building specialised agents (e.g., tender desks, campaign agents) on top of a shared reusable knowledge layer with provenance.
- Platform teams that need per-agent identities and file-level access controls so every tool call is attributable and destructive endpoints are gated by review.
- Companies wary of vendor lock-in who want a portable spec (MCP/UTCP) so they can switch agent runtimes without rebuilding context, skills, and tool wiring.
- Engineering leaders who want the AI 'operating manual' reviewed in git diffs and change requests instead of edited inside a black-box vendor console.
- Open-source-first teams evaluating a control plane locally with Hexis before committing to Bevel's hosted platform for larger deployments.