Quiq Review (2026)
One AI agent across voice and messaging, priced per conversation, aimed at enterprises tired of running a different vendor per channel.
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What the score measures. Five things we can verify from published material: whether pricing is transparent, whether the product can act on your systems or only answer from documents, whether the vendor still owns its own roadmap, how much independent review evidence exists, and whether the bill stays predictable as volume grows. Weighted, then scored against an ideal platform that scores 5.
BEST FOR: ENTERPRISES WANTING ONE AGENT ACROSS VOICE AND MESSAGING · LAST UPDATED 2026-07-30
Quiq in depth
Quiq's argument is against channel fragmentation. Many enterprises have ended up with one vendor for web chat, another for SMS, another for voice, and three different sets of behaviour that customers notice when they move between them.
Quiq offers one agent across all of them, priced per conversation rather than per seat or per resolution. That billing model sits between the two extremes: less exposed than per-resolution to your own success, less fixed than per-seat.
What it does
- Unified agent across voice, chat and messaging
- Per-conversation pricing model
- SMS, WhatsApp, web chat and voice
- Integration with contact centre platforms
- Human handoff with context
- Analytics across channels
More detail on how it works
Public information is thinner than for the larger enterprise platforms, and there is limited independent review evidence, which is why we do not score it.
One agent, many channels
The reason to consider it. Consistent behaviour whether a customer arrives by SMS, web chat or phone, and one configuration to maintain rather than three.
Per-conversation billing
A middle position. You are not billed extra for the agent succeeding, as with per-resolution, and not paying for idle seats. Get the definition of a conversation in writing, including what happens when one spans two days.
Messaging breadth
SMS and WhatsApp handled properly rather than as afterthoughts, which matters for consumer brands outside the US in particular.
Setting Quiq up
Realistic time to a working deployment: Weeks. These are the standard steps for this category. We have not published a walkthrough specific to this tool yet.
- 01
Scope the calls you are automating
One or two intents, not the whole queue. Voice failure is far more visible to customers than chat failure.
- 02
Provision numbers and routing
Port a number or forward from your existing system. Carrier work, and the step with actual lead times.
- 03
Build the conversation flows
Voice needs tighter scripting than chat because there is no scroll-back and no visible menu.
- 04
Connect backend systems
Order, account or appointment lookups. Same engineering work as chat, less tolerance for latency.
- 05
Tune interruption and silence handling
Barge-in, timeouts and turn-taking. Gets the least attention in evaluation and drives the most customer frustration.
- 06
Test on real accents and real noise
Not the demo audio. Your actual callers, on phones, in cars.
- 07
Keep a fast route to a human
A voice agent with no obvious escape is the fastest way to lose a customer.
The step that takes longer than they imply. Telephony. Numbers, carrier routing and IVR replacement are infrastructure changes with real lead times, not a settings page.
What the AI actually does
Every platform here says “AI agent”. It covers a bot that reads your help centre and a system that can refund a customer. This is which one you are buying.
Quiq's agents act through a Call API behaviour, and its documentation is the most specific in this directory about what the agent cannot do.
It is also the one vendor here that argues, on its own pricing page, against being paid per resolution.
- The Call API behaviour “enables you to call off to external systems to either fetch and/or modify data”, against any HTTP target you have configured as an API Resource
- Your team supplies the reach. Each call needs the API Resource plus a pair of Python functions to shape the request and turn the response into actions
- The sandbox is tight and published: the Python runtime is “not a general software runtime”, “The import keyword is not allowed”, and there is “no way to make network requests from within your functions”, so every outbound call goes through a Call API behaviour where it can be audited
- A Verify Claim behaviour scores answers with the named Vectara hallucination evaluation model and logs the claim, its score and whether it passed — naming the model behind the marketing term is rare here
Who Should Use It
The second list is the more useful one.
Enterprises with fragmented channels
If you run different vendors for chat, SMS and voice, consolidation removes real inconsistency and real overhead.
Brands with heavy messaging volume
SMS and WhatsApp as first-class channels rather than bolt-ons.
Teams wanting middle-ground billing
Per-conversation avoids the worst of per-resolution without the fixed cost of seats.
Who should look elsewhere. Small businesses, and single-channel operations where consolidation buys nothing. Also a hard evaluation for buyers who need substantial public evidence before engaging, since there is little.
Strengths and Weaknesses
What Works
- One agent across voice and messaging is a genuine consolidation benefit.
- Per-conversation billing is a sensible middle position.
What Does Not
- Thin public information and limited independent review evidence, which is why we do not score it.
- Per-conversation definitions can be ambiguous and are worth pinning down.
- Smaller than the enterprise platforms it competes with.
Quiq pricing
Quiq does not publish pricing, though it uses a per-conversation model. Expect enterprise quotes.
Per-conversation is generally more predictable than per-resolution and less predictable than per-seat. The question that decides your bill is what closes a conversation — if a customer replies three days later, is that one conversation or two?
The full arithmetic
Ask that, and ask what happens on abandonment. Those two answers determine your invoice more than the headline rate does.
Custom and private pricing
Quiq does not publish a rate. You have to ask, which means a sales conversation before you can compare it against anything else on this site.
Contact sales ↗Ask two things on that call: what triggers a billable event, and what the rate is at twice your current volume.
INTEGRATIONS
Our Recommendation
Who we would tell to buy this, and who we would not.
Quiq addresses a real enterprise problem — one AI agent across voice and messaging rather than a different vendor and a different personality per channel — and its per-conversation billing is a more sensible middle ground than either extreme in this category. The messaging coverage is strong. The difficulties are evaluative: no published pricing, limited independent evidence, and a billing unit whose definition you will need to pin down in writing before signing. Worth a quote if channel fragmentation is your actual problem.
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The questions people actually search for about Quiq.
What does Quiq do?
Provides one AI agent across voice, web chat, SMS and WhatsApp, priced per conversation, aimed at enterprises running different vendors per channel.
How much does Quiq cost?
Not published. It uses per-conversation pricing with enterprise quotes — get the definition of a conversation in writing.
What counts as a conversation?
The question that determines your bill. Ask specifically what closes one, and whether a reply three days later starts a new one.
Is per-conversation better than per-resolution?
Generally more predictable, because you are not billed more when the agent succeeds more. Less predictable than per-seat.
Does Quiq handle voice and chat together?
Yes, one agent across both plus SMS and WhatsApp, which is its main consolidation argument.
Why is Quiq not rated on this site?
There is too little published information — pricing, independent reviews, verifiable capability detail — to score it fairly.
What are the main Quiq alternatives?
Sierra for voice and chat from one agent, LivePerson for enterprise messaging, PolyAI for voice specifically, or the native AI in NICE CXone and Genesys.