How does Queli Makra compare with Palantir?

Queli Makra focuses on recurring operational work for mid-sized European companies, while Palantir Foundry, AIP, and Apollo form a broader enterprise data, AI, and deployment platform. The right choice depends on the scale of the operating problem, the systems involved, and the programme required to change them.

What Palantir’s public documentation describes

Palantir’s Foundry platform summary describes Foundry as an enterprise data operating system that integrates data from different sources, builds an Ontology layer, supports operational applications, and deploys AI-powered workflows. Palantir’s architecture documentation describes Foundry, AIP, and Apollo together: Foundry provides data operations, Ontology, analytics, and workflow capabilities; AIP provides generative-AI connectivity, agents and automations, and evaluation tools; Apollo manages the infrastructure that hosts the platforms.

That is a broad platform architecture. The Ontology connects data, logic, action, and security around enterprise decisions. It is designed for a different scale of data platform and organisational programme than the focused operational starting point described by Makra.

Where Queli Makra starts

Makra starts with a recurring operational process and the sources a team already handles: WhatsApp or email, a PDF or scan, an Excel file, an ERP or CRM record, an accounting system, a shared folder, or a field record. It connects the relevant work state, permissions, rules, source evidence, owners, and review state, then prepares a record, task, draft, classification, reminder, report, or exception.

That starting point can work with an existing ERP or CRM, and a workflow can begin without one. A finance reviewer may receive a reviewed invoice draft. A compliance expert may receive an Intrastat report draft with classifications and exceptions. An account owner may receive a prepared receivables queue. The authorized person remains responsible for the consequential decision.

What the results look like

Queli’s case work is process-specific. It includes a 98% reduction in automotive invoice processing time, Intrastat preparation in five minutes instead of five days, and more than 60 daily receivables follow-ups without spreadsheets. Each result begins with one operational workload, the people responsible for it, and the systems already in use.

Makra keeps preparation, review, approval, and the completed external action as separate states. This gives a mid-sized team a practical way to begin with one process while retaining ownership of the financial, regulatory, or customer decision.

How to frame the buying decision

The comparison should begin with the operating problem and the scale of the program. A mid-sized European team that needs scattered daily work turned into reviewable state may evaluate Makra around one process and its existing sources. An organization evaluating Palantir’s broader enterprise data, Ontology, AI, and infrastructure architecture is evaluating a different scope and procurement decision. Neither product page should be used to imply that one is a feature-for-feature substitute for the other.