E‑Commerce.
A Vienna e-commerce retailer with 23 people and ~€5.4M annual revenue. API-less legacy systems integrated through a single definitional layer.
A Vienna-based mid-size e-commerce retailer with 23 people, thousands of active SKUs, and ~€5.4M annual revenue. Four definitional gaps identified across three API-less legacy systems and a manual reporting layer. Stathon deployed at Forge Tier with all four modules active. Within nine months: stockout events reduced by 34%, 1,500–2,000 operational hours per month recovered, decision cycle collapsed from days to under 60 seconds, and compound annual financial impact reached ~€650K.
The retailer.
The client is a mid-size Austrian e-commerce retailer operating out of Vienna. Having traded for nearly a decade, the organisation had grown to manage thousands of active SKUs across multiple product categories, with a team of 23 people and annual revenue approaching €5.4 million.
By mid-2025, the operational infrastructure had not kept pace with the commercial growth. Order management, inventory tracking, supplier coordination, and customer data each lived in entirely isolated systems that had never shared a common language. None exposed a programmable interface.
Reporting was manual. Decisions were reactive. Even the most current data available to the leadership team was routinely days behind operational reality. The organisation lacked the interpretive layer required to compose its own data into a single, accurate representation of what was actually happening.
Operational fragmentation.
2.1 Legacy system landscape
The central transactional record for orders and supplier management. No API surface exposed. Data was accessible only through scheduled exports or manual screen-level extraction.
Managed physical stock levels and pick-and-pack workflows. Operated in complete isolation from the ERP. Inventory figures were reconciled manually on a weekly cadence at best.
Product listings, customer sessions, and order intake. Availability logic had no live awareness of warehouse stock states. Product availability was updated manually, with a lag of 24–48 hours.
Reporting was produced by a single analyst combining exports from all three systems. The most current picture available to leadership was three to five days behind operational reality.
The three systems and manual reporting layer together formed an organisation whose data was present everywhere — but nowhere was there a definitional link between them. There was no established record of what the same operational concepts meant in relation to one another.
The ERP, warehouse management system, and e-commerce front-end each applied different criteria to identify a product variant. As a result, inventory counts, SKU availability, and sales attribution differed across every system.
There was no consistent definition of what constituted a confirmed, processing, fulfilled, or returned order. Each system tracked a subset of the lifecycle independently, producing irreconcilable order status discrepancies.
Purchase orders, lead time commitments, and delivery confirmations were tracked manually. There was no machine-interpretable definition of what a supplier obligation meant or when it was considered met, delayed, or failed.
Guest checkouts, registered accounts, and repeat purchasers existed as separate records with no canonical identity resolution. Customer lifetime value and churn signals could not be computed reliably.
2.2 Pre-deployment diagnostic findings
Stockout events were occurring at a rate that suppressed approximately 8–12% of potential demand. Without a real-time inventory state, the e-commerce platform could not surface accurate availability, and reorder signals arrived too late.
The leadership team was making commercial decisions on data that was 3–5 days old. Pricing, inventory allocation, and promotional timing decisions were all operating behind operational reality.
An estimated 1,500–2,000 hours per month were consumed by manual data extraction, cross-system reconciliation, and report assembly. This was not a structural necessity — it was a consequence of the absence of a definitional integration layer.
Without a coherent, continuous, and structured data layer, AI-driven predictive capabilities cannot be built. The organisation was excluded from forecasting, demand modelling, and churn prediction for as long as the foundational layer did not exist.
The client had previously engaged multiple technology partners — from systems integrators to software development agencies to enterprise consultants. The conclusion in every case was the same: connecting the legacy systems, unifying data in real time, and introducing automated decision support in this environment was not possible.
Four modules.
The organisation engaged at Forge Tier with all four modules active. The infrastructure was not built on top of the existing software — it was positioned beneath it, replacing the interpretive layer that no one had previously constructed.
Arch\u00e9 \u2014 Definitional layer
The Arché phase established the definitive description of the retailer's complete operational logic — before any automation or intelligence was built on top. This was not data modelling. This was ontological decision-making: what does a “product variant”, an “order state”, a “supplier obligation”, a “customer” mean to this organisation — across systems that had never agreed on these concepts before.
