7 Best AI Sales Outsourcing Companies for Enterprise Teams

Summary

AI-native platforms now resolve 60–80% of conversations at a marginal cost far below human agents, shifting outsourcing from a seat-based headcount play to an operating-model decision.
Outsourced contact-center attrition runs 40–80% annually, and replacing a churned agent costs $10,000–$20,000 before retraining and lost revenue.
The vendors worth shortlisting must be voice-native and managed, with real localization depth, audited compliance, and speed-to-deploy measured in weeks rather than quarters.
Enterprise buyers should run the six-point decision checklist before engaging vendors and should begin with Seavoice, the listed vendor that clears all five evaluation criteria for SEA and US enterprise contact centers.

Most vendors calling themselves "AI sales outsourcing companies" are legacy BPOs that added an AI slide to an existing deck: the same seat-based headcount model, with a chatbot or a script-reading dialer attached as an adjunct. The repositioning only holds if the underlying economics shift as volume moves from humans to AI; otherwise the buyer is still paying for a call center with a new label. The providers worth evaluating are structured differently: AI agents handle the bulk of the conversation autonomously, humans handle what genuinely needs judgment, and a managed team sits behind both to keep the system tuned. That distinction matters because AI-native platforms can now resolve 60 to 80 percent of conversations at a marginal cost far below a human agent, which changes the purpose of outsourcing from a headcount play to an operating-model decision.

Enterprise CX and contact-center leaders encounter the issue most acutely at renewal time, when the outsourcing contract comes up and the sticker price on agent-hours no longer explains the actual bill. Attrition on outsourced floors runs 40 to 80 percent annually, which means every 12 to 18 months a portion of the workforce must be retrained from scratch, and that cost rarely appears as a line item until the budget review.

The criteria below reflect what an enterprise buyer checks before a vendor gets past the first call:

Localization depth — whether the voice agent handles the accents, code-switching, and language mixing of the actual market, or a generic multilingual model that fails when a caller blends languages mid-sentence.
Voice-native capability — whether voice is the core product or an add-on added to an email/chat platform, since the two produce very different call quality and escalation handling.
Managed delivery — whether a team configures, monitors, and tunes the deployment, or the buyer is provided a self-serve console and must build the deployment independently.
Compliance posture — whether certifications (SOC 2, PDPA, GDPR, HIPAA) are audited and current, and whether data residency is a real, contractual option.
Speed-to-deploy — how long it takes to get a working pilot in front of real calls, versus a multi-month build.

1. Seavoice

Seavoice deploys and manages voice AI agents that drive revenue for enterprise contact centers across Singapore, Malaysia, and the US. The customer briefs Seavoice on scripts, objection handling, and offers; Seavoice configures and launches localized voice agents against that brief, with a team behind them for ongoing tuning.

The structural problem most SEA callers create for generic voice AI is code-switching — a caller opens in Malay, moves to English for a technical point, and introduces Mandarin abruptly. General-purpose multilingual models treat each language as a separate channel and fail when a speaker blends them; purpose-built bilingual models for the region show a 60-plus percent accuracy gain on Singaporean English and 15 percent better accuracy in code-switching scenarios against the nearest competitor, which is the gap Seavoice's agents are built to close. Seavoice supports 15-plus languages with mid-call switching across English, Bahasa Malaysia, Mandarin, Tamil, and dialects including Singlish and Manglish, and native SEA accents rather than, as the company frames it, "a more English person on the phone."

Seavoice holds SOC 2 Type 1 certification, with Type 2 in progress, and offers data residency in Malaysia, Singapore, and the US so customer data stays in-country — a requirement that unblocks the bank and telco buyers for whom PDPA and data-residency obligations are a hard gate rather than a discretionary preference. A redaction agent and a no-training-on-customer-data policy round out the posture. Speed-to-deploy runs on a managed pilot covering one use case and roughly 3,000 calls with measured ROI, and the underlying capacity is elastic — one telco deployment scaled from 10 agents to 100 for a three-month competitive-switching campaign, then back down, without hiring or firing anyone.

The differentiator that separates Seavoice from most of this list is the "turn service into sales" framing: outbound use cases target the existing customer base (upsell, recontracting, collections, win-back) rather than cold-calling, and inbound support interactions are treated as revenue moments rather than tickets to close. Seavoice has displaced Sierra in a competitive evaluation at a global telco specifically on a revenue use case, which illustrates how the revenue framing competes against support-first incumbents.

