SStarkExecutive Cockpit

Tech Overview

The architecture under the cockpit — how data flows from a house of disconnected systems into one governed truth, how that truth becomes a decision, and how two 360s still deliver over real data gaps.

The Stark Group · FY26 (modeled)
Luxury to-the-trade — designers & architects only (no retail)
670 employees · 12+ US sites · 8 countries
Under the hood

One governed brain over
a house of brands.

No big-bang migration. The platform federates each acquired system, resolves it to one ontology, and serves a single trusted number — then turns that number into a decision, and the decision into an owner's action.

10/12
Sources fresh
96,712
Records governed
5/8
Houses integrated
72%
Revenue at showroom grain
Technical architecture

The governed stack — eight tiers, federated not centralized

Each acquired system stays where it is. The platform layers ingestion, master-data resolution, a shared ontology and a semantic layer on top, then serves one governed truth to the apps and AI. Data flows top → bottom.

Sources
12 systems of record
ERP / order managementShowroom POSDesigner CRMPIM — product catalogHRIS · payrollDesign press · registries
Ingestion
adapters · lineage · SLA
Source adaptersCDC & batch loadsFreshness / SLA checksLineage capture
Store
raw → curated
Raw landing zoneCurated storeVersioned snapshots
MDM · Resolution
many codes → one node
Entity resolutionGolden recordsSurvivorship rulesDedup · term-conflict
Ontology
the knowledge graph
T-Box · 10 classesA-Box · instancesTyped predicatesShowroom = keystone
Semantic
defined once, federated
Metric definitionsGrain tagsFederation engineAllocation + confidence
Serving
governed access
Governed metrics APIQuery layerReconciliation tests
Consumption
apps + intelligence
360 views · Next.jsExec briefs · deterministicAzure OpenAIAgentsddgs web-grounding
Data flow

How one record travels — source to served truth

A single transaction's journey through the stack. A confidence flag and a reconciliation tie-out ride along with it the whole way.

1
Extract

Adapters pull each brand's events on schedule / CDC.

2
Land

Raw records stored verbatim, with lineage + timestamp.

🧩
3
Resolve

Codes matched to one canonical entity.

🧬
4
Model

Mapped onto the ontology — classes & relationships.

📐
5
Define

Native fields → governed metrics; estimates flagged.

🔌
6
Serve

One metrics API; reconciliation gates the numbers.

🔭
7
Consume

360 views, exec briefs & AI read one truth.

🏷 A confidence flag (Actuals / Allocated / Region-only) and a reconciliation tie-out travel with every value — so a number is never shown without knowing how bankable it is.
From data to action · the decision flow

How one number becomes a decision

The governed truth doesn't sit in a warehouse — it routes itself to the right view, the right action, and the right owner.

The agentic layer

An agent on every value pillar

The four value-creation pillars don't just have dashboards — each has a standing agent that reads its governed data products and recommends the next move. Same ground truth, automated.

🧵Craft & Heritage
Watches

design-market trends, first-quality yield & custom mix

Grounds on
signal · ops_metric · business_unit · goal
Acts in Craft & Heritage
🤝Portfolio & M&A
Watches

acquisition value capture & cross-house attach

Grounds on
brand_cohort · ma_target · deal_economics · cross_sell_site
Acts in Portfolio & M&A
📈Showroom & Trade
Watches

showroom pipeline, whitespace & account retention

Grounds on
customer · pipeline_stage · renewal · contract_record
Acts in Showroom & Trade
⚙️Margin & Delivery
Watches

on-time craft delivery, lead times & fibre costs

Grounds on
ops_metric · supplier · ar_aging · kpi
Acts in Margin & Delivery
The hard part

Two 360s that work before the data is clean

Some acquired houses haven't migrated, so the brand→group mapping and showroom-grain detail are incomplete. These views still answer — by resolving, allocating-and-flagging, then reconciling. The estimate is labelled, never hidden.

🗂Org Roll-up 360
Open →
The gap
3 of 8houses not yet showroom-grain

Acquired houses report at their own grain — so the showroom → brand → product-house → legal-entity rollup is partly missing or inferred.

How the platform bridges it
1
Resolve. AI maps each legacy brand / showroom / leader code to one canonical org node.
2
Allocate + flag. Where a showroom isn't mapped, revenue is disaggregated from its region on learned drivers — and marked an estimate.
3
Reconcile. Allocated parts must foot back to the house total; breaks are surfaced, not hidden.
~72%grain coverage

of revenue already at true showroom grain; the rest labelled & closing as houses integrate

📍Showroom 360
Open →
The gap
5 showroomsallocated or region-only

For non-integrated showrooms the order, repeat-designer and revenue detail isn't available at showroom grain — so the showroom twin would otherwise be blank.

How the platform bridges it
1
Estimate. Showroom figures are modelled to ~88% coverage from regional totals and order-book signals.
2
Flag confidence. Every estimated showroom carries an Actuals / Allocated / Region-only badge and a coverage %.
3
Flip to actuals. As each house cuts over, its showrooms' grain rises and estimates become ledger actuals.
~88%grain coverage

avg showroom-grain coverage today — transparent where it's modelled

The harness catches the gaps
11 of 14 governed identities tie out to the cent

Reconciliation runs live on the data. The 3 known breaks below are the showroom-grain gap surfaced on purpose — exactly what a CFO or auditor wants flagged, not buried.

Open Data Health →
⚠ flagged
Initiative pricing identity
⚠ flagged
Orders in production = Σ showroom orders
⚠ flagged
Repeat revenue = Σ showroom repeat revenue
11
tie to the cent