Operational AI studio

We install AIin your operations.An operator who ships. Not a firm that recommends.

Diagnostic in 2 weeks. Implementation in 6 to 10 weeks. Shipped, transferred to your teams, documented.

The operator

Operations10 years building and rebuilding.
AI products10 applications shipped to production, built solo with AI.
BackgroundGraduate of CESI engineering school.
Track record
FormerOperations turnaround specialist.

Case studies

One person equipped with AI outpaces a team that isn't.

Three domains where we most often intervene. For each, the same logic: we arrive, we audit, we structure the process — then we automate what can be. The goal isn't to replace your teams. It's to refocus them on value while AI absorbs the repetition.

Case 01

Customer service.

The typical situation

In most mid-market companies we meet, customer service is left to itself. No contact codes to classify requests, no response templates, no written processes. Agents improvise, quality varies by person, handle times climb. A 20-agent team spends 60-70% of its time on repetitive tier-1 requests it has already handled a thousand times.

Our intervention

We start with an operational audit: request types, volumes, real SLAs, friction points. We install contact codes, write processes, produce the templates that didn't exist. Then we install the AI layer — automated ticketing via n8n connected to your helpdesk, LLM qualification of each incoming ticket, auto-response on tier-1 grounded in your validated processes and templates, smart escalation to a human agent — themselves LLM-assisted — on complex cases.

The outcome

40-60% deflection on tier-1 requests. First-response SLA divided by three. CSAT maintained or improved (consistent processes = less perceived variability). The 20-person team can drop to 10-12 agents, refocused on tier-2 and tier-3 — what they were hired for. Quality and productivity KPIs in place, audited monthly.

Return on investment

8–12×

Year-1 ROI

Starting point: a 20-agent service, loaded cost ~€45k/year, payroll ~€900k.
Effect: 40–60% deflection on tier-1 tickets — 8 to 10 FTEs freed, recurring savings of €360–450k/year.
Cost: Aure-lab engagement of €35–45k, diagnostic + implementation included.
Payback in under two months. The remaining team, refocused on tier-2 and tier-3, works better.

Engagement: €35–45k
Recurring savings: €360–450k/yr
Payback: ≈ 2 months

Range calibrated for a 15 to 30-agent team. Absolute gain scales with the size of the existing team.

Designer's desk — printed mockups, warm studio atmosphere

Case 02

Marketing & content.

The typical situation

Marketing is mostly outsourced to an external agency. Each visual, each product variant, each copy piece eats 3-5 days and a €1,500-3,000 round trip. Internal teams no longer produce — they manage the agency. Cycles are slow, editorial consistency is weak, and annual agency spend balloons without quarterly output rising.

Our intervention

Audit of recurring needs and mapping of produced formats. Install an internal generative stack your teams own: Claude for long-form copy, Gemini Nano Banana for fast visuals, Midjourney for editorial compositions. Brand-voice-locked prompt library. n8n automation for multi-format adaptation (LinkedIn, newsletter, landing, catalogue), editorial calendar, and internal validation routing.

The outcome

Brief-to-deliverable cycle divided by ten (hours, not days). Outsourced agency budget cut 60-80% on recurring operational content. Strong editorial consistency across channels, since the prompts lock brand voice. Marketing teams become producers again, not subcontractor-managers.

Return on investment

2–4×

Year-1 ROI

Starting point: €80k annual agency budget spent on recurring operational production.
Effect: 60–80% cut on that spend — €50–60k net savings from year 1.
Cost: Aure-lab engagement of €25–35k. The stack stays in place at zero marginal cost.
Compound effect: over 6× across 3 years, plus 10× faster production opening market tests that weren't possible before.

Engagement: €25–35k
Yr-1 savings: €50–60k
3-yr compound: > 6×

Range calibrated on an agency spend of €80-300k/year. Absolute gain depends on current spend — excluding unquantified quality and speed gains.

Editorial desk — leather portfolio, contract, brass lamp

Case 03

Admin, ops & HR.

The typical situation

Onboarding eats 6-10 hours of HR time per new hire: contract generation, sending and tracking the 30 admin documents, reminders, HRIS entry. Recurring questions (PTO, payroll, handbook) saturate the HR inbox. Contracts cycle through three reviews before signature. Monthly reporting ties up two people for three full days.

Our intervention

Audit of HR and administrative processes. Standardisation of templates for contracts, amendments, agreements, standard replies. AI agents that generate documents from structured data, review and file incoming documents, and answer recurring HR questions via an augmented FAQ wired to your reference data. Reporting automated via n8n from your HRIS, ERP, and CRM.

The outcome

Onboarding time divided by 3. Volume of repetitive HR questions reduced 70%. Monthly reporting delivered in half a day instead of three. The HR team refocuses on retention, talent development, and real-value topics — not generating standard documents.

Return on investment

1–2×

Yr-1 ROI — 3–5× in yr 2

Starting point: a 4-person HR team managing 200 employees, saturated by onboarding and repetitive questions.
Effect: 0.5 to 1 FTE freed — €25–50k/year saved or reallocated to real-value topics.
Cost: Aure-lab engagement of €25–40k.
Compound effect: year 1 deliberately conservative. The real leverage lands in years 2–3, when the stack absorbs growth at zero marginal cost.

