How to choose an AI document processing provider

An AI document processing demonstration can be impressive. Upload an invoice, wait a moment and watch the supplier, date and total appear in neat fields. It is a useful test—but it is not yet a business process.

Your team still needs to know where the document came from, whether the captured information is dependable, which rules apply, who approves it, where the result goes and what happens when the document is unusual.

Choose a provider for that complete job. Extraction accuracy matters, but it is one part of the decision.

Quick answer

Ask every provider to show how their solution handles:

1. your real document types;
2. your required fields and checks;
3. missing, unclear and unusual items;
4. human review and approval;
5. connections to your existing systems;
6. document and data storage;
7. roles, permissions and traceability; and
8. a small pilot with agreed measures.

The right provider should be willing to discuss the awkward documents, not only the clean sample.

For example, imagine Njeri is comparing solutions for supplier invoices. One provider shows a clean PDF captured perfectly. Njeri adds three files from the real queue: a scanned invoice with a faint total, a duplicate invoice number and an invoice without a purchase order. The useful comparison begins when she asks each provider to show what the reviewer sees and what prevents an uncertain record from moving forward.

This is an illustrative buying scenario, not a reported customer result. Its purpose is to make the evaluation test concrete.

COMPARE THE COMPLETE JOB

A clean extraction demo is the starting point, not the buying decision.

THE DEMO

Clean invoice→Neat fields

Useful evidence, but only one part of the process.

→

THE BUSINESS TEST

REAL INPUTSNormal and awkward documents

CONTROLChecks, roles and human approval

BOUNDARYStorage, AI processing and access

HANDOFFDestination, errors and traceability

→

THE DECISIONA measured pilotContinue, change or stop using agreed evidence.

Begin with your process, not a feature list

Before speaking to providers, prepare a one-page brief:

• the document type and approximate monthly volume;
• how documents arrive today;
• the fields people capture;
• the checks they apply;
• the people who submit, review and approve;
• the system that receives the final record;
• the common reasons an item cannot proceed; and
• the current handling and turnaround time.

This makes the conversation concrete. It also helps you compare providers against the same requirement instead of comparing one polished presentation with another.

Question 1: can it handle our documents, not just the category?

Two supplier invoices may contain the same facts in very different layouts. A receipt may be faded, folded or photographed at an angle. A refund request may include a form, email and supporting evidence.

Ask the provider to test a representative sample that includes normal and difficult items. Do not share confidential information until the data arrangement is clear; use redacted or synthetic examples early in the discussion.

The aim is not to demand perfection. It is to see whether uncertainty is detected and presented honestly.

Question 2: who defines the checks?

A provider can configure a rule, but your organization must supply and own the business meaning behind it.

Ask how the solution will check duplicates, required fields, approved suppliers, limits, purchase orders or other trusted records. Then ask who can change those rules and how changes are tested.

Be cautious when a provider describes every judgement as something “the AI will learn.” Some decisions belong in explicit policy, and some belong with a qualified person.

Question 3: what happens when the answer is uncertain?

Good exception handling is a feature, not a failure.

Ask to see an unclear document, a missing field, a possible duplicate and an item outside the normal rule. The solution should show what it knows, what it does not know and what the reviewer needs to decide.

If the system turns every uncertainty into a confident field, your team may save typing time and spend it investigating mysterious errors later.

Question 4: can people review without rebuilding the work?

The reviewer should see the original document beside the prepared information, the checks already performed and the reason the item needs attention.

Ask whether the system supports distinct roles for submitters, reviewers, administrators and auditors. Confirm which actions are recorded and how an earlier decision can be understood later.

Human approval is not useful when it means clicking “approve” without enough context.

Question 5: where do documents and AI processing happen?

Ask directly:

• Where are the original documents stored?
• Is document content sent to an external AI provider?
• Can the solution run inside an environment our organization controls?
• Who at the provider can access our data, and under what conditions?
• What information leaves the environment when an approved result is sent elsewhere?

The appropriate answer depends on your risk, regulation and internal policy. What matters is that the boundary is specific and documented.

Question 6: does it finish the handoff?

An extracted record that must be copied into an ERP or accounting system is prepared work, not completed work.

Ask how approved information reaches its destination. Confirm what happens when that system is unavailable, rejects a record or returns an error. Somebody should be able to see the status and resolve the problem without searching through logs they cannot understand.

Question 7: what will the pilot prove?

A useful pilot has a limited scope and a decision at the end. Agree on:

• one document type;
• a representative sample;
• the normal and exception cases to test;
• the people involved;
• the data and system boundary;
• the measures you will compare; and
• the conditions for continuing, changing or stopping.

Measure handling time, turnaround, corrections, repeated entry and exception rates. A high extraction percentage is useful evidence, but it should not be the only evidence.

For a broader governance reference, the voluntary NIST AI Risk Management Framework covers risk mapping, measurement, management, documentation and organizational responsibility. It does not replace Kenyan law, sector requirements or professional advice, but it provides useful questions for an internal review.

Hold Garatropic to the same test

Garatropic Relay is designed for organizations that want the document workflow configured around their real sources, fields, checks, approvals and destinations. For an enterprise deployment, the intended design keeps document storage and AI processing inside an environment the organization controls, while people review exceptions and approve consequential work. The proposed architecture and any external connection should still be documented and reviewed with your technical, security and legal teams before production use.

Ask us the same difficult questions in this guide. Bring normal documents and awkward ones. Ask us to identify which checks Relay can prepare, which decisions must remain with your team, what access the destination system makes possible and what the pilot will measure.

It may be a good fit when:

• the repeated work extends beyond reading a document;
• approved information needs to reach an existing business system;
• you need clear human responsibility and traceability; or
• your data boundary requires an in-house deployment.

Relay may be more than you need when a standard cloud tool already handles one simple document type, fits your data policy and connects cleanly to your system. Custom configuration earns its place when the workflow, controls or integrations genuinely require it.

Choose the provider that can explain the hard part

The best provider conversation should leave you with a clearer picture of your own process, including the parts automation should not decide.

Bring a representative sample, ask to see the exception path and agree on what a worthwhile result would look like. A system that processes the easy document beautifully but leaves your team to repair the workflow around it is only half a solution.