Agent-driven data profiling

Turn messy cost & pricing spreadsheets into a documented, trusted data asset

Beacon Data Prism runs a team of AI agents over the files you already have — discovering structure, explaining what each column means, scoring quality and telling you exactly what to fix first.

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$A beam of light refracting through a prism into columns of structured data$

Six capabilities, one workspace

Everything in the engagement scope, delivered as a platform your whole team can log into.

Autonomous discovery

Point an agent at an Excel or CSV file and every sheet, table and column is discovered and catalogued without manual mapping.

Semantic understanding

Agents infer what each column actually means in business terms — cost drivers, rates, identifiers, dates — not just its data type.

Automated quality checks

Deterministic rules plus agent review surface missing values, outliers, duplicates, inconsistent formats and possible personal data.

Natural-language queries

Ask the catalogue plain-English questions about structure, quality and priorities and get grounded, citable answers.

Dynamic documentation

Export a living Excel data profile — assets, column meanings, statistics and every finding with a suggested fix.

Multi-client workspaces

Each client gets an isolated workspace with its own team, roles and invitations. Nothing is shared between them.

How the process works

Twelve clear steps from your first project through reports and custom pivots to final questions about the completed analysis. Every stage is saved in the session so you can stop and resume.

1Create a project
2Create a session
3Upload files
4Categorise & map
5Exclusions
6Review & correct
7Transformation lineage
8Pricing analysis
9Charts & waterfall
10Reports
11Pivot report
12Ask the catalogue

Step 1

Create a project

Name the engagement for a client. Opening it takes you inside, where its sessions live.

Step 2

Create a session

Inside the project, start a working session — a subfolder that holds its own files and analysis. Leave and resume it any time.

Step 3

Upload files

Inside the session, add transaction, cost, quote, contract and master files in any common format. Each one is read and profiled as it arrives.

Step 4

Categorise & map

Confirm which requested data item every file answers, then match its columns to the fields the analysis needs.

Step 5

Exclusions

Leave out what should not count: intercompany, returns, non-sellable, international or named accounts, plus unused columns.

Step 6

Review & correct

Work the checklist: supply missing columns, answer open questions and clear quality issues, alone or in bulk.

Step 7

Transformation lineage

See the full audit trail for any file — how it was categorised, renamed, mapped and excluded — and export it.

Step 8

Pricing analysis

Per-product price trends, discount levels and cost-to-price ratios across the cleaned data.

Step 9

Charts & waterfall

The quote-to-margin waterfall plus supporting charts, filtered by region, customer and period.

Step 10

Reports

Review and export the analysis workbook and printable findings report before the final data exploration step.

Step 11

Pivot report

Build custom cross-tabs by dragging dimensions and measures, then download the underlying rows for further analysis.

Step 12

Ask the catalogue

Finish by asking plain-language questions across the session and get answers grounded in its uploaded files and completed analysis.

Ready to see your data clearly?

Create a workspace, invite your team and profile your first file in minutes.

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