Granular data infrastructure
Powering AI with facts.
Granular data infrastructure for banking, industrial, and public-sector compliance — built in Singapore, anchored in Asia.
Regulators are moving from aggregated reports to position-level granular data. AI deployments stall on the same gap. Evercomm builds the granular data layer underneath both — verifiable, audit-grade, AI-ready.
Building with
SMBCCTBCThai AirwaysMitsubishiJurong Port
01 / The granular shift
The granular shift is already underway.
Across banking, industrials, and public reporting, regulators are moving from aggregated annual disclosures to transactional, position-level data — AnaCredit in Europe, MAS 610 in Singapore, Bank of Thailand granular returns, PCAF v2025 financed emissions, CSRD/ESRS digital tagging.
Stage 1
Aggregated
Annual disclosures rolled up at portfolio or facility level. Defensible until ~2023. No longer sufficient.
Stage 2
Basic granular
Major instruments (loans, large facilities) reported individually with core attributes. Foundational.
Stage 3
Wide granular
Derivatives, securities, and supplier-level data added to the granular base. Most APAC regulators arriving here 2026–28.
Stage 4
Full granular
Every transaction, every meter reading, every emission factor — lineage-tracked, AI-ready, audit-ready.
The reason is the same everywhere: aggregated reports hide risk, and they cannot feed AI. Granular data exposes both. Evercomm is built for the world after the shift.
02 / Powering AI with facts
AI is only as good as the facts underneath it.
Models trained on annualised, aggregated reports don't generalise to operational decisions, and they hallucinate where the data is thin. AI in regulated sectors needs the row beneath the row — every transaction, every meter reading, every loan, every emission factor — timestamped, tagged, lineage-tracked, verifiable to a standard.
The Nx-Engine builds that layer. Every figure Evercomm produces is traceable to a source record. Every regulatory report is a query against the same granular base. Every AI agent on top operates on facts the auditor will also see.
Outcome 1
Compliance becomes a query.
Annual reporting projects collapse into queries against the granular base. New regimes (CBAM, ESRS, MAS 610) ship as rule packs, not as new data pipelines.
Outcome 2
AI compounds instead of stalling.
The data quality problem that kills 80% of enterprise AI pilots stops being a problem. Models, agents, and analyses all run against the same lineage-tracked base.
Outcome 3
Decisions use auditor-grade evidence.
Operations, risk, finance, and sustainability teams stop arguing over which dataset is correct. There's one base. The auditor sees the same numbers the CEO does.
Outcome 4
One foundation, every regime.
PCAF, MAS 610, ISSB, ESRS, CBAM, SBTi — same granular base, different rule packs. Build the data layer once, report against it everywhere.
03 / Three sectors
One granular data infrastructure. Three sectors deploying it.
Banking, industrial, and public sector all sit on the same Nx-Engine foundation. The use cases differ; the data discipline is identical.
01 / Banking
Position-level financed emissions. Audit-ready. AI-ready.
PCAF v2025 · MAS 610 · ISSB S2
Building with SMBC as the launch initiative. CTBC in active build for Q3. Partnership with leading APAC SupTech providers going live alongside the ABS granular data reporting initiative.
See the banking platform →02 / Industrial
One granular base. Every report. Every model.
CSRD · CBAM · ISSB · SBTi · ISO 14064
Thai Airways, Mitsubishi Electric, Jurong Port, KTIS, Punggol. Sensor, meter, and ERP data ingested at source, structured to ISO 14064, and used for both compliance reporting and operational AI.
See the industrial platform →03 / Public sector
Accountable, AI-ready public reporting.
Launching 2026 · Design partners open
Same granular data infrastructure, adapted for government, healthcare, education, and major NGOs. Data sovereignty, mission-aligned metrics, multi-stakeholder access, funder-grade reporting.
Apply to design partner program →04 / The platform
One granular data infrastructure. Three working layers. One engine.
Products are layers of the same infrastructure, not separate tools. The Nx-Engine sits underneath all of them as the granular data engine that powers AI with facts.
| Layer | Product | What it does |
|---|---|---|
| Ingest & structure | NXOps | Connects sensors, meters, ERPs, core banking, custody, supervisory feeds. Outputs granular, ISO-grade, lineage-tracked data. |
| Measure & verify | NXMap | The compliance and financed-emissions inventory. Position-level, audit-ready, multi-standard (PCAF, ISSB, ESRS, GRI, ISO 14064). |
| Simulate & decide | NXPlan | Scenario simulation and reduction-strategy planning on top of granular data. Where AI on operational reality begins. |
05 / Proof
Partners. Standards. Recognition.
Operating with named partners, against named standards, against named regulators. Specificity is the proof.
Partners & strategic relationships
SMBC initiative lead · Q3
CTBC active build · Q3
ABS & Regnology Q3 reveal
Mitsubishi Electric
Shell Energy
BlueOnion · Bureau Veritas
Standards we operate to
PCAF
v2025
MAS 610
Singapore
ISSB
S1 · S2
ESRS
E1–E5
ISO 14064
67 · 68
SBTi
target setting
CBAM
EU
ISSA 5000
assurance
Recognition
B Corp Certified
PCAF Member 2024
COP28 partner
APEX Singapore 2025
Holon Foundation
TGO Thailand recognition
06 / Asia-anchored
Built in Singapore. Anchored in Asia. Built for the world after the shift.
Most granular-data infrastructure is built around European and US regulators. Evercomm is built around APAC's first — MAS, ABS, Bank of Thailand, TGO, JFSA-aligned ISSB adoption, and the China and India CSRD-equivalent waves arriving 2026–2028 — and extends naturally to CSRD, CBAM, and US SEC regimes the same customers also report into.
Since 2013, from Singapore. Now operating across banking, industrial, and public sector.
Talk to us.
Whether you're a regulator designing a granular reporting regime, a bank starting your PCAF v2025 transition, an industrial preparing for CSRD, or a public-sector organisation thinking about AI-ready impact data — there's a conversation to have.