Customer support · Production

Simba Sleep unlocks £600K+ monthly revenue with Ada AI agent Luna

The problem

As Simba Sleep grew from a start-up into a major player and expanded into new markets, it needed to scale customer support without the compounding costs of headcount growth — supervisors, training, and infrastructure — while preserving the agility, talent retention, and customer-first culture it valued.

First attempt

Simba initially deployed the declarative (scripted) version of Ada and then briefly tried another platform, neither of which produced transformational results.

Workflow diagram · grounded in source
1
Customer inquiry received
Trigger
Customer inquiries arrive across web chat, email, and social community channels.
▾ source quote
“Simba's 24 person customer service team handles all customer touchpoints, including web chat, email, and social community management”
2
Luna resolves conversation
Ai action
Luna resolves an average of 1,000 conversations per week autonomously without increasing headcount.
▾ source quote
“Luna now resolves an average of 1,000 conversations per week across channels—that's the equivalent workload of 8 full-time agents—without increasing headcount”
3
Sales inquiry routing
Routing
Luna provides a Calendly link to any sales opportunity or product inquiry so customers can book a callback with the sales team.
▾ source quote
“set up guidance for Luna to provide a Calendly link to any sales opportunity or product inquiry so customers can book a callback with the sales team”
4
Agent context handoff
Integration
Luna enables the sales agents with all the information about the interaction.
▾ source quote
“Luna then enables the sales agents with all the information about the interaction”
5
Human agent handles complex cases
Human review
Human agents focus on emotive, complex, and revenue-generating interactions while Luna handles everything else.
▾ source quote
“freed up Simba's agents to focus on emotive, complex, and revenue-generating interactions, while Luna seamlessly handles everything else”
6
Weekly QA scorecard review
Feedback loop
Luna undergoes weekly QA meetings alongside human agents with scorecards assessing accuracy, tone, compliance, and customer satisfaction.
▾ source quote
“Luna undergoes weekly QA meetings alongside human agents, complete with scorecards assessing accuracy, tone, compliance, and customer satisfaction”
Reported outcome

Ada's generative AI agent Luna handles the equivalent of 8 full-time agents' workload, resolving an average of 1,000 conversations per week around the clock, while freeing 3 human agents to focus on abandoned carts and sales callbacks — generating approximately £600,000 per month in additional revenue.

Reported metrics
Additional revenue per month (header stat)£600K+
FTE workload equivalent managed by AI agent8 FTE
conversations resolved per week by Luna1,000
Additional monthly revenue from agent reallocation~£600,000
Show all 6 reported metrics
additional revenue per month (header stat)£600K+
FTE workload equivalent managed by AI agent8 FTE
conversations resolved per week by Luna1,000
additional monthly revenue from agent reallocation~£600,000
agents shifted to revenue-generating tasks3
customer support availability24/7 instant customer support around the world
Reported stack
AdaCalendly
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Source
https://www.ada.cx/case-study/simba-sleep
Read source ↗

Frequently asked questions

What did this team achieve with this AI workflow?

Ada's generative AI agent Luna handles the equivalent of 8 full-time agents' workload, resolving an average of 1,000 conversations per week around the clock, while freeing 3 human agents to focus on abandoned carts an…

What tools did this team use?

Ada, Calendly.

What results were reported?

Additional revenue per month (header stat): £600K+; FTE workload equivalent managed by AI agent: 8 FTE; conversations resolved per week by Luna: 1,000; Additional monthly revenue from agent reallocation: ~£600,000 (source-reported, not independently verified).

What failed first in this deployment?

Simba initially deployed the declarative (scripted) version of Ada and then briefly tried another platform, neither of which produced transformational results.

How is this customer support AI workflow structured?

Customer inquiry received → Luna resolves conversation → Sales inquiry routing → Agent context handoff → Human agent handles complex cases → Weekly QA scorecard review.

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