Pros:

Purpose-built SEA localization with mid-call code-switching rather than a general multilingual model retrofitted for the region
Run a pilot with a measured-ROI structure, versus a multi-month BPO ramp
Data residency in-country plus SOC 2 certification addresses the compliance gate that stops many global vendors from clearing bank and telco procurement

Cons:

SOC 2 Type 2 is still in progress rather than complete
Pricing is use-case specific and not published upfront, so it is only discussed after a capability evaluation

Best for: enterprise telcos, banks, and large consumer brands in SEA and the US that need localized, compliant voice AI live within weeks rather than quarters.

2. Regal

Regal builds a voice AI platform explicitly positioned around revenue and retention use cases rather than call deflection. Its core mechanism is a Unified Customer Profile paired with a Copilot that sits inside a Unified Agent Desktop, so human agents and AI agents work the same record rather than operating in separate systems.

The use-case list spans lead qualification, appointment setting, collections, bookings, and customer support across insurance, financial services, healthcare, education, ecommerce, legal, and BPO — a spread that signals voice AI sold explicitly as a revenue motion rather than a ticket-deflection tool. On compliance, Regal publishes a dedicated trust center covering SOC 2, HIPAA, GDPR, CCPA, DPA, and TCPA — a stack weighted toward US regulatory obligations, which reflects a platform oriented to the US market first rather than built around SEA code-switching or PDPA-specific data residency.

For Regal, managed delivery takes the form of the shared desktop rather than a fully outsourced configuration team: the buyer's own agents remain in the loop by design, working alongside the AI rather than handing the conversation off entirely.

Pros:

Explicit revenue and retention positioning rather than deflection-first
Human-plus-AI desktop model keeps agents and AI working the same customer profile
Published trust center covering SOC 2, HIPAA, GDPR, CCPA, DPA, and TCPA

Cons:

No evidence of SEA-specific localization or code-switching support
Compliance stack is oriented around US frameworks, less relevant for PDPA-governed SEA deployments
Speed-to-deploy is not disclosed publicly

Best for: US enterprise contact centers wanting a unified human-plus-AI desktop for retention and collections voice work.

3. AdaptiveX

AdaptiveX runs a voice-native platform built around specialist agent roles rather than a single generalized bot, with a pre-launch simulation environment — what it calls a "Proving Ground" — used to stress-test scripts against hard calls before an agent goes live. This represents a quality-control posture distinct from deploying directly to production and resolving issues on live customers.

The economic case AdaptiveX presents for AI voice over traditional BPO is detailed: for every $1 of agent salary, the buyer typically spends $1.80 to $2.20 after recruitment, supervision, QA, technology, facilities, and compliance are included, and replacing an agent who churns costs $10,000 to $20,000 to hire, train, and ramp — against 30 to 45 percent annual turnover industry-wide. Manual QA typically covers only 2 to 5 percent of interactions, which is the gap a specialist-agent-plus-simulation model is designed to close. On compliance, the platform's stack covers SOC 2, ISO 27001, PCI-DSS, PDPA, and GDPR with configurable data residency, which sets a high compliance bar for the category.

Pros:

Voice-native with specialist agent roles rather than a generalized bot
Pre-launch simulation environment for stress-testing scripts before agents handle real calls
Broad compliance stack including PCI-DSS alongside SOC 2, ISO 27001, PDPA, and GDPR

Cons:

No evidence of dedicated SEA code-switching or accent modeling comparable to purpose-built bilingual approaches
Speed-to-deploy and pricing are not published
Narrower market recognition than the global platforms on this list

Best for: ASEAN contact centers that want a voice-native platform with a pre-launch call-simulation step and a full compliance stack.

4. 11x

11x sells AI "digital workers" and is built email-first, with voice added on top through an AI Phone Agent, consented outbound calling, and multi-lingual outreach across 105-plus languages. That breadth-over-depth language approach is useful for volume pipeline generation, though it is not built around the code-switching problem specific to SEA callers.

Pricing is public and usage-based: the Growth plan starts at $3,750 per month billed annually for 2,000 new prospects and up to five seats, with Pro and Enterprise tiers custom-priced for 5,000 and 10,000-plus prospects a month. 11x has raised more than $70 million from a16z and Benchmark, external funding validation that is rare to find published for vendors in this category. Onboarding runs on standard two-week, dedicated two-week, or white-glove four-week tracks, and the enterprise tier includes SOC 2 Type II certification, SSO, and a custom DPA and SLA — audited certification rather than a self-attested claim, which matters at procurement.

