AI that contact centers love š
Joined June 2018
- Tweets 706
- Following 142
- Followers 704
- Likes 195
322 Photos and videos
Pinned Tweet
Feb 5
Our new AI platform is driving real acceleration.
š 2.5Ć YoY revenue growth
āļø 1 billion customer service phone minutes automated
Built in production, proven at scale.
Read more š
replicant.com/blog/replicantā¦
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May 21
Most outbound workflows fail to ever reach a person.
That means teams waste time and resources just trying to get outbound calls to the right destination.
Our Outbound Al Agents are built to fix this:
š Boost completion rates by calling at the exact moment customers expect it
šµ Capture more revenue by responding to every lead at scale
š Automate complex B2B calls, navigate IVRs and screening questions
Read more about the next generation of Al-powered outbound: replicant.com/blog/outbound-ā¦
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May 6
Underperforming AI agents aren't always a result of insufficient modeling power.
In fact, the critical failure point often occurs well before go-live during the evaluation design stage: replicant.com/blog/ai-agent-ā¦
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Apr 23
Character-Based Voice Simulation isnāt just about accelerating AI testing.
Itās about testing AI on real scenarios specific to your business before go-live: replicant.com/blog/ai-agent-ā¦
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Apr 14
Better training data leads to better ASR which ultimately leads to higher performing AI conversations.
But not all data is created equally.
Learn how we balance quantity with quality to ensure AI doesn't just listen, but understands: replicant.com/blog/asr-modelā¦
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Apr 8
Some customer service flowsālike confirmations, updates, and geolocationāare faster to complete over text than on a live call.
With Replicant, AI Agents can keep the conversation going across channels without losing context: replicant.com/blog/how-to-auā¦
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Apr 1
Voice AI demos always seem fast. But they aren't designed to actually resolve requests.
Conversational efficiency in a production setting is a product of both engineering and strategic design: replicant.com/blog/voice-ai-ā¦
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Mar 24
The paradigm of conversation design has shifted from rigid, deterministic scripting to dynamic, context-aware reasoning.
This evolution is fundamental to how we handle real-world ambiguity at scale: replicant.com/blog/agent-expā¦
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Mar 20
š Standard testing frameworks like SOC 2 can fall short when applied to AIĀ due to the non-deterministic, free-form nature of LLMs.
Learn how our multi-pronged approach to AI security addresses LLM safety gaps that traditional audits donāt always account for: replicant.com/blog/iso-42001ā¦
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Mar 18
Maintain natural dialogue flow. But ensure model accuracy.
Balancing these competing constraints involves strategically distributing guardrails across contexts: replicant.com/blog/ai-voice-ā¦
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Mar 11
š While SOC 2 remains a foundational security standard, its static, point-in-time nature is often ill-equipped to address the non-deterministic risks inherent to LLMs.
Moving beyond traditional compliance requires a multi-pronged approachāintegrating frameworks that secure non-deterministic AI in the enterprise: replicant.com/blog/iso-42001ā¦
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Replicant retweeted
Mar 10
Most contact center AI projects never leave pilot. Here's why.
Companies aren't struggling to build one bot. They're struggling to operationalize AI across 150 intents, policies, and edge cases ā without creating another silo.
After automating 1B minutes on the phone, we built something different: AI that builds itself from your best agents' calls.
AAA went from 23% ā 75% automation success with @Replicant_AI.
Watch how we do it š [video] š¢
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Mar 5
Most AI pilots donāt fail because of ambition.
They fail because governance, consistency, and compliance werenāt priced in from day one.
Join us to learn how enterprise conversation data becomes production-ready AI agents: replicant.com/resources/evenā¦
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Mar 4
With Hallucination Checker, we combine real-time response enforcement with historical pattern analysis to ensure accuracy before deliveryāand increase stability over time: replicant.com/blog/hallucinaā¦
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Mar 3
Weāve all heard the stat: 95% of enterprise-level AI pilots fail. But what about the other 5%?
See why aligning scope with operational readiness leads to iron-clad pilots and predictable ROI: replicant.com/blog/ai-pilot-ā¦
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Mar 2
šµ The Payments Component enables teams to extend payment capabilities across AI Agents without rebuilding compliance and flow designs every time.
Read more: replicant.com/blog/payments-ā¦
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Mar 2
More intelligence ā better experience.
In voice AI, design is the differentiator.
Hereās why agent experience design is becoming the new competitive edge in customer service ā
replicant.com/blog/why-voiceā¦
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Feb 26
If your AI vendor hands you a static feature and walks away, you donāt have transformation.
You have tech debt.
Scaling #AI in customer care requires an operating system for learning, optimization, and ROI.
Read more ā replicant.com/blog/voice-ai-ā¦
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Feb 25
Deploying high-resolution AI AgentsĀ fast,Ā safely, andĀ repeatedlyĀ requires far more than a prompt and a smart model.
It requires structure, validation, and control.
Learn how AI-based tooling helps us unlock operational velocity while maintaining rigorous oversight: replicant.com/blog/acceleratā¦
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Feb 25
Great #AI agents donāt just sound human.
They:
ā¢ Prevent actions that should never happen
ā¢ Trigger actions that must happen
ā¢ Hide private info from the model
ā¢ Enforce compliance in real time
Hereās the framework behind it: replicant.com/blog/ai-agent-ā¦
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Feb 24
Ensuring AI reliability in complex enterprise workflows requires more than just better prompts.
Here's a fresh look at how we design rigid structures around critical business rules to ensure 100% accuracy when it matters most: replicant.com/blog/determiniā¦
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