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primorpa+ai
Primo AI Server

Document AI and decision intelligence —
without per-page pricing

Primo AI Server processes structured and unstructured documents — invoices, contracts, claims, statements, foreign trade paperwork — and powers AI agents and decision-making across automated workflows. Deployed on-premise. Licensed by capacity, not by page volume.

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RPA + AI

RPA executes rule-based tasks with full repeatability.

Form submissions, data transfers, system integrations — when the logic is explicit, RPA runs it consistently, with a full audit trail and no variance.

AI understands documents and free text, and decides where rules cannot be written.

Document fields that shift across suppliers, free-text inputs, classification by content rather than filename — these require understanding, not a rule set.

AI Server adds the decision layer, on the customer's own infrastructure.

Capabilities

Six AI capabilities. Documents are the core

01

Smart OCR / IDP

Extract structured fields from documents — invoices, identity documents, forms, customs declarations. Train on around 50 labeled samples in the UI; no ML team or separate training infrastructure required.
  • —Train on around 50 labeled samples, no ML team needed
  • —Unstructured documents — full text extraction without training
02

Document classification

Classify documents before extraction to route each one to the right pipeline. Supports named document types and mixed batches. Configurable in the UI — no separate ML infrastructure.
03

AI Text — LLM document processing

Apply extraction, classification, summarization, and generation over raw text or directly on document scans via multimodal LLM.
  • —Connects to any local model or external LLM API
  • —Switch models without rebuilding pipelines
  • —Works on scanned PDFs and images via multimodal inference
04

Expert Systems and RAG

Knowledge base over the customer's own documents. Plain-language queries return answers with source citations. Role-based access controls which documents a user's context can include. Runs entirely on-premise.
05

Agent Builder

Visual flow builder to connect LLMs, OCR, RAG retrieval, APIs, and RPA robots as tools. Build and orchestrate AI agents visually — agent decisions are logged with inputs, tool calls, and outputs.
06

RPA robots as MCP tools

Robots are exposed as MCP tools and called from AI agents without integration glue or custom connectors. The same robot running in production is invokable directly from an agent flow.

Several design decisions set AI Server apart from cloud AI services and traditional IDP platforms — in pricing model, deployment topology, and how tightly document AI integrates with the automation layer.

Differentiators

What sets AI Server apart

No per-page pricing

Pay by capacity, not document volume.

No ML team required

Analysts and RPA developers configure and train.

No vendor AI lock-in

Connect supported local or external LLMs through approved APIs. Switch models without rebuilding workflows.

RPA robots as MCP tools

Native orchestration, no integration glue.

Agent observability

Agent decisions logged with inputs, tool calls, and outputs.

Air-gapped deployment

See deployment architecture →

For teams with an existing stack, AI Server typically consolidates tools currently managed as separate products.

What AI Server replaces in your stack

Without AI Server

  • —Separate IDP platform with its own licence, integration, and per-page billing
  • —Cloud-only OCR/NLP services that send documents off-premise
  • —Custom ML team to train and maintain extraction models

With AI Server

  • —Document AI integrated into the same platform, the same orchestrator, the same audit trail
  • —On-premise execution, including in air-gapped environments
  • —Configuration by analysts and RPA developers, no ML team needed

Not every deployment is a fit. Here is an honest read on where AI Server performs best — and where it may not be the right call yet.

When AI Server is the right choice

Good fit

  • —High document volume with consistent document types
  • —Existing RPA program needing to handle unstructured input
  • —Air-gap or data-residency requirements
  • —No ML team, but a need for production AI
  • —Predictable cost matters

Watch out for

  • —No clear success criteria defined before starting
  • —Primarily handwritten or very low-quality source documents
  • —No budget for GPU infrastructure
  • —Highly variable input with no available labeling data
Proven at scale

What AI Server delivers
at document scale.

−50—70%
Manual document handling at scale
Typically, across deployments handling invoices, contracts, and foreign trade paperwork.
−40%
Lower IDP cost vs. separate-platform alternatives
Capacity-based licensing eliminates per-page fees at volume.
0
Documents leave customer infrastructure
In on-premise and air-gapped deployments with external services disabled.

Ranges synthesized from industry analyst reports and Primo's deployments across document-intensive environments. Individual deployment results depend on baseline maturity, process scope, and integration complexity.

Related modulesPrimo RPA Orchestrator →Primo RPA Robot →Primo RPA Studio →
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See AI Server on your own document workflow

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