Concierge by Juniors AI vs HyperProbe: Features, Pricing & Which Is Better (2026)
A side-by-side comparison of Concierge by Juniors AI and HyperProbe — 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.
HyperProbe
HyperProbe
AI-native production debugger that lets coding agents place read-only probes on live running code to capture variable state without redeploying.
Key features
- Read-Only Live Probes: Places a non-blocking virtual breakpoint on a specific line in a running service and snapshots the live variable state there, with no code change, redeploy or restart required.
- Automated Incident Workflow: Picks up alerts from PagerDuty, Datadog or Slack, reads logs and traces to locate the offending file and line, plans the debugging flow, probes, captures and delivers a confirmed root-cause analysis.
- Coding Agent Integration via MCP: Ships an MCP server so Cursor, Claude Code, Codex and opencode can install the SDK, configure probes and read captures from inside the agent session.
- Multi-Runtime SDK Coverage: Supports JavaScript, TypeScript, Java, Python and Ruby services, with one SDK install covering every instance a service runs on.
- Probe Safety Controls: Every probe carries a time-to-live, a capture rate limit and a hit-expiry count, so it clears itself automatically and high-traffic lines stay bounded.
- Default PII Redaction: Sensitive values are redacted by default on every plan, with custom per-field and per-file redaction rules available at the Enterprise tier.
- Immutable Audit Log: Every probe placement and capture is recorded in a tamper-evident log, with approval gates and organization-level policy ceilings available for regulated teams.
- Per-Service Pricing Model: Billing counts running applications rather than engineers, hosts or captures, so probes and captures are unlimited on every plan including the free one.
Best for
- Shortening Time to Root Cause: Cut incident investigations that would take three to four hours of log-and-redeploy cycles down to minutes by capturing the value that explains the failure directly.
- Debugging Non-Reproducible Bugs: Inspect live memory and variable state for race conditions and data mismatches that only appear under real production traffic and never reproduce locally.
- Eliminating Debug Redeploys: Investigate a production failure without shipping temporary logging code, avoiding the risk and delay of extra deployments during an incident.
- Reducing On-Call Load on Senior Engineers: Let agents run the evidence-gathering phase of an incident so senior engineers are not pulled off roadmap work for every page.
- Confirming a Fix Was Actually Correct: Verify a hotfix against captured evidence rather than accepting an educated guess that leaves the same conditions able to fire again.
- Instrumenting a Whole Stack Safely: Roll probes across many services under org-level policy ceilings, approval gates and per-namespace allow lists that a security team defines.