# Clerked automates complex accounts-payable work inside the finance systems teams already use

Canonical: https://mudpie.ai/companies/clerked/
Breadcrumb: [Home](https://mudpie.ai/) / [Companies](https://mudpie.ai/companies/) / [Clerked automates complex accounts-payable work inside the finance systems teams already use](https://mudpie.ai/companies/clerked/)
Author: Ali Abouelatta (https://mudpie.ai/authors/ali-abouelatta/)
Published: 2026-09-19
Updated: 2026-09-19
Research type: Company profile
Method: Company and accelerator sources checked 2026-09-19. Product claims are attributed to their sources; this is research, not a hands-on product trial.

Clerked is for finance teams that want accounts payable work to run inside the ERP and communication tools they already use. It handles the messy middle between an invoice arriving and a correctly coded, matched, approved and booked payable.

## What it does

Clerked’s current site describes AI clerks for line-level three-way matching, non-PO invoices, GL coding, partial deliveries, multi-currency, credits, freight, tax, duplicates, approvals, accruals and vendor follow-ups. It says the agents operate within existing systems rather than asking finance teams to move the whole process into a separate AP application. [Clerked homepage](https://clerked.ai/)

That breadth is the advantage for physical businesses. Manufacturing, logistics and other operators often have invoices with multiple POs, receipts, pack-size conversions and freight or duty lines; a simple OCR tool stops before the judgment. Clerked’s public examples also include approval routing, fraud signals and payment-status questions, so the product is trying to own the workflow rather than only extract fields.

The ENVE Composites case study gives a concrete customer example: Clerked says a two-person AP team cut invoice cycle time by about 85%, reached about 98% field-level accuracy on booked invoices and processed 84% of invoices without human correction inside SAP Business ByDesign. [ENVE customer story](https://clerked.ai/customers/enve) Those are customer and company-reported results, not a guarantee for a different ERP or invoice mix.

## What to check

The integration boundary is the real buying decision. Confirm which ERP, email, supplier portals and approval systems are supported, how exceptions are routed, who can change coding rules and how a human reverses a mistaken posting. The public pages do not list standard pricing, so scope should be tied to invoice volume, integration complexity and the cost of manual correction rather than a guessed per-seat rate.

The [YC profile](https://www.ycombinator.com/companies/clerked) identifies Evan Meyer as a data, ML and AI operator and Sunjeet Chugh as a former applied-AI and distributed-systems engineer with Amazon and Adyen experience. The current company was previously presented in the YC launch as Mod AI; Clerked is the current product and brand.

## My editorial take

I would shortlist Clerked for a company with complex invoices, a real ERP bottleneck and enough volume to measure matching and exception quality. Start with one entity and one document path, keep approval authority with finance and compare correction time—not just extraction accuracy. For a simple AP process, a conventional automation tool may be easier to own.

## Quick facts

| Field | Sourced detail |
| --- | --- |
| Buyer | Finance and AP teams in physical or complex businesses |
| Product | AI invoice capture, coding, matching, approvals and follow-up |
| Systems | ERP-native operation is advertised; exact integrations are scope-dependent |
| Pricing | Demo-led; no standard public price found |
| Main fit question | Are invoice exceptions and ERP context the bottleneck, not OCR alone? |

## Sources checked

Clerked’s YC profile, homepage and ENVE customer story were checked on 2026-09-19.

## Cohort context

Clerked is listed in Fall 2025. In our 2026-09-18 directory snapshot, 90 of 146 listed companies in that cohort have YC’s primary industry label B2B (61.6%). This is a current-directory comparison, not an original intake count or a performance ranking. [Nine-cohort dataset](https://mudpie.ai/research/yc-cohorts-2026-09-19.json).

## Public website snapshot

Observed 2026-09-19T16:14:57.557Z in raw homepage HTML. This records visible metadata and advertised links, not agent execution or product quality.

| Signal | Homepage observation |
| --- | --- |
| Product description metadata | Observed |
| Canonical link | Observed |
| H1 or H2 heading | Observed |
| Typed structured data | Observed |
| Docs/developer link | Not observed in this response |
| Pricing link | Not observed in this response |
| llms.txt link | Not observed in this response |
| Markdown alternate | Not observed in this response |

[Public observations](https://mudpie.ai/research/yc-homepage-links-2026-09-19.json) · [Collection method](https://mudpie.ai/research/yc-homepage-methods/README.md). Missing links here do not establish that a capability or file is absent elsewhere.


## Author disclosure

I cofound Lazyweb and publish Mudpie. This is an owner-written publication, not an independent testing organization. Research notes distinguish observations, sourced reporting and editorial judgment.