Manufacturing Finance Intelligence: Every Number Traced to Source

Key Takeaways

• Manufacturing finance teams spend 70% of their time finding data, not using it.
• Aperture connects ERPs, MES logs, PDFs and spreadsheets into one source-backed ontology graph.
• Month-end close compresses from 3–5 days to hours; COGS variance from 5 weeks to minutes.
• Every output cites document, page, section and cell — audit-ready by construction.
• 3-stage hybrid retrieval improves precision ~19% over naive RAG (Thakur et al., NeurIPS 2021).

The Problem: Finance Teams Run on Broken Data

Today, finance lives in silos. ERP exports go into one folder. MES logs go into another. PDFs and emails pile up on shared drives. Each handoff strips context. Each export breaks the link to the source.

The result is the classic 5-week variance cycle. By the time the report lands, the decision is already gone. Controllers spend nights on manual reconciliation. CFOs sign off on numbers no one can fully trace.

Manufacturing finance teams spend 70% of their time finding data rather than acting on it.

• According to Aperture customer research (2026), the median COGS variance cycle in mid-market manufacturers runs about 5 weeks from period close to root-cause sign-off.
• According to Ventana Research (Office of Finance benchmark, 2023), mid-market finance teams take 3–5 days to complete a manual month-end close.
• According to the McKinsey Global Institute (2012), knowledge workers spend about 9 hours a week — 28% of their time — searching for and gathering information.
• According to Panko ("What We Know About Spreadsheet Errors", rev. 2008), 88% of operational spreadsheets contain at least one material error — a key reason source-cited reporting matters in finance (overview of the literature).

Our Mission: From Raw Data to Sourced Decisions

Aperture turns raw documents into structured, sourced answers. We start with an ontology built for manufacturing finance. It ships with the entities your team already uses every day:

• BOMs, suppliers, components and equipment.
• Cost centres, contracts and compliance documents.
• Periods, versions and approval states.

Every new file you connect snaps into this graph. Over time, the graph becomes your team's shared memory — searchable, auditable and always linked back to source.

How Aperture Works: Connect, Retrieve, Answer

Aperture works in three stages. Each one is built to keep the link to the source intact.

• Stage 1 — Connect. Drop in any source: ERP exports, Excel, PDFs, MES logs, scans. No schema work needed.
• Stage 2 — Retrieve. A 3-stage hybrid engine (keyword → deep AI → cross-encoder reranking). According to Thakur et al. (NeurIPS 2021), cross-encoder reranking improves retrieval precision by ~19% over naive RAG on the BEIR benchmark (see also the original RAG paper, Lewis et al., NeurIPS 2020).
• Stage 3 — Answer. Every output cites document, page, section and excerpt. Numbers are checked at the token level. If a value can't be verified, it's flagged "[Data not available]" — never made up.

Every finance report produced by Aperture cites its source: document, page, section and cell.

Five Workflows for Manufacturing Finance Teams

• Compresses month-end close from 3–5 days to hours.
• Turns 5-week COGS variance cycles into root-cause investigations with every figure cited.
• Produces board-ready output with field-level provenance for CFOs and auditors.
• Consolidates multi-entity M&A reporting in days, not months.
• Builds an evidence trail by construction for compliance and legal teams.

How is Aperture Different from a Naive LLM Wrapper?

Most "AI for finance" tools are thin wrappers on a public LLM. They chunk-split tables. They lose row and column context. They cite nothing. Aperture is built the other way around.

Aperture vs ChatGPT, Copilot and naive RAG tools

• Provenance. Aperture cites document, page, section and excerpt for every number. The others do not.
• Financial tables. Aperture injects full documents to keep table structure. Naive RAG splits tables into chunks and breaks variances.
• Numeric grounding. Aperture checks every number against source. LLM wrappers invent plausible-looking numbers.
• Manufacturing ontology. Aperture ships pre-built entity types for BOMs, suppliers, cost centres and periods. Generic tools start from a blank page.
• ERP and MES connectors. Aperture plugs into SAP S/4HANA, Oracle, NetSuite, Dynamics and MES logs. Generic chat tools rely on copy-paste.

Naive RAG tools hallucinate financial figures; Aperture coerces unverifiable values to "[Data not available]".

Frequently Asked Questions

What is Aperture?

Aperture is the data intelligence layer for manufacturing finance teams. It links ERPs, MES logs, PDFs and spreadsheets into one source-backed graph. Every number in a report is traceable.

How does Aperture work for manufacturing finance teams?

You connect your sources as they are. Aperture structures them through an ontology-first knowledge graph. It then answers questions with field-level provenance — document, page, section and excerpt.

Can Aperture be used for COGS variance and month-end close?

Yes. Controllers use Aperture to cut month-end close from days to hours. CFOs and FP&A teams use it to compress 5-week COGS investigations into minutes, with every figure cited.

How does Aperture handle source traceability?

Every claim, number and metric is linked to its source document, page and cell. If a value can't be verified at the token level, Aperture marks it "[Data not available]". Reports stay trustworthy and audit-ready.

What document types and systems does Aperture support?

Aperture supports PDFs, Word, Excel and CSV, PowerPoint, emails (EML) and images with OCR. It also connects directly to ERP, MES, Google Drive, SharePoint and supplier portals.

How does Aperture compare to a standard RAG (retrieval-augmented generation) tool?

Standard RAG tools chunk-split tables, lose row and column context, and cite nothing — they often hallucinate financial figures. Aperture injects full documents to preserve table structure, runs token-level numeric verification, and cites document, page, section and cell for every number. A 3-stage hybrid retrieval engine with cross-encoder reranking improves precision by ~19% over naive RAG (Thakur et al., NeurIPS 2021).

What security and compliance standards does Aperture meet?

Aperture enforces multi-tenant isolation with per-tenant data encryption keys (DEKs), Postgres row-level security on every table, and an immutable audit log of every retrieval and report. Field-level provenance makes outputs audit-ready by construction for SOC 2, ISO 27001 and internal financial controls.

How Much Time Does Aperture Save?

• COGS variance: from about 5 weeks down to minutes.
• Month-end close: from 3–5 days of manual work down to hours.
• Data hunting: ~70% of finance time, given back to the team.

Aperture cuts month-end close from three to five days of manual reconciliation down to hours.

Controllers get whole days back each close cycle. CFOs get board-ready, source-cited variance reports the same day a question is asked.

Which ERPs and Systems Does Aperture Integrate With?

Aperture plugs into the systems your team already uses:

• ERPs: SAP S/4HANA, Oracle, Oracle NetSuite and Microsoft Dynamics 365.
• MES logs and supplier portals.
• Google Drive, SharePoint, Gmail, HubSpot and Stripe.
• Spreadsheets (Excel, CSV), PDFs, Word, PowerPoint, emails (EML) and OCR-scanned images — ingested as-is.
• Snowflake and Databricks for enterprise warehouse use cases.

No schema work is required. You connect a source and it is searchable in minutes.

Security, Compliance and Data Isolation

Aperture is built for finance and audit workloads. Security is not bolted on at the end.

• Each workspace is fully isolated, with per-tenant data encryption keys.
• Postgres row-level security is enforced on every query.
• Credential decryption needs a real user session — never a service-role bypass.
• Every ingestion, retrieval and report action writes to an immutable audit log.
• Field-level provenance lets external auditors trace any number back to its source.

Request Early Access

Aperture is in early access for manufacturing finance teams. Request access and cut your variance cycle from 5 weeks to minutes.