Meet Argus, the AI Data Analyst who turns messy ESG files into structured data | Credibl
Incredibl Agents · Part 2 of 5

Meet Argus, the AI Data Analyst who turns messy files into structured ESG data

Messy in. Structured out. Utility bills, CSV dumps, meter photos: Argus extracts, cleans and maps every row to the right facility, month and KPI, and you preview and approve before anything commits.

MeticulousPatientDetail-obsessedFast

Handles utility bills, meter photos, CSV and XLSX exports and supplier files, across every facility and entity in scope.

Every ESG workflow starts with data, which is why Argus is the agent teams end up working with most. He is the Data Analyst inside Incredibl Agents, Credibl's team of 5 AI agents, and his job description is simple: turn messy into clean, structured, platform-ready data.

Argus is meticulous, patient and slightly nerdy. He loves a well-structured CSV. He says "let me check" more often than "I think." And he will surface the unit mismatch you missed on row 3,412.

From inbox chaos to structured data

Drop in PDFs, CSVs, XLSX files, scanned images or pasted text. Argus runs the full pipeline: AI-powered OCR extraction, deduplication, normalization, unit conversion and schema mapping into the Credibl ontology. Unlike agents that only advise, Argus actually executes the transformation, always behind a preview-and-approve gate. You see the proposed clean dataset, with a diff, before a single row commits.

Utility billsPDF · scannedCSV dumpsEXPORTSMeter photosJPG · PNG47 MESSY FILESARGUSEvery row mappedELEC_MAR.pdfsheet3_col_FIMG_2841.jpgYOU APPROVEdiff shown before commitEVERY TRANSFORM LOGGED

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47 messy files in, structured data out. Every row mapped, every transform logged, nothing committed without your approval.
Argus executes real data transformations. You approve every one of them before it lands.

What Argus actually does for you

Ingests anything

Upload, paste or scan. Argus extracts electricity consumption from a stack of utility bill PDFs and maps each reading to the right facility.

Cleans in plain language

Tell him “clean, combine and deduplicate this purchase-orders file” and he does it, logging what was duplicate, what was kept, and why.

Maps your schema

Your columns rarely match anyone’s ontology. Argus proposes the mapping, with a confidence level per column, and learns your conventions.

Automates the recurring

Monthly bills from the same vendor? Argus saves the transformation as a recurring import rule and runs it on schedule.

Schema mapping, shown, not hidden

The mapping step is where most data tools quietly get things wrong. Argus makes it explicit. Every proposed mapping comes with a confidence score, and you confirm before load:

YOUR COLUMNSCREDIBL SCHEMAkwh_usedelectricity_consumption98%site_codefacility_id96%bill_monthreporting_period94%charge_inrspend_amount91%

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Argus proposes every schema mapping with a confidence score. You confirm before load.

Inside the upload screen

Argus is in-module primary, so his real home is not a chat window. This is what he looks like where you actually meet him, right on the upload screen:

Data Engine · Bulk upload
utility_bills_march.zip · 47 PDFs processed · 12 facilities detected
Argus

Extraction complete. 47 readings mapped to 12 facilities. 2 need your attention: 1 bill is in MWh where your schema expects kWh (converted, please confirm), and site code BLR-W2 is not in your facility list.

Preview datasetView mapping logApprove and load

Where the job ends

Argus is the transformation layer, and only that. He prepares clean activity data for the calculation modules, but methodology questions belong to Cira, disclosure narratives belong to Quill, and evidence and assurance checks belong to Verity. That separation is deliberate: the agent who transforms your data is never the one grading it.

Argus owns

  • Ingestion, OCR and extraction
  • Cleaning, dedupe, unit conversion
  • Schema mapping to the Credibl ontology
  • Recurring import rules

Argus hands off

  • Emission factor choices, to Cira
  • Disclosure drafting, to Quill
  • Evidence and assurance flags, to Verity
  • Routing and memory, to EVA
Argus is the agent teams work with most, and the only one trusted to execute real transformations on your data. Every upload on the platform runs through him. If data is the foundation of your ESG program, Argus is the one pouring it.

What flows in, what flows out

No agent works alone. Here is exactly what Argus receives, from you, the platform and the other agents, and what he hands off once the work is done:

YouPlatformCiraVerity

Flows in

Your files: PDFs, CSVs, XLSX, scanned images, pasted data
Natural-language instructions: clean this, dedupe by facility-month
Facility schema, activity types and unit definitions from Configuration
Historical measurements, for reconciling new data against old

Flows out

Clean, mapped, structured datasets, loaded after your approval
Validated activity data, ready for the calculation modules Cira guides
Tagged documents in the DMS, linked to measurements for Verity’s evidence graph
The cleaned, consolidated file back, whenever you want it

Argus, module by module

The full map of where Argus shows up across the Credibl platform and what he does there:

Data Engine
Home turf. Argus owns the full ingestion pipeline: upload UX, AI-powered OCR, schema mapping, and reconciliation. Every upload on the platform runs through him, and he hands the cleaned, consolidated file back whenever you want it for your own records.
DMS
Tags and organizes every uploaded document, and links each one to the measurements it supports. Ask him to find the bill behind any reading.
Emissions, Water, Waste, Air
Validates the underlying activity data before it reaches a calculation: completeness, units, missing periods, conflicts with prior submissions.
Activity Metrics
Validates uploads of production output, FTEs and revenue for units, periods and completeness.
Custom KPI
Validates uploads against your user-defined KPI schemas, and tells you when a KPI has no feeding data stream.
Social & Governance
Validates uploads of qualitative and quantitative data, from training hours to board composition.
Supplier Assessments
Helps suppliers structure their own responses at upload time, and validates the quality of supplier-provided data.
Facility & My Tasks
Surfaces a data-freshness status on every facility page, and auto-creates tasks for stale or incomplete uploads.

Ask Argus anything

Real prompts Argus handles today, straight from the platform:

“Clean, combine and deduplicate this purchase-orders data”
“Extract electricity consumption from these utility bills”
“Standardize all country codes to ISO 3166-1 alpha-2”
“Set up a recurring import rule for monthly utility bills”
“Map the column kwh_used to our electricity schema”
“Find rows where the unit does not match the activity”
“Which of my facilities have not reported this quarter?”
“Find the bill that supports this reading”

Argus by the numbers

100%
of transformations behind a preview-and-approve gate
30
days of silence before a facility is flagged as stale
1
recurring rule replaces every repeat upload
Next in the series · Part 3 of 5 Cira, the GHG Agent How every emission factor and methodology choice gets grounded.

Your extended sustainability team is here.

5 specialist AI agents. 1 extended sustainability team. AI agents that clean, calculate, report and audit, with you in control. Argus is live inside the Credibl platform today.

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