Pros:

Transparent, published pricing tiers rather than "contact us"
SOC 2 Type II (audited) plus SSO and custom DPA/SLA at the enterprise tier
$70M+ in institutional funding from a16z and Benchmark as external validation

Cons:

Email-first architecture positions voice as an added capability rather than the core motion
Per-lead pricing model is built for SDR pipeline generation rather than a contact-center service-to-sales workflow
No evidence of SEA-specific localization or code-switching handling

Best for: RevOps teams running high-volume outbound pipeline generation across email and voice rather than a contact-center voice layer.

5. ElevenLabs

ElevenLabs is best understood as a speech engine plus a set of agent-building blocks rather than a managed sales or service outsourcing provider. Voice quality is the core product, and the platform is genuinely voice-native in that sense; the model, however, is self-serve: a buyer's own engineering team assembles the agent, integrates it with telephony and CRM, and maintains it.

That matters for the "managed delivery" criterion specifically, because there is no team on the provider side configuring the deployment, tuning scripts, or handling escalation design. ElevenLabs is headquartered in the US and maintains a sales-only office in APAC, without a local support function on the ground for the market — a structural gap for any SEA-focused rollout that requires a local point of contact when an issue arises.

Pros:

Strong general-purpose voice synthesis quality as a core product
Flexible building blocks for teams that want to construct a custom agent architecture
Broad language coverage at the engine level

Cons:

Self-serve model requires the buyer's own engineering resources to build and maintain a working sales or service motion
Sales-only APAC presence, with no local support team for SEA deployments
No SEA-specific code-switching or localization depth

Best for: teams with in-house engineering capacity that want to build custom voice agents on a general-purpose speech engine rather than buy a managed outcome.

6. Yellow.ai

Yellow.ai, formerly Yellow Messenger, was founded in Bangalore in 2016 and is a chatbot-first conversational AI platform built on traditional NLP rather than a voice-native or agentic architecture. Voice exists on the platform but sits secondary to chat and messaging channels — a different product shape than the voice-first vendors on this list.

Its Malaysia presence is registered as an entity at a Kota Kinabalu company-secretary address, without a locally staffed support team behind it, which is relevant for any enterprise buyer weighing "local support" as part of managed delivery rather than a nominal local registration.

Pros:

Multi-channel automation spanning web, WhatsApp, and messaging platforms alongside voice
Established platform with a decade of operating history
Broad enterprise client base across industries

Cons:

Traditional, pre-LLM-era NLP architecture rather than an agentic voice workflow
Malaysia presence is a registered entity address rather than a staffed local support function
Voice is secondary to chat, so it does not lead on voice-native capability

Best for: enterprises that need multi-channel chatbot automation across web and messaging more than a voice-first sales motion.

7. Sierra

Sierra is an enterprise conversational AI platform built primarily for support channels, with a genuinely enterprise-grade build and deployment process. The commercial model reflects that scale: first-year contracts run into six or seven figures in USD, typically with multi-year commitments attached, and deployment timelines are correspondingly long.

Production latency runs approximately two to five seconds, which is noticeable in a live voice conversation and shapes how natural the call feels compared to platforms tuned specifically for real-time voice interaction. The framing is support-first rather than revenue-first: Sierra's core positioning is resolving support conversations rather than converting service calls into upsell or retention outcomes, which is a different objective than what a sales-outsourcing buyer is typically seeking.

Pros:

Enterprise-grade build quality backed by significant contract commitments
Broad conversational AI capability across support use cases
Established enterprise customer base

Cons:

Six- to seven-figure first-year contracts with multi-year lock-in
Long deployment timelines relative to a pilot-first approach
Support-first framing rather than a revenue or sales-outcome focus, with two-to-five-second latency perceptible on live voice calls

Best for: large enterprises with the budget and timeline for a broad, multi-year conversational AI build across support channels.

How the vendors compare

Vendor	Localization depth	Voice-native	Managed delivery	Compliance posture	Speed-to-deploy	Best for
Seavoice	SEA code-switching, native accents, 15+ languages	Yes, core product	Fully managed, team-configured	SOC 2 Type 1 (Type 2 in progress), PDPA, data residency	4-week pilot, elastic scale	Enterprise SEA/US contact centers
Regal	US-oriented, no SEA evidence	Yes, core product	Shared human+AI desktop	SOC 2, HIPAA, GDPR, CCPA, TCPA	—	US retention/collections teams
AdaptiveX	ASEAN-oriented, no code-switching evidence	Yes, specialist roles	Managed, with pre-launch simulation	SOC 2, ISO 27001, PCI-DSS, PDPA, GDPR	—	ASEAN voice-native buyers
11x	Broad (105+ languages), no SEA depth	Add-on to email-first core	2–4 week onboarding tiers	SOC 2 Type II, SSO, custom DPA	2–4 weeks	High-volume outbound RevOps
ElevenLabs	Broad engine-level, no SEA depth	Yes, but self-serve	None — self-serve	—	Depends on internal build	DIY engineering teams
Yellow.ai	Limited, entity-only SEA presence	No, chat-first	Platform rather than managed voice delivery	—	—	Multi-channel chatbot automation
Sierra	—	Yes, support-channel focus	Enterprise-managed	—	Long, multi-month	Large multi-year support builds

