Scale Up CX Build
We build CX so they hire later, hire less, and stay close to the customer.
We build the customer experience function for scale ups before they hire one. Tailored AI agents across the channels their customers actually use, realtime assistants for the small team they already have, a knowledge base that holds up, and a quarterly voice of customer cadence that feeds the product.
How this works
Build. Run. Iterate.
The shape of the engagement mirrors the shape of the problem. We build the system from scratch, we run it alongside the team while they get used to it and grow into it, and we iterate it every quarter as the company, the product, and the customer base move.
Build
We design and stand up the CX system. AI agents on the channels the company actually uses, a tailored knowledge base built from the product and the team's own answers, realtime assistants wired into the tools the human team already works in, and a CX strategy that defines tone, escalation, and what good looks like.
Run
We operate the system with the team for the first months. Quality monitoring, prompt and tone tuning, knowledge base updates as the product changes, training the first hires as they come on. The aim is a clean handover when the company is ready, not a permanent dependency.
Iterate
Every quarter we read the conversations, surface what customers are really asking for, and turn that into changes in the CX system, in the knowledge base, and in the product roadmap. This is the layer that keeps the system from decaying and keeps the company close to its customers as it scales.
The pattern
Build the system. Run it with the team. Iterate it with the customer.
Sound familiar?
This is for you if...
Customer messages are coming in faster than your team can answer on top of their other work
Volume climbs, channels multiply, response times slip. The founder, the ops lead, or one early hire can no longer absorb CX between other responsibilities. The function needs to be built properly before someone is hired to run it.
There is a live conversation about hiring the first dedicated CX leader
The signal is operational: response times are visibly slipping and the team is actively discussing a Head of Customer Service. That hire should come into a working system, not a blank page.
Customer contact is spread across email, WhatsApp and voice with no unified system underneath
Each channel is being handled differently with no shared knowledge base, no consistent tone, and no visibility across them. The answer is not more headcount per channel. It is a system built around how the company actually operates.
You want the CX function shaped around your product and brand, not assembled from generic playbooks
The companies we serve best are still small enough that their CX setup is not yet locked in. The founder or operator is choosing how the function gets built and cares that it gets built around their company rather than bolted on from a template.
Phase 1: Build
A CX system shaped around the company.
Off the shelf CX products assume a generic company. We assume the opposite. Every component is shaped around how this specific scale up sells, supports, and speaks to its customers.
Channels
Where customers actually reach the company
Email agent
An AI agent that reads incoming email, drafts or sends responses in the company voice, handles routine threads end to end, and routes anything ambiguous to the human team with full context attached.
WhatsApp agent
A conversational agent that handles WhatsApp inbound: order status, account changes, common questions, light troubleshooting, with handoff to a human in the same thread when needed.
Voice and call centre agent
An AI voice agent that takes calls, answers what it can, and warm transfers to a human when the conversation needs one. Configurable for first line triage, after hours coverage, or full handling depending on the company's appetite.
Realtime
Where humans are still in the loop
Realtime message assistant
A copilot that sits inside the tools the human team already uses. It drafts replies, suggests tone fixes, surfaces the right knowledge base article, and keeps responses consistent across whoever happens to be on shift.
Realtime call assistant
Live transcription and prompts during calls. The agent sees the right answer surface as the customer is talking, gets nudged on disclosures, and has the call summarised and logged automatically.
Foundations
What everything else runs on
Tailored knowledge base and FAQ
We build the knowledge layer that everything else runs on, drawn from the product, the team's existing answers, and the conversations themselves. Owned by the company, structured for the agents, and kept current as a deliverable rather than a chore.
CX strategy
The non software work that makes the software useful: tone of voice, escalation rules, SLAs, channel strategy, what to automate versus keep human, and how the function should grow as the company does.
Expected outcomes
What good looks like and the metrics we move.
