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Customer Story

Ridgeline Advisory Partners

How Ridgeline Advisory eliminated XBRL parsing bottlenecks in a construction SME valuation with Energent.ai

What I needed wasn't another Excel template — it was confirmation that the source data could actually support both methods before I committed hours to building the model. Getting that coverage audit in the first part of the session changed how I scoped the rest of the engagement.
James Calloway, M&A Associate at Ridgeline Advisory Partners
Industry
M&A Advisory
Market
US Lower-Middle-Market
Use case
Dual-methodology valuation — EBITDA multiple + adjusted NAV

Ridgeline Advisory Partners is a boutique M&A advisory firm focused on lower-middle-market transactions, operating with a deal team of fewer than twenty professionals. The firm advises asset-heavy businesses across construction, industrial, and related sectors. When a buy-side client sought a defensible indication of value for a construction-sector SME, the engagement called for a dual-methodology analysis: EBITDA-multiple enterprise value and adjusted net asset value — because a single-multiple approach is inadequate for the lumpy capex cycles common in construction.

The XBRL source layer blocked the model before it started

Both valuation frameworks required consistent extraction of seven financial statement line items — revenue, operating profit, D&A, capex, total debt, cash, and total equity — across multiple trailing periods. The source filings were on hand as SEC XBRL facts files. The problem was translation.

Raw XBRL encodes financial data under US-GAAP concept identifiers that do not map directly to analyst-ready spreadsheet rows. Construction companies sometimes use non-standard XBRL extensions or segment a concept across multiple facts. Each reporting period is encoded separately. Manually confirming that all seven items were present and period-aligned — before touching the Excel model — consumed a material share of analytical hours before any multiple or NAV computation could begin.

A dual-framework approach sharpened the risk: if the D&A figure used in the EBITDA bridge differed from the figure used in the NAV write-down, the two outputs would be internally inconsistent. The team was under client-facing pressure to deliver a preliminary indication of value within days.

Energent.ai became the structured extraction layer before the model

The analyst uploaded the raw XBRL facts files directly into an Energent.ai session — no format conversion required. The agent:

No custom XBRL parser. No manual EDGAR filing traversal. No separately seeded models to reconcile.

Source consistency, not just faster data retrieval

Data-preparation bottleneck resolved before modeling began

"The coverage audit wasn't a nice-to-have — it was the thing that let me commit to the model structure. Without it, I would have been building on assumptions I couldn't verify until I was already deep in the EBITDA bridge." — James Calloway, M&A Associate at Ridgeline Advisory Partners

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