Quivly vs. Planhat
Both run agents on live customer data. Quivly goes live in days with Actions Feed and Radar. Planhat is a mature CS platform built around Health Lab, playbooks, and portfolio workflows.
Phased migration — see integrations or book a demo for timelines.
Both cover health, agents, playbooks, and MCP. Quivly goes live in days and adds Radar — without a months-long CSP rollout.
| Capability |  Quivly |  Planhat |
|---|---|---|
| Go live in days | Yes | —Not a focus |
| Health / risk scoring | Yes | Yes |
| AI agents & drafted outreach | Yes | Yes |
| Playbooks / automated workflows | Yes | Yes |
| MCP / assistant access | Yes | Yes |
| Works without a dedicated CS Ops admin | Yes | Partial or depends on setup |
| External market signals (Radar) | Yes | —Not a focus |
| CRM + warehouse + billing in one view | Yes | Partial or depends on setup |
Short answer: Planhat is a mature customer success platform (Health Lab, AI workflows, playbooks, MCP). Quivly is an agentic post-sales system that typically goes live in days, with customer data connected across CRM, billing, product usage, Slack conversations, call transcripts, support and Radar for external account signals.
Planhat is a full CS platform with Health Lab scoring, AI agents and workflows, playbooks, and an MCP server into assistants like Claude. Real deployments typically take weeks to months. Quivly is purpose-built for post-sales execution — Actions Feed, Ask Quivly, and Radar — with go-live typically in days. Overlap is real on health, agents, and automation; the split is time-to-value, connected customer data depth, and external signals.
CS teams that want a proven CSP: configurable health in Health Lab, playbooks triggered by score changes, portfolio monitoring, and agents that draft communications and escalate what needs a human. Strong when you have (or will hire) ownership for ongoing configuration.
Teams that want post-sales execution live in days on connected customer data — not a multi-month CSP program. Ask Quivly answers across CRM, warehouse, billing, and support; the Actions Feed proposes and drafts next steps; Radar adds market and account signals outside the product.
Both offer health and risk views, AI that drafts and assists CSMs, workflow automation, CRM and product-usage integrations, and MCP access from AI assistants. Choosing between them is less “who has AI” and more how fast you need to be live, and whether Radar and a wider connected view matter more than a full CSP suite.
Consider Quivly when you need days-not-months time-to-value, Radar-style external signals, and Ask Quivly over warehouse, billing, and support — not only CSP data. Keep Planhat if a full CS platform rollout with deep Health Lab configuration is the operating model you want.
“Before Quivly, our team spent over half their time on manual data-gathering and spreadsheet wrangling. We saw immediate ROI: within our first two weeks.
Now months in, our CSMs are managing up to 60% more ARR in their books of business compared to before—effectively saving us from needing additional headcount as we scale.”
Robyn Lee
Director of Customer Experience · Mycroft
"We look at Quivly notifications every day. Quivly has become a core part of our customer intelligence stack."
Shalabh
Head of Customer Solutions Engineering · Union AI
"Churn risk starts on day one. Quivly shows us what's happening in product usage, adoption, and value moments — there's immense value in that."
Chalam PVS
Co-founder, CEO · Siftly
"At Upflex, we have lots of different business models. The way Quivly centralizes all those disparate data points to predict revenue growth is amazing."
Ginger Dhaliwal
Cofounder · Upflex
"As founders we can't be in every QBR and every spreadsheet. Quivly is where we see which accounts are actually healthy and where expansion is real—so we spend time on revenue, not on stitching dashboards together."
One Chowdhury
Co-founder · Octolane AI
Customer 360
CRM, data, billing, product, and support connected in one place — so health and risk stay tied to the facts behind the account.
At-risk · by P(churn)
Signals re-ranked every hour
Railway
Vercel
Supabase
Resend
Vercel: Health 84 → 66 in 2 wks
Railway: Score crossed below 50
Actions Feed
Signals become recommended plays with drafts your team can send. Less stitching tools; more judgment.
Expansion playbooks
Start when expansion signals match
Supabase
Expansion QBR + pricing bands
Vercel
New seats: approval loop
Railway
API tier — upgrade nudge
Slack: #expansion — Supabase QBR sent for review
Automated
Vercel: 12 seats — finance approval draft
Needs approval
In-app: Railway “upgrade API tier” nudge
Automated
Ask Quivly
Ask about risk, renewals, or what changed — grounded in connected systems, not one-off exports.
Portfolio command center
Heatmap · renewals · team load
Portfolio health heatmap
Segments × regions · warmer tone = more attention needed
Renewal risk · ARR at stake
prob.-weighted
CSM workload & capacity
Radar
Market and account context next to operational signals — not only product usage and CS engagement.
Expansion signals
Ranked for your book · last 30d
Supabase
Seats +18 · API calls +42% vs LM
+$240K ARR
Vercel
New workspace · 3 envs in 14d
Uplift
Railway
Adoption: deploys 2.1× L90
+$95K
Resend
Steady usage, cross-sell fit
QBR
Vercel+Supabase: shared expansion cluster
Railway crossed usage tier threshold
Quivly typically goes live in days. Planhat implementations commonly take weeks to months, especially when data modeling or integrations are heavy. We connect sources, validate health and workflows on a pilot, then expand.
Yes. Planhat offers AI agents and workflows, Health Lab with LLM-enriched scoring, and an MCP server into assistants like Claude. Quivly’s difference is days-not-months go-live, connected customer data across your stack, Actions Feed, Ask Quivly, and Radar — not whether AI or MCP exists.
Planhat deployments often benefit from a dedicated admin or CS Ops owner for configuration and ongoing tuning. Quivly is designed so post-sales teams can run without standing up a heavyweight CSP admin function.
Often weeks, not quarters — shorter than a greenfield Planhat-style CSP rollout because Quivly’s own go-live is days once sources are connected. Scope depends on how many playbooks and historical views you want side-by-side before cutover.
Once pilot KPIs hold — health coverage, action throughput, Ask Quivly confidence. Many teams decommission the CSP soon after expanding the pilot to the full book.
See Quivly replace a legacy CSP — from go-live in days to cutover.
