Grant Processing & Scoring

The bottleneck nobody can hire their way out of

Public institutions, grant-giving bodies, procurement offices and regulators all face the same operational problem. Each intake brings thousands of pages of structured and unstructured documentation. A small team of experts has to verify formal correctness, eligibility, budget compliance and signatures before they can even start reviewing the substance of what is being proposed.

The volume keeps growing. The expert headcount does not. Quality erodes, not because the experts are slower, but because they spend most of their time on tasks that should be automated.

Conventional automation fails here. RPA breaks the moment a document deviates from the template. Generic LLMs hallucinate when the budget table is messy or the signature block is unusual. Both fall foul of GDPR and the EU AI Act when they involve sending personal data to external services.

Grant Processing & Scoring is built specifically for this class of work, with the regulatory constraints baked in.

What Grant Processing & Scoring is

A hybrid review system that combines deterministic automation (for rules that should never be left to a model) with AI-assisted analysis (for everything that requires reading and interpretation). The system processes batches of applications in their original form, produces a structured assessment per application, and routes only the cases that require human judgement to the expert team.

Every assessment carries citations. Every flag links back to the page, table cell or paragraph that produced it. Final substantive decisions remain with the human expert; the system is explicit about what it can and cannot decide.

How it works

Four stages, each tuned to the kind of decision being made.

• Read and check. Documents and budget tables parsed and extracted. Hard rules verified by the system: implementation window, cost-category limits, minimum own contribution, page numbering. The output is unambiguous: pass, fail or attention required.
• Eligibility. AI-assisted: is the applicant entitled to apply? Is the document signed by an authorised person? Does the project fall into an excluded category? Findings carry citations and route for review when uncertain.
• Completeness and technical correctness. Budget detail, attachment presence, cost-category eligibility against regulations. Mixed rule-based and model-based checks.
• Substantive scoring. AI analyses alignment with statutory goals, internal coherence, VAT treatment consistency. The model produces a structured score with explanations. The expert decides.

Optional modules add automated entity verification against external registers (KRS, REGON, sanctions lists, beneficial-owner databases), prior-application history and conflict-of-interest screening.

Capabilities

Hybrid architecture. Rule-based where rules are right (numerical limits, formal rules), AI-assisted where context matters (eligibility, coherence, fit with statutory goals). The architecture is explicit about which decision is which.

On-premise, sovereign deployment. Full GDPR and EU AI Act compliance. No personal data leaves the institution. Open-source models, including Polish-language Bielik for sovereign deployments.

Citation-grounded flags. Every flag and every score is linked to its evidence, the row of the budget, the paragraph of the project description, the section of the regulation it potentially breaches.

Configurable for your regulations. Cost categories, eligibility rules, scoring criteria and statutory goals are configured per institution. The product is the engine; the rules are yours.

Batch and incremental modes. Large intakes processed offline, individual applications processed on submission. Same engine, both modes.

Auditable and reviewable. Every action, automatic or human, is logged. Reviewers can override the system; overrides feed back into tuning.

Where it's used

In national cultural institutions to process grant applications across multiple programmes, with full GDPR and EU AI Act compliance and on-premise deployment. In public-sector education programmes to assess proposals against statutory criteria. In procurement and tender review to handle formal verification at scale.

The pattern repeats across domains: experts are scarce, formal review is slow, and the process is heavily regulated. The product is configured per programme; the engine is the same.

Why Grant Processing & Scoring

Most automation tools are either fast or compliant. This one is built for the cases that have to be both.

The hybrid architecture is what makes it work. Deterministic rules cover the cases where a model would be both unnecessary and risky. AI-assisted modules cover the cases where rules cannot capture the question. Every output carries the evidence that produced it, and every substantive decision stays with a human who can be held accountable.

For institutions operating under GDPR and the EU AI Act, the on-premise architecture is a feature, not a constraint. Applicant data never leaves the institution. The system is auditable, explainable and reviewable from the first day.

FAQ

Pilot on a real intake

We configure the engine for one of your programmes and run it on a representative batch of applications. Three to six weeks. The deliverable is a working pipeline plus a quality report against your expert reviewers.

Start a pilot →