EXPERTISE · AI
The AI that reaches production.
Eighty per cent of AI POCs never reach production. Not for lack of ambition. For want of architecture, governance and business grounding. We design the AI use cases — Voice AI and generative AI — that integrate with your existing CCaaS and produce measurable KPIs.
THE CONTEXT
The paradox of the POC.
In CX AI, large organisations face a paradox. Board pressure to 'do AI' is intense. Budgets unlock quickly. But eighty per cent of POCs never reach production.
Hallucinations on sensitive responses. Underestimated IS integration. Absent governance. Unclear ROI. CX teams inherit poorly designed AI tools that degrade NPS instead of improving it.
We put AI back in service of the customer journey. Use case by use case. Not platform by platform.
OUR ANGLE
A CX consultant who speaks data science.
I am a CX architect who learned data science, not a data scientist who has discovered a curiosity for AI. The difference is substantial: I know what happens after the POC.
CCaaS architect for 18 years — Genesys Cloud, Amazon Connect, Avaya — with the associated certifications. Data Analyst RNCP Level 7 (DataScientest / École des Mines Paris, 2025). Practical operational stack: Python, n8n, GPT-4o, ElevenLabs, Supabase, Twilio.
In practice: I design AI use cases that integrate with your existing estate, reach production, and produce measurable KPIs. Not a PowerPoint demo that ends up in a drawer after six months.
OUR METHODOLOGY
Four phases. To clear the POC.
Each phase produces a standalone deliverable. The method prioritises secure passage to production over demonstration effect.
01 · FRAME
Audit & scoring
Audit of candidate use cases, value/feasibility scoring, selection of the one or two priorities. Everything begins from a choice defensible in committee.
02 · ARCHITECT
AI design & governance
Target AI architecture, model selection (LLM, ASR, TTS), IS integration, data governance framework. Governance is not a final deliverable — it is a prerequisite.
03 · SECURED POC
Validation before industrialisation
Supervised development, testing on a controlled scope, measurement of hallucinations, latency and errors. The POC is a measured experiment, not a commercial demonstration.
04 · INDUSTRIALISE
Progressive deployment & monitoring
Deployment by scope, continuous monitoring, team training, optimisation plan. Industrialisation is measured over time.
WHAT YOU RECEIVE
Six defensible artefacts.
Every mission produces deliverables retained by your teams — not one-use slides.
- AI use-case scoring matrix (value / feasibility)
- Target AI architecture (models, flows, API integrations)
- Data governance and security framework
- Working POC + performance report (correct-response rate, hallucinations, latency)
- Industrialisation roadmap
- AI steering dashboard (automation rate, satisfaction, human escalations)
ON ENGAGEMENT
Voice receptionist — premium service SME.
THE CONTEXT
Dental practice, three practitioners. Thirty per cent of calls lost during peak hours. Saturated diary, manual appointment booking, patient friction at every telephone interaction.
OUR ENGAGEMENT
Deployment of an autonomous multichannel agent operating 24/7, capable of handling voice, email, WhatsApp, Telegram, WebChat and web form according to the client's chosen channel. Qualification, appointment booking, follow-up. Secure architecture, GDPR-compliant, hosted in France.
THE RESULT (over 90 days)
MEASURED GAINS
Three axes of ROI.
AUTOMATION RATE
Thirty to fifty per cent of interactions resolved without human intervention on the automated scope.
COST PER INTERACTION
Cost per interaction reduced by 30 to 40% on the automated scope, measured in live operations.
TIME TO RESOLUTION
Resolution time reduced by 25 to 40% via the agent assistant, without NPS degradation — which is the primary watchpoint.
TO GO FURTHER
You have an AI use case struggling to industrialise?
Six packaged audits cover the full journey, from the Flash Diagnostic to the 360° Assessment. For larger programmes, mission on quotation.