Automate your customer support.
An AI agent that handles 80% of tier-1 questions, frees your team from repetitive tickets, and stays plugged into your internal documentation in real time.
What's broken.
Customer support is the function that scales the worst as a company grows. The more users you ship to, the more your support team drowns in repetitive questions. Most off-the-shelf solutions stack static FAQs or scripted bots that frustrate users and end up escalating to humans every time. Here's what we keep seeing on the ground.
- 01
Tier-1 eats all the time available
70 to 80% of tickets are tier-1 questions (where to find the invoice, how to reset a password, how to enable a feature). Agents spend their days repeating themselves, and strategic topics (retention, upsell, product escalation) get neglected.
- 02
Scripted chatbots frustrate users
Decision trees are too rigid. The moment a question falls outside the planned branches, the user goes in circles and falls back to a human. Scripted chatbot satisfaction sits around 30%, worse than no bot at all.
- 03
FAQs go stale faster than you can write them
Product teams ship continuously, the FAQ gets written by support on the side. Within six months, 30% of entries are obsolete. No one has time to maintain them, and users find wrong answers.
- 04
24/7 human coverage costs too much
Round-the-clock human support requires at least three people in rotation. For low overnight volumes, the cost-to-value ratio is laughable. You either leave users without an answer or pay for a team that sits idle.
- 05
GDPR rules out most off-the-shelf tools
Intercom AI, Zendesk AI, Drift and friends ship conversations to non-EU servers. For sensitive verticals (healthcare, finance, public sector, HR) or for compliance-heavy mid-market, it's a deal-breaker. And making a third-party platform compliant is rarely an option.
How we do it.
We don't ship a chatbot, we ship a system that learns from your support and scales with it. Here are the principles we hold to.
Plugged into your docs, not into rules
RAG architecture. All your product documentation, help articles, and internal procedures are indexed in a Vector Store. The agent retrieves the answer in real time and cites its sources. When your docs evolve, the agent evolves with them automatically.
Knows how to say "I don't know"
The agent has an explicit awareness of the limits of its knowledge. When a question falls outside its scope, it escalates to a human with a clean summary of the context. No invented answers, no users stuck in a loop.
Learns from usage, not on its own
Every failed conversation feeds a dashboard for the support team. The team improves the docs, the agent doesn't retrain itself in a corner. Governance stays 100% human, and the docs grow as a living asset.
Sovereign by default
You choose. Local LLM on on-premise GPU, sovereign cloud (SecNumCloud, OVH, Bleu, S3NS), or public cloud depending on your GDPR constraints. We've shipped all three options and we know the trade-offs.
Integrated into your existing stack
Zendesk, Intercom, Crisp, Front, Slack, Teams, or your own back-office. We plug into what you use. No third-party platform to learn, no forced migration.
Measurable from day one
Resolution rate without human, satisfaction per ticket category, response latency, escalation rate, hallucination rate. You drive ROI with numbers, not feelings.
Shipped on a real mission.
For Peps Digital, a SaaS platform for home healthcare providers, we shipped a RAG chatbot plugged into all the product documentation. Today, 80% of support requests are handled without human intervention. The support team has freed itself to focus on product onboarding and strategic accounts.
80% of customer support digitized
A RAG-powered AI chatbot integrated into the Peps Digital platform, answering PSDM users' questions directly from the interface, 24/7.
Our process.
Audit your support
We read your tickets from the past 3 to 6 months, identify the 20% of questions that drive 80% of the volume, and map your documentation sources. You walk out with a clear view of the automation potential and a precise estimate.
Design the agent
Model selection (cloud, sovereign cloud, or local) based on your GDPR constraints. RAG architecture: chunking, embeddings, reranking. Escalation rules to human. Guardrails against hallucinations.
Pilot on a restricted channel
First deployment on a limited channel (web FAQ, in-app technical support, or a defined ticket segment). One week of observation with your support team, targeted adjustments, calibration of escalation thresholds.
Industrialize and hand over
Rollout to all relevant channels, full instrumentation (dashboards, alerts, runbooks), gradual transfer of maintenance to your team. We stay reachable for the long run, but operational ownership goes back to you.
Frequently asked questions.
Got a question before we go further? Reach out directly.
01How long to ship a support agent in production?
Plan for 4 to 6 weeks for a full deployment. One week of audit, two weeks of design and development, two weeks of pilot, one week of industrialization. Faster if the docs are already well-structured and the target channel is well-defined.
02What resolution rate without human can we expect?
On comparable missions we sit between 70 and 85% of tier-1 volume. The deciding factor is not the AI model, it's the quality of your documentation as input. That's exactly why the audit phase is critical.
03Do the docs need to be clean before we start?
No, and they rarely are. The audit phase reveals doc gaps and ambiguities. The project is as much a documentation quality project as it is an AI project. We hand you a prioritized checklist of gaps to close in parallel.
04How do you handle GDPR compliance?
Several options depending on your constraints. Local LLM on on-premise GPU for maximum sovereignty. Sovereign cloud certified SecNumCloud (OVH Bleu, S3NS) for compliance-heavy mid-market. Public cloud (OpenAI, Anthropic) with EU data residency for standard cases. No data leaves the EU if that's your requirement.
05Which channels can the agent plug into?
Any channel that goes through text. Web chat, Intercom, Zendesk, Crisp, Front, Slack, Teams, in-app. Voice and email are also possible, we just add a transcription or parsing connector.
06What's the run cost after going live?
It varies with volume and model. With a public cloud like OpenAI or Anthropic, plan for 0.01 to 0.05 euro per question handled. With a local LLM, marginal cost tends to zero but GPU infra has a fixed cost. We model both scenarios on your real volume during the audit.
07What happens if the agent says nonsense?
It's our obsession. Three guardrails: systematic citation of doc sources used, rule-based validation for numerical and critical data (amounts, dates, references), escalation to human at the slightest doubt. Hallucination rate is instrumented from the pilot, and we sit under 1% in production on most missions.
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Ready to
automate everything
We listen. We analyze. We build. With you.