PPavionExecutive Cockpit

Tech Overview

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

Pavion · FY26 (modeled)
#4 SDM Top Systems Integrators (2025)
2,800 employees · 70+ US sites · 23 countries
Under the hood

One governed brain over
ten disconnected systems.

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
860,091
Records governed
5/10
Brands integrated
71%
Revenue at office 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
Brand ERPs — SAP · Sage · QuickBooksCPQ estimatorsMonitoring / PSIMCRMHRIS · payrollEDGAR · 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 predicatesOffice = 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.

🧩Integration
Watches

synergy capture, blockers & cutover by cohort

Grounds on
integration_workstream · blocker · synergy_track · brand_cohort
Acts in Integration
⚙️Synergy
Watches

pricing leakage & field-force productive time

Grounds on
quote_system · workforce · supplier
Acts in Synergy
🤝M&A
Watches

external signals & the funnel vs dry powder

Grounds on
signal · ma_target · deal_economics
Acts in M&A
👥Cross-sell
Watches

single-service whitespace & at-risk renewals

Grounds on
customer · cross_sell_site · renewal · contract
Acts in Cross-sell
The hard part

Two 360s that work before the data is clean

Some acquired brands haven't migrated, so the branch→org mapping and office-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
5 of 10brands not yet office-grain

Acquired brands report at their own grain — so the branch → office → brand → business-unit → legal-entity rollup is partly missing or inferred.

How the platform bridges it
1
Resolve. AI maps each legacy brand / office / leader code to one canonical org node.
2
Allocate + flag. Where a branch isn't mapped, revenue is disaggregated from its area on learned drivers — and marked an estimate.
3
Reconcile. Allocated parts must foot back to the brand total; breaks are surfaced, not hidden.
~71%grain coverage

of revenue already at true office grain; the rest labelled & closing as brands integrate

📍Site / Asset 360
Open →
The gap
16 officesallocated or region-only

For non-integrated offices the device, ARR and revenue detail isn't available at office grain — so the office twin would otherwise be blank.

How the platform bridges it
1
Estimate. Office figures are modelled to ~87% coverage from regional totals and installed-base signals.
2
Flag confidence. Every estimated office carries an Actuals / Allocated / Region-only badge and a coverage %.
3
Flip to actuals. As each brand cuts over, its offices' grain rises and estimates become ledger actuals.
~87%grain coverage

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

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

Reconciliation runs live on the data. The 2 known breaks below are the site-grain gap surfaced on purpose — exactly what a CFO or auditor wants flagged, not buried.

Open Data Health →
⚠ flagged
Monitored devices = Σ office devices
⚠ flagged
ARR = Σ office ARR
12
tie to the cent