"AI customer support" now covers everything from a chatbot that answers FAQs to an agent that actually resolves a ticket end-to-end — reading account context, taking action, and closing it out without a human touching it. Decagon and Lorikeet both sit at that more ambitious end of the spectrum, and they explicitly name each other as alternatives. But they were built for different companies: one for large-scale consumer support volume, the other for regulated B2B SaaS where every AI decision needs to be explainable to an auditor.
The short version: Decagon is the broader, larger-scale platform — true omnichannel (voice, chat, email) from one agent, built for large enterprises and high-volume consumer apps in fintech, travel and retail. Lorikeet is the narrower, more specialized option — purpose-built for regulated industries like fintech and healthtech, with full audit trails and outcome-based pricing instead of a flat enterprise contract. If you're a large consumer-facing company with high ticket volume, look at Decagon. If you're a technical B2B SaaS company in a regulated space that needs to prove exactly why the AI did what it did, look at Lorikeet.
Decagon: omnichannel AI support built for scale
Decagon builds conversational agents that handle voice, chat and email tickets end-to-end for large companies, using what it calls Agent Operating Procedures — plain-language instructions rather than rigid decision trees, which makes the agent's behavior easier to update without an engineering ticket every time policy changes. It also ships Watchtower, continuous QA monitoring built into the product rather than a bolted-on afterthought. The company was valued at $4.5B as of a $250M round in January 2026.
Pricing: not public — enterprise-only, demo required.
Strengths: genuinely omnichannel from a single agent instead of separate tools per channel; plain-language procedures are easier for non-engineers to maintain than decision-tree logic; built-in continuous QA monitoring instead of manual sampling.
Limits: no self-serve tier at all, so you're committing to a sales cycle before you see real pricing; and like most vendors in this space, its deflection-rate and cost-savings figures are self-reported, not independently audited.
Lorikeet: built specifically for regulated, technical B2B
Lorikeet targets a narrower slice of the market on purpose: regulated, technical B2B SaaS companies in fintech and healthtech, where SOC2, ISO27001 and HIPAA business-associate agreements aren't optional. Its Coach Agent runs 100% ticket QA rather than reviewing a sample, and every resolution comes with a full audit trail explaining the AI's reasoning — the kind of explainability a compliance team can actually sign off on.
Pricing: outcome-based, per resolved ticket. Start tier ~$1,500/month for under 5,000 tickets; Scale ~$4,000/month for 5,000-20,000 tickets; Enterprise custom beyond that. Per-resolution costs run $0.80-$0.95 for chat/email/SMS and $1.20-$1.50 for voice, with no per-seat charges.
Strengths: purpose-built compliance posture (SOC2, ISO27001, HIPAA BAA) instead of generic enterprise security; 100% ticket QA coverage rather than sampling; outcome-based pricing means you're not paying for seats you don't use.
Limits: a narrower focus and smaller proven scale than Decagon means it may not be the right fit outside regulated industries; and that same specialization makes it overkill — both in cost and complexity — for a company that doesn't actually need the compliance layer.
Side-by-side
| Decagon | Lorikeet | |
|---|---|---|
| Core idea | Omnichannel AI agents for large-scale support | Auditable AI support for regulated B2B |
| Pricing | Not public, enterprise demo required | Outcome-based, ~$0.80–$1.50 per resolution |
| Channels | Voice, chat, email — one agent | Chat, email, SMS, voice |
| Compliance focus | General enterprise | SOC2, ISO27001, HIPAA BAA built in |
| QA approach | Watchtower continuous monitoring | Coach Agent, 100% ticket review |
| Best fit | Large enterprises, high-volume consumer apps | Regulated B2B SaaS (fintech, healthtech) |
Verdict
Pick Decagon if you're a large enterprise or consumer app with high ticket volume across multiple channels, and you want one agent handling all of it with continuous QA baked in — and you're fine going through an enterprise sales process to get there.
Pick Lorikeet if you're a B2B SaaS company in a regulated industry where every AI decision needs an audit trail a compliance officer can actually read, and you'd rather pay per resolved ticket than commit to an opaque enterprise contract.
Both companies name each other directly as alternatives, and both also compete with Intercom Fin and Ada, which tells you this category is still sorting itself into scale players versus specialists rather than converging on one dominant approach. The honest starting question isn't "which is better" — it's whether your support volume and your compliance requirements look more like Decagon's customers or Lorikeet's.