ForthClover
AI Document Processing

Documents in, clean data out.

Invoices, claims, contracts, forms and scans read into structured records and written back to your ERP, accounting system or CRM. Multimodal models handle tables, stamps and handwriting; every field is checked against your rules, and anything uncertain goes to a person before it reaches your books.

Book a free fit callSee how it works
Where document projects stall

OCR reads the text. Your process needs the right answer.

Most document tools work on the clean samples in the demo. They break on faxed scans, new vendor layouts, 80-page contracts and tables that span pages. These are the questions we answer before anything writes to your systems.
We set confidence thresholds per field and measure straight-through rate on your own documents — with a fixed price and a success metric agreed before we build.
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  • What accuracy does each field need — and how will we measure it?
  • Which documents can go straight through, and which need a person?
  • What happens when a new layout or vendor shows up?
  • Is extracted data checked against your POs, master data and rules?
  • Can every value be traced back to the page it came from?
  • What does each page cost, at today’s volume and at month-end peaks?
What we build

Documents we process most

We start with a high-volume document where errors are costly and a person can check the result. These are the ones teams bring us most often.

Invoice & AP intake

Reads invoices from email or a shared inbox, matches them to POs and receipts, and posts them to your accounting system or ERP for approval.

We measureStraight-through rate, field accuracy, cost per invoice

Claims & application intake

Extracts data from claim forms, applications and supporting evidence — handwriting and photos included — and flags what’s missing before a case is opened.

We measureField accuracy, missing-item catch rate, handling time

Contract data extraction

Pulls parties, dates, renewal terms and non-standard clauses from long contracts, each linked to its page, for legal to review.

We measureClause recall, review time per contract

Logistics & trade documents

Bills of lading, packing lists, customs forms and delivery notes read into your TMS or ERP, with mismatches against the order flagged.

We measureStraight-through rate, exception rate, processing time

Classification & routing

Sorts mixed batches — scans, email attachments, multi-document PDFs — by type, splits them, and sends each to the right queue, like the classification and extraction workflow we built with GLS Solutions.

We measureClassification accuracy, misroutes, time to route

Search over processed files

Extracted fields and full text indexed together, so your team can search years of files by meaning and jump straight to the source page.

We measureSearch success rate, time to find

How we build document pipelines

From one document type to a pipeline you can audit

Every engagement follows the same four phases, and most start with the 14-day pilot. You can stop after any phase and keep everything we built: code in your repo, infrastructure in your cloud.
  1. 01Phase 01

    Document audit

    We sample your real documents and agree in writing what “correct” means, field by field.

    • Document types, layouts, volumes and scan quality mapped
    • Field schema and validation rules agreed with your team
    • Labeled test set built from your own documents
    • Baseline: time and error rate of today’s process

    DeliverableExtraction spec and a signed success metric

  2. 02Phase 02

    Pilot

    A working pipeline on one document type, measured against the baseline.

    • Layout-aware extraction with a multimodal model or OCR service — whichever scores better
    • Schema validation and checks against your master data
    • Confidence thresholds per field, with a review queue for the rest
    • Every value linked to the page and spot it came from

    DeliverableReadout: field accuracy, straight-through rate, cost per page, go/no-go

  3. 03Phase 03

    Production build

    We connect it to your systems and harden it for real volume.

    • Write-back to your ERP, accounting system or CRM through its API
    • Evals as CI gates on every model or prompt change
    • Auto-scaling for month-end peaks, with cost alerts
    • Audit log of every extraction and every human correction

    DeliverableProduction pipeline, runbook and dashboards in your accounts

  4. 04Phase 04

    Operate & improve

    New vendors and layouts arrive every month. We keep accuracy measured.

    • Weekly accuracy reports built from reviewer corrections
    • Corrections fed back into prompts, rules and test sets
    • New document types added as you grow
    • Hand-off to your team whenever you’re ready

    DeliverableMonthly report scored against your baseline

What we build with

Models
ClaudeGPTGeminiOpen-weight vision models
Document AI services
Amazon TextractGoogle Document AIAzure Document Intelligence
Validation & review
Schema validationCustom review queuesAmazon A2ICustom eval suites
Integrations & cloud
ERP & accounting APIsAWSAzureGCPTerraform

“ForthClover's partnership accelerated our scalable AI workflow to production.”

ForthClover played a pivotal role in helping us develop a scalable AI workflow for document classification and data extraction. Their early prototypes and thoughtful knowledge transfer provided a solid foundation for our implementation, accelerating our journey to production. The team was collaborative, adaptable, and highly responsive throughout. We look forward to building on this partnership in future phases.

Gabriel RinaldiCTO, GLS Solutions

GLS Solutions

FAQ

Questions we hear on fit calls

Do we still need OCR if LLMs can read documents?
Sometimes. Multimodal models read messy scans, handwriting and tables well, but services like Amazon Textract, Google Document AI or Azure Document Intelligence are cheaper and more predictable on clean, high-volume forms. We test both on your documents and often combine them.
How accurate will it be?
That depends on your documents, so we don’t quote a number before we’ve seen them. We measure accuracy per field on a labeled set of your own documents, and set confidence thresholds so uncertain values go to a person instead of into your system.
What about handwriting, stamps and bad scans?
Current multimodal models handle much of this, and we test on your worst real examples, not clean samples. Any field that can’t be read with confidence goes to the review queue.
Can it write straight into our ERP or accounting system?
Yes, through its API, once the data passes validation. Most teams start with draft entries a person approves, then switch on straight-through posting for the document types that prove reliable.
What if we have many document types?
Start with the one that costs you the most. The 14-day pilot covers one document type; more types, more systems or a full intake pipeline move to the Agent Build proof stage at $9,500.

Which document eats the most hours?

Bring it to a free 45-minute fit call. You leave with a recommendation, a rough cost per page, and a straight answer on whether AI can handle it reliably.

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