The Arché phase took approximately 10 weeks. The majority of the work was not technical implementation but structured decision-making: the commercial and operations leadership jointly defined the organisation's canonical conceptual framework. Every downstream capability — integration, AI modelling, compliance governance — is grounded in what was established here.
Core \u0026 Athena \u2014 Continuity and intelligence
Following Arché layer completion, Core and Athena activated in parallel. In the absence of API surfaces, Core deployed structured extraction processes against each legacy system. Schema normalisation, deduplication, and temporal sequencing were applied to reconstruct a continuous operational record from fragmented, static data stores.
Aegis \u2014 Sovereignty and compliance
The Aegis phase established the GDPR compliance architecture and data sovereignty infrastructure — grounded in the Arché layer entity definitions and the data flow inventory generated by Core. Every customer data lifecycle is now traceable, auditable, and defensible under Austrian and EU regulatory requirements.
After 9 months.
The following results are based on 9 months of operational data measured from the Q1 2026 reporting period. All figures originate from the live deployment environment.
Manual data reconciliation, hand-compiled reporting, and system-to-system manual transfers have been eliminated entirely. The analyst role previously dedicated to producing reports now operates at a strategic level. No staff were displaced — the capacity was redirected to commercial analysis and growth activities.
The ~€650K compound annual impact aggregates across three areas: recovered operational capacity (~€54K per month), avoided revenue loss from the 34% reduction in stockout events, and margin protection through real-time anomaly flagging before structural erosion occurs.
What we learned.
API absence is a symptom, not the constraint
The absence of API surfaces in the legacy systems was repeatedly cited by prior technology partners as the barrier to integration. It was not. The real constraint was the absence of definitions. Without a machine-interpretable specification of what a product variant, an order state, or a customer identity means across systems, no integration layer can produce intelligence — regardless of what protocols are available. The API-less environment required novel extraction methods, but those were tractable engineering problems. The definitional absence was the structural one.
Predictive capability requires definitional infrastructure first
The organisation had attempted to introduce forecasting tools in prior years. Each initiative failed at the data preparation stage: there was no coherent, continuous signal to train models on. Once Arché established the domain ontology and Core produced a temporally consistent operational record, Athena’s demand forecasting model was operational within six weeks. The AI capability did not require new data — it required the existing data to be interpreted correctly for the first time.
Operational decisions shift from reactive to predictive
The leadership team was previously making pricing, inventory, and promotional decisions on data that was days old. After deployment, the organisation sees what is about to happen — not only what has already occurred. Reorder signals arrive before stockouts materialise. Churn signals arrive before customers disengage. Margin anomalies surface before they compound into structural erosion. The decision logic itself has changed, not merely the speed at which legacy decisions are made.
Infrastructure position, not product position
The deployed system is not an integration layer, not middleware, and not a reporting framework. It is the structural intelligence foundation on which the organisation’s operational reality now exists in machine-interpretable form — for the first time in the organisation’s history. The legacy systems continue to operate unchanged. What changed is what their data means — and what can be built on top of that meaning.
Next iterations.
Dynamic price optimisation
Athena will extend into competitive pricing intelligence: model-driven margin protection and positioning recommendations on a per-SKU, per-channel basis. The Arché product variant taxonomy and Core pricing event stream provide the structural foundation.
Basket value expansion models
Predictive cross-sell and upsell recommendations based on real-time customer session context and historical purchase patterns. Signals routed from Athena into the e-commerce front-end through the Core propagation layer.
Fully automated reorder triggers
Athena-driven reorder signals will close the loop into automated purchase order generation. The supplier obligation state machine defined by Arché ensures every automated order is traceable and auditable from inception.
Supplier risk intelligence
Supply chain fragility forecasting using Athena’s supplier performance scoring layer. Proactive generation of alternative supplier recommendations before lead time failures materialise.
This Field Report is based on documented operational data. The client name and identifying details have been anonymized at the organisation's request.
Financial and operational figures were measured within nine months of deployment, based on Q1 2026 data. Results are to be understood within the specific organisational context of this engagement.