Not a collection of disconnected automations. A repeatable system for discovering, quantifying, building, measuring and scaling AI initiatives that move revenue, gross margin, inventory economics and decision quality.
I step in as your AI Lead, bring an agentic AI engineer onto the team, and we aim the portfolio at €100,000+ in validated or annualised business value within 12 months.
Five steps that keep running after the program ends. The point is not how many AI projects ship — it is how much validated value the portfolio produces.
Find high-value opportunities across commercial, merchandising, inventory and operations — from the people who actually do the work.
Baseline the KPI and model the financial upside before a single hour of engineering capacity is committed.
Prototype in days. Productionise only the initiatives with enough evidence and strategic value to deserve it.
Experiments, control groups or before/after baselines, so impact is attributed rather than assumed.
Expand the winners, kill the weak bets early, and keep refilling the opportunity pipeline.
A starting menu, not a fixed scope. Discovery replaces these with the bets that fit your data, your margins and your team.
An intelligence layer for category managers that continuously reads product performance, margin, inventory, seasonality and customer behaviour, then recommends what to promote, bundle, discount, reposition or drop.
Gross margin · sell-through · AOV · conversion · inventory turns
Agents generate commercial hypotheses, prepare campaign, landing-page and offer variants, analyse results and propose the next tests. Humans still approve anything material that customers see.
Incremental gross profit · conversion rate · AOV · experiment velocity
Customer- and segment-specific offers built from purchase history, affinity, margin and stock constraints — dynamically constructed bundles and incentives, not a "recommended products" widget.
AOV · attach rate · repeat purchase · contribution margin
Continuously evaluates elasticity, competitor signals, margin, stock position and promotion history to recommend pricing and promotional action instead of relying on static rules and intuition.
Gross margin % · promo ROI · sell-through · markdown cost
Sharper SKU-level forecasting plus early detection of stock-out and overstock risk, translated into concrete purchasing, replenishment and promotion recommendations.
Stock-outs · excess inventory · working capital · lost margin
A governed decision layer over commercial and financial data that answers management questions, flags anomalies and explains what is actually moving revenue, margin, marketing efficiency and inventory.
Decision cycle time · forecast quality · speed to corrective action
Compresses the path from supplier data to a sellable SKU: enrichment, positioning, localisation, creative briefs, channel assets, SEO inputs and launch analysis.
Time-to-market · launch throughput · revenue from new SKUs
A reusable layer connecting customers, products, behaviour, campaigns, inventory and commercial outcomes, so every later agent reasons over shared business context.
Enabler: faster delivery and higher quality of everything built after it
Every initiative starts with a baseline, a financial hypothesis and an agreed measurement method. Revenue uplift and economic value are never treated as the same thing: wherever possible, revenue effects are converted to incremental gross profit or contribution margin.
| Value lever | How it is calculated |
|---|---|
| Conversion / AOV uplift | Incremental revenue × contribution margin − incremental variable costs |
| Pricing / margin | Affected revenue × change in gross-margin percentage points |
| Inventory | Reduced markdowns + avoided lost margin from stock-outs + carrying-cost benefit; working-capital release reported separately |
| New product velocity | Incremental contribution from earlier launches + avoided external or manual production cost |
| Productivity | Hours genuinely eliminated or redeployed × fully loaded hourly cost; soft time savings reported separately |
The figures below exist to show the economics, nothing more. During discovery they are replaced with your own baselines — and the targets move with them.
| Value stream | Illustrative logic | Annualised |
|---|---|---|
| Merchandising / bundles | €5m affected revenue × 0.6 pp contribution-margin improvement | €30k |
| Growth experiments | €4m affected revenue × 1.0% incremental revenue × 40% contribution margin | €16k |
| Pricing / promotions | €6m affected revenue × 0.5 pp gross-margin improvement | €30k |
| Inventory intelligence | Avoid €40k of markdown and lost-margin leakage | €40k |
| Illustrative annualised value | €116k | |
This is a portfolio ambition, not a guaranteed financial return.
€40,000+
6-month program, billed monthly + VAT
Budgeted separately: compensation for the agentic AI engineer, cloud, model and API usage, third-party software and data, media spend, and implementation capacity beyond the agreed team.
Book a CallStartup CTO from 2012 to 2017, so the software development lifecycle is not theory to me. 30+ AI projects in the last year across defense tech, trading, recruitment, e-commerce and non-profits. Three of my own AI products in production with paying customers — Calyflow, Coconector and Sellerita — which is where the agent patterns in this program come from. And 20 years in business, so the conversation about margin and process is not a translation exercise.
See the client workFrom €40,000 + VAT for the six-month program, billed monthly in arrears. If after the first month we agree not to continue, you do not pay for that month. Budgeted separately: compensation for the agentic AI engineer, cloud, model and API usage, third-party software and data, media spend, and implementation capacity beyond the agreed team.
No. It is a portfolio ambition, not a guaranteed financial return. What is committed is the method: every initiative starts with a baseline, a financial hypothesis and an agreed measurement method, and results are reported monthly whether they are good or bad. Weak bets are stopped early rather than defended.
A dedicated agentic AI engineer joins your team for the program, and I lead them. I set the standards, the architecture and the priorities, review the work, and stay hands-on where it matters. You get a lead and a builder, not a deck.
Approximately two dedicated days per week for six months, scheduled in advance, plus a weekly delivery session and a monthly executive portfolio review.
The initiative catalogue above is written for e-commerce and retail because that is where the sharpest margin levers are. The AI Value Engine itself — discover, quantify, build, measure, scale — is industry-agnostic and has been applied across defense tech, trading, recruitment and services.
A Big 4 AI strategy in this region typically runs €30,000–€80,000 for six months and ends with a document. This program ends with software in production, a measured value scorecard and a team that can keep going without me.
A 30-minute call is enough to tell whether a six-month program is the right shape for your company — or whether something smaller is.
Book a Call