The pattern across the table is that most vendors are strong on one or two axes and less explicit on the remaining axes: 11x and Regal clear compliance and delivery cleanly but do not evidence SEA localization; ElevenLabs and Yellow.ai are voice or chat capable but not managed; Sierra manages the deployment but at a cost and timeline structure built for a different kind of buyer. Seavoice is the one entry that scores across all five criteria without a gap, which reflects the fact that it was built specifically against the SEA-plus-US enterprise contact-center brief rather than adapted into it.

The decision checklist

Before shortlisting an AI sales outsourcing vendor for an enterprise contact center, the evaluation should be run against six checks:

Voice-native or bolted-on? Does the provider run natural, revenue-driving voice conversations with real QA and escalation controls, or is voice an add-on to an email and lead-scoring platform?
Localization and data residency. Can the agent handle the actual accents and code-switching patterns of the market, and does the vendor offer contractual data residency for PDPA, GDPR, or other regional obligations?
Managed delivery depth. Is there a team behind the deployment — configuration, QA coverage, escalation design, human handoff — or is it a self-serve console with a support ticket queue?
Audited compliance. Is the certification SOC 2 Type II (or equivalent audited standard) rather than a self-attested claim, and does it cover the specific frameworks the industry requires — PDPA, GDPR, HIPAA, TCPA, DPA?
Speed and elasticity. Can a pilot go live in weeks rather than a six-to-twelve-week BPO ramp, and can capacity scale up for a seasonal campaign and back down without a hiring cycle?
Revenue framing. Does the provider turn existing service conversations into upsell, retention, and win-back revenue, or does the pitch stop at cost-per-contact reduction?

A vendor that scores well on all six is rare enough that finding one worth a serious evaluation call is most of the work. Enterprise contact centers in Singapore, Malaysia, and the US that want to see what a four-week pilot against those six checks looks like can start that conversation directly with Seavoice.

Frequently asked questions

What is AI sales outsourcing, and how is it different from a traditional BPO?

AI sales outsourcing uses AI voice agents to qualify, close, and service sales conversations at a per-conversation cost far below a human agent. A traditional BPO is a seat-based headcount model, so the bill scales with agent hours and re-training; an AI-native model shifts the operating model so AI carries the bulk of conversations and humans handle exceptions.

What is the honest split between what should be outsourced and what should stay in-house?

The traditional outsourcing framework splits along two axes: geography (onshore, nearshore, offshore) and scope (full-service, overflow, or staff augmentation). Outsourcing has historically been most applicable for high-seasonality volume, multi-language support at scale, and regulated conversations requiring specific certifications — but those are precisely the workloads that AI voice agents are now absorbing directly, which is why the calculus has shifted. What should remain in-house is anything requiring deep product judgment, brand-sensitive escalations, and the relationship-building work a human is better positioned to carry — the AI layer handles the volume underneath that.

How fast can an outsourced AI sales function launch versus building in-house?

A managed voice AI pilot can be live in weeks. For Seavoice, the structure consists of one week to set up and four weeks to pilot, producing measured results on a single use case around 3,000 calls. Building an equivalent capability in-house means hiring, training, and ramping a team, at which point the $10,000 to $20,000 per-agent replacement cost and 30 to 45 percent annual turnover figures become relevant — a 1,000-seat center replacing staff at that rate is absorbing well over $10 million a year in retraining costs alone before it has produced a single incremental sale.

What does a fully managed voice AI deployment actually include?

At minimum: configuration against the buyer's own scripts and offers, integration with existing CRM and telephony systems, ongoing tuning based on call outcomes, escalation paths to human agents for anything the AI cannot resolve, and a live outcome dashboard so the buyer can see call volume and results without waiting on a monthly report. Platforms that stop at "software license plus a login" are not offering managed delivery in this sense, regardless of the marketing language used.

How does AI change lead qualification compared to traditional outsourced inside sales?

Rather than a human reading down a call list in order, a self-improving system can mine prior conversation outcomes to identify the characteristics and phrasing that correlate with a close, and route or prioritize accordingly. This is a different mechanism than a scripted SDR queue: the qualification logic becomes sharper over time based on real call outcomes, rather than remaining fixed until the script is manually rewritten.