Engagement: €25–40k
Capacity freed: 0.5–1 FTE
Yr 2–3 ROI: 3–5×

Year-1 range kept deliberately conservative — HR gains are diffuse. The real leverage comes in year 2-3, when the installed process scales with no marginal cost.

Principle

The lever isn't AI. It's the process, rethought.

Made consistent, then automated on the sequences that allow it. And it has to be installed in that order.

Who operates

Nicolas Chareille.

Founder, principal operator.

Ten years building and rebuilding operational teams across three countries, three contexts. The red thread: customer service. The one you stand up, the one you straighten out, the one you run across multiple sites.

Google trained me in KPI discipline. Dodax and Vindima proved the method holds — whether it's 25 agents across markets or a service to build from zero.

Four years at Google Ireland as Quality Engineering Lead on Machine Learning projects — twenty evaluators under my responsibility, an obsessive KPI culture, one simple lesson: measure, analyze, correct. I then spent three and a half years at Dodax importing that method into a European customer service that was falling apart. Ten agents in Vienna at the start, twenty-five across Vienna and Warsaw by the end. KPIs deployed from zero: tickets per hour, AHT, Quality Score on a scored rubric, "ticket closed under 24h" SLA, CPO (Contact Per Order) to analyze recurring patterns. Automated reporting via Google Data Studio, weekly rituals plus monthly 1:1s, an Employee Handbook. Not surface management — a written framework that makes teams autonomous.

Same logic applied at Groupe Perret (€255M revenue, 600 people) for two and a half years. Customer service built from zero for the e-commerce launch: HubSpot for tickets, Ringover for calls, two permanent hires, documented process. KPIs installed from the first month: customer satisfaction, volume of product records qualified per range, automated reporting via Digdash. In parallel, ownership of catalog qualification: 5,500 items qualified and deployed in six months with a mixed permanent-contractor team, bi-weekly reporting to the executive committee.

Graduate of CESI School of Engineering — Unit & Project Management. Today I'm applying that same discipline to AI products shipped in production. What I bring your teams isn't a method on a slide deck — it's ten years as an operator at the end of the line, who knows what can be automated, what should stay human, and how to bring teams along through the transition. Aure-lab works with French mid-market companies, 250 to 5,000 employees, remote first, on-site for scoping phases.

Proof

I'm not a developer. Here's what I've shipped to production.

If Aure-lab is asking you to learn how to ship with AI, what Aure-lab does has to hold up. These four products are live today. None came from a technical team — they were built solo, with an end-to-end AI stack, leveraging exactly the methods we transfer to you.

Proof

The proof isn't theoretical. It's online.

It works. You can click on it.

Method

Four steps. Deliverables at each.

No step ends on a PowerPoint. Each one produces a measurable deliverable and a transfer to your teams.

01
Operational diagnostic
2 weeks
Interviews with your key operators (CS, marketing, ops, HR). Mapping of friction points, volumes, SLAs, and hidden costs. Identification of AI use cases with short-ROI horizons.
Deliverable
Diagnostic report (15 to 20 pages). Visual process mapping. Prioritised 90-day AI roadmap ranked by impact and feasibility.
Client engagement
Operator access four to six hours per week. Anonymised operational data.
02
First use-case scoping
1 week
Joint selection of the first use case to implement. Detailed functional specifications. Technical architecture. Definition of quantified success criteria and guardrails.
Deliverable
Signed scoping document. Functional and technical specs. Detailed deployment plan with weekly milestones.
Client engagement
Sign-off from business leads. Access to a test environment and the necessary data.
03
Implementation and iteration
4 to 8 weeks
Production build, not a throwaway POC. Weekly iterations with your operators. Tests on real data. Integration with your existing systems (CRM, helpdesk, ERP, HRIS).
Deliverable
Solution in production. Technical and operational documentation. Regression test suites. Operator user guide.
Client engagement
One operational lead on the client side. Two to four hours per week during active phases.
04
Transfer and autonomy
2 weeks
Training of key operators and business leads. Documentation of new processes. Monitoring, alerting, and quality guardrails set up.
Deliverable
Training sessions delivered. Maintenance runbook. Twelve-month retention and continuous-improvement plan.
Client engagement
Active participation of trained operators. Naming of an internal owner for the follow-on.

Delivery

Every step ships a deliverable. No slides.

No eternal dependency. You leave with written processes, an installed stack, and trained teams.

Productized offers

Three entry points. Prices published. No ambiguous retainers.

AI Operations Diagnostic

2 weeks

€5,900

Two weeks. We map where you lose time, identify short-payback AI use cases, and deliver a prioritized 90-day roadmap.

Start a diagnostic

★

AI Workshop for your team

3 days

€7,500

Three days, on-site or remote. Your managers learn to deliver with AI what their outside agency charges ten times more to do. Concrete deliverables produced during the workshop.

Schedule a workshop

Implementation

6 to 10 weeks

starting at

€35,000

Six to ten weeks. We integrate AI into your helpdesk, CRM, or business workflow. Engagement through to production deployment and knowledge transfer to your team.

Scope a project

Frequently asked

Common objections, direct answers.

Next step

Let's talk for thirty minutes.

A first no-commitment conversation to qualify your need, identify a candidate use case, and decide together whether there's ground for working together.

Pick a slot

or write us directly

Case 01