We do not sell ticket counts in the abstract. We sell durable changes in how a scale up's customer experience function performs, measured against the baseline we read at the start of the engagement and tracked through the Run and Iterate phases.
| Metric | Typical baseline | Target at 90 days | Note |
|---|---|---|---|
| Messages handled without a human | 5 to 15% of inbound | 50 to 65% | Depending on product complexity and channel mix |
| First response time | 24 to 72 hours | Under 5 min on AI handled, under 30 min on human handled | Averaged across channels |
| One touch resolution | 30 to 45% | 60 to 75% | Driven by better routing, knowledge access, and the realtime assistant |
| Cost per contact | €4 to 9 per contact | €1.5 to 3 per contact | Total CX cost divided by inbound message volume |
| CX headcount avoided | Volume drives headcount | 3 to 5 FTEs in the first year | Depending on volume curve |
| Repeat question rate | Steady or rising | Declining each quarter | As product fixes ship from the Iterate read |
Specific targets are written into the Statement of Work against the company's actual numbers at kick-off. Treat the ranges as what is realistic at 90 days of steady-state operation.
Phase 3: Iterate
Voice of customer, every quarter, fed back into the company.
Every customer conversation is data about the product, the experience, and what the company should change next. Most CX setups never use it. The Iterate phase is the recurring layer where we read what customers are actually saying and turn it into decisions the company can act on.
Deliverable
What it includes
Top conversation drivers
What customers are contacting the company about, ranked by volume, with movement quarter on quarter so the team can see what is growing and what is shrinking.
Root cause read
Why each driver exists, read directly from the conversations, not inferred from category tags. Product friction, copy ambiguity, missing information, expectation gaps, billing logic.
Product and UX recommendations
Specific, scoped changes the product team can pick up, sized to a sprint or a quarter, not generic advice.
CX system updates
Knowledge base, prompt, agent and routing changes we will make on the back of the quarter's conversations, so the system gets sharper as the company learns.
Strategy check in
Where the CX function should be heading next quarter: channels to add, automation to expand or pull back, hiring signal, escalation policy adjustments.
Customer themes the founder should hear
The qualitative thread that does not show up in numbers: what customers love, what they are confused by, what they are starting to ask for.
The compounding effect
Quarter on quarter, the system gets sharper, the product gets clearer, and the team handles less of the same problem twice.
Why Ravon
Three things we do that the alternatives do not.
We build, we do not resell.
The CX system we put in place is shaped around the company: its product, its tone, its customers, its channels. We are not configuring a single vendor's product against a checklist. The company gets a system that fits, owned by them, that we keep sharpening.
Realtime, not just async.
Most CX AI focuses on handling messages without humans. We build for the small team that is still on the front line. The realtime assistants make every message and every call faster, more accurate, and more on brand, even before the team grows.
We stay for the iteration.
The Iterate phase is the part most providers skip. Every quarter we read what customers are saying, feed it into the CX system, and feed it into the product roadmap. The CX function and the company keep getting sharper together, instead of drifting apart.
Why now
The CX function you build now is the one you live with.
Customer experience operations are sticky. Once a company stands up a contact centre setup, vendor stack, headcount, scripts, escalation paths, agent training, internal politics, that setup becomes very expensive to change. Most established CX operations were built before AI agents were credible. Bolting AI onto a contact centre that has already been optimised for human throughput rarely produces AI native economics or AI native experience.
Scale ups that build their CX function correctly the first time skip this transition entirely. The system is AI native from day one. The team is hired around it, not retrofitted to it. The knowledge base, escalation logic, tone, and tooling are designed for how AI and humans actually work together, and they keep moving as the AI itself moves.
Build it right once, with us, or build it the old way now and pay to rebuild it later.
How we work
Diagnosis before prescription.
Every engagement follows three phases — discovery and diagnostic, impact & scoping, and solution design with explicit checkpoints. See how we reduce delivery risk before you commit scope.
Start a discovery
Most engagements begin with a conversation about context.
We do not send a proposal before we understand the problem. Start by telling us about your decision context. We will identify the highest leverage intervention areas before any scope is agreed.