RRico AutoExecutive Cockpit

Tech Overview

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

Rico Auto Industries Limited · FY26 (Mar'26, actuals)
Leading Indian aluminium high-pressure die-casting auto-components maker
7,500 employees · 8+ plants & units · 12 export markets
Under the hood

One governed brain over
many disconnected systems.

No big-bang migration. The platform federates each division and plant 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
17,130
Records governed
2/10
Divisions on SAP grain
46%
Revenue at plant grain
Technical architecture

The governed stack — eight tiers, federated not centralized

Each division and plant 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
SAP S/4HANA · division ledgersMES / die-casting line bus & SCADAOrder & quoting systemsCRMHRIS · payrollBSE/NSE filings · news
Ingestion
adapters · lineage · SLA
Source adaptersCDC & batch loadsFreshness / SLA monitorLineage 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 predicatesPlant = 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 OpenAIAgentsweb-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 division & plant system'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.

⚙️Margin Expansion & Operational Excellence
Watches

machined/value-added mix, OEE & cost/yield savings vs plan

Grounds on
business_unit · brand_cohort · kpi · synergy_track
Acts in Margin Expansion & Operational Excellence
🔋EV & New Mobility
Watches

the EV/capex funnel & the new-mobility program book vs target

Grounds on
signal · ma_target · deal_economics · service_line
Acts in EV & New Mobility
📈Customer Diversification & Exports
Watches

Hero concentration, content cross-sell & export share

Grounds on
customer · cross_sell_site · vertical · region
Acts in Customer Diversification & Exports
💎Deleverage & Returns
Watches

net debt/EBITDA, working capital & covenant headroom

Grounds on
kpi · ar_aging · renewal · debt_tranche
Acts in Deleverage & Returns
The hard part

Two 360s that work before the data is clean

Some divisions and units aren't fully on the common SAP grain yet, so the unit→division mapping and plant-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
8 of 10divisions not yet plant-grain

Some units report at their own grain — so the unit → plant → division → segment → legal-entity rollup is partly missing or inferred.

How the platform bridges it
1
Resolve. AI maps each legacy unit / plant / leader code to one canonical org node.
2
Allocate + flag. Where a unit isn't mapped, revenue is disaggregated from its geography on learned drivers — and marked an estimate.
3
Reconcile. Allocated parts must foot back to the division total; breaks are surfaced, not hidden.
~46%grain coverage

of revenue already at true plant grain; the rest labelled & closing as divisions move onto SAP

📍Plant / Asset 360
Open →
The gap
6 unitsallocated or region-only

For units not yet on the common grain, machine, value-added-revenue and revenue detail isn't available at plant grain — so the plant twin would otherwise be blank.

How the platform bridges it
1
Estimate. Plant figures are modelled to ~85% coverage from geography totals and installed-machine signals.
2
Flag confidence. Every estimated plant carries an Actuals / Allocated / Region-only badge and a coverage %.
3
Flip to actuals. As each unit cuts over to SAP, its plant grain rises and estimates become ledger actuals.
~85%grain coverage

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

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

Reconciliation runs live on the data. The 1 known break below is the plant-grain gap surfaced on purpose — exactly what a CFO or auditor wants flagged, not buried.

Open Data Health →
⚠ flagged
Value-added = Σ plant value-added
13
tie to the cent