AI + human customer support
Let AI handle the repeatable conversations. Keep humans for the moments that matter.
We build AI chatbots and support agents, connect them to your knowledge and workflows, and add human agents for judgement, empathy, escalations and exceptions. VA20Four7 Intelligence adds Auto QA, scorecards, sentiment, pain points, feature requests and voice-of-customer intelligence on top.
Named ownership, documented workflows and clear reporting.
AI + human customer support
Live workflow
ExecutionAI + automation
ControlHuman-owned
IntelligenceMeasurable
VA20Four7 ownsRoutine support conversations
Your team keepsProduct and policy decisions
One operating model
Execution. Control. Intelligence.
Three connected layers keep the work moving without turning automation into another black box.
ExecutionAI agents + automation
Handle the stable, repeatable steps.
ControlHuman owners
Approve, resolve exceptions and own sensitive judgement.
IntelligenceQA + customer signals
Measure quality, workflow health and what users are telling you.
What the workflow can own
What you can get off your plate.
We build the bot and staff the people behind it. The work below splits between them, and where that line sits is the most important decision in the whole engagement.
Chatbot configuration
Automation built around your FAQs, policies and product information instead of generic responses.
Live chat coverage
Human agents available during the hours you choose, with handover rules agreed in advance.
Email and ticket support
Queues worked to your priorities and service levels, with useful tagging and ownership.
Escalation handling
Clear rules define what agents can resolve, what needs approval and what should reach your internal team immediately.
Knowledge base upkeep
Reusable answers are improved as new questions appear, so support knowledge stays current.
Support reporting
Volume, response patterns and recurring customer issues summarised so you can see what is driving contact.
Execution + control
Automate what is stable. Keep human ownership where judgement begins.
The workflow separates repeatable execution from the moments that require a person. AI agents and automation handle stable steps; a named human owner handles judgement, approvals, exceptions and final accountability.
AI + human customer supportWorkflow view
Customer: I was charged twice.
AI: I found the duplicate-charge policy and account context.
Escalated → Human owner
Owner: reviewed transaction history and resolved the exception.
SentimentFrustrated → resolvedThemeBilling frictionQAPolicy followed
Flagship use case
Support is where execution and customer intelligence meet.
Every conversation is work to resolve and data to learn from. AI handles repeatable requests, people own judgement and escalation, and the platform turns quality and customer signals into action.
Resolve repeatable questionsEscalate with contextAuto-QA every conversationSurface themes & requests
Conversation intelligencePlatform live
One rubric across AI and human conversations.
Repeated friction grouped into themes.
Recurring asks structured for review.
Track where customer emotion changes.
End-to-end support loopFrom customer message to operational insight.
What changes
Less manual coordination. More visible execution.
Capacity matters, but the bigger gain is a workflow with clearer ownership, better handoffs and a measurable path from request to outcome.
Work you can actually see
A record of what the bot deflected, what a person resolved and what your customers kept asking about.
Measurement built in
Because we also build the measurement side, you get a clear record of what was done, what was automated and what a person checked — rather than a monthly summary written by the person being reviewed.
Routine questions move faster
Automation can answer predictable questions immediately without forcing every conversation into the same path.
Agents spend more time on real problems
Common questions are deflected or accelerated, leaving people available for cases that need judgement.
Support becomes useful feedback
Recurring themes are easier to spot when conversations are tagged, summarised and reviewed consistently.
Fits your stackWorks around the systems you already use.
IntercomZendeskFreshdeskTidioCrispHubSpot ServiceHelp Scout
How we start
One workflow first. Prove the model, then expand.
We map the work, define what AI can execute safely, place the named human control points and launch with a contained scope before expanding.
Map
Understand the workload and current handoffs.
Design
Define AI execution, named human review and escalation.
Launch
Run the workflow inside your existing systems.
Improve
Use QA and performance signals to refine it.
Questions
Questions before you start
Clear answers before you decide whether the model fits.
How do we stop the bot making things worse?+
By deciding what it is not allowed to attempt. We scope the bot to the questions it answers reliably and route everything else to a person immediately — no loops, no dead ends, no pretending to be human.
How do you decide what the bot answers?+
By looking at your actual conversation history rather than guessing. Questions that repeat with a stable answer go to the bot. Anything involving refunds, complaints, account changes or an upset customer goes straight to a person, with the full history attached.
Does the chatbot pretend to be a person?+
No. Automation should be clear about what it is and provide an obvious path to human help when needed.
What happens when the bot cannot answer?+
The conversation can be handed to a human with the available context, using the escalation and routing rules agreed during setup.
Can we start with human agents only?+
Yes. A human-first support setup can be a sensible starting point, with automation added later once the recurring questions are clear enough to automate well.
Start with the workload
Ready to fix support without losing the human part?
Tell us what your queue looks like. We will scope the bot, the people behind it and the line between them.