Sovereign enterprise engine

Argonos Synap: the sovereign engine for enterprise AI capabilities

Argonos Synap (previously QWAM) gives your organization a ready-to-use workspace for people who need answers, and a composable five-tier API for the team building the product, grounded in a rule-based ontology, deployed entirely on-premise or on a sovereign private cloud.

1M+ Documents processed with consistent API response times
3x Average TCO savings vs. volatile per-token billing
100% Sovereign: no data touches a public cloud

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Argonos Synap

Two ways in one sovereign foundation

Argonos Synap reaches your organization two ways – a ready-to-use workspace for the people asking questions, and a composable API for the team building the answer into something else. Same ontology, same governed data, same infrastructure underneath.

Synap Portal

A compliance officer opens Portal on a Tuesday morning, searches a claims file in plain language, and gets a summarized answer with the source documents attached: no query syntax, no engineering ticket. Ingestion, search, and reporting are already built; dashboards and newsletters update themselves as new documents come in. The expert reviews and validates before anything ships, but the first draft is already there.

Unified ingestion across internal data streams, with automatic structuring
Ontology-driven search and rapid entity extraction, no query language required
Human-in-the-loop screening to validate AI-generated tags before they’re trusted
Automated push to dashboards, workspaces, and newsletters and no manual reporting

cyber intelligence specialist examines data on big screen in governmental agency

Foundation

A five-tier API, one sovereign foundation

Stateless Extraction
Speech-to-text, named-entity recognition, and summarization for heavy documents, with zero-persistence processing.
Advanced RAG and Indexing
Hybrid search combining vector and BM25, with dynamic chunking and semantic reranking.
Prompt Engineering API
API-driven templates replace hardcoded prompts, with centralized versioning and validation.
Stateful Agents
Conversational CRUD with memory and token routing managed for you, so engineering teams build the interface, not the plumbing.
Tools and MCP
Internal databases exposed securely through function calling and native MCP for agentic execution.

Why Synap

Two ways proof-of-concepts fail and what changes with Synap

Generic LLMs hallucinate and cloud deployment stalls in compliance review, the two failure modes that keep enterprise AI stuck at the proof-of-concept stage. Synap addresses both from the same sovereign foundation.

The pain

Why proof-of-concepts don’t survive contact with production

Black Box Accuracy Crisis

Generic LLMs hallucinate without deep, industry-specific vocabulary in regulated sectors, an unvetted output isn’t a rounding error, it’s a liability.

Integration & Orchestration

Engineering teams lose months to fragile integration work, managing conversational state, memory, and chunking themselves instead of building the product.

Compliance Deadlock

Public cloud APIs don’t clear compliance review in regulated environments, so CISOs block deployment over prompt-injection and data-leak exposure before the PoC ever reaches production.

With Argonos Synap

What changes.

No verification backlog

The manual fact-checking that used to sit between a draft answer and a shipped one shrinks to edge cases, not routine review.

A quarter back on the roadmap

The state-and-memory work that used to eat a full engineering cycle is infrastructure now, not a project.

One deployment, not two conversations

Security review happens once, on the infrastructure, not again for every new use case built on “top of it.”

The pain

From hallucinated answers to a liability nobody signed off on

No context, no accuracy

Generic models hallucinate on industry jargon, proprietary terms, and edge cases outside general training data.

Confidently Wrong

A hallucinated answer looks the same as a correct one — nothing flags it before it reaches a customer or a regulator.

Untraceable Origin

Without grounding, an answer can’t be traced back to a source, so no one can verify it after the fact.

With Argonos Synap

What changes.

Ontology-Grounded

A rule-based industry ontology grounds every answer in vocabulary the model didn’t have to guess at.

Hybrid Retrieval

Advanced RAG combines vector and BM25 search with semantic reranking, instead of retrieval that returns the wrong document with confidence.

Fully Traceable

Every output stays traceable to the source it came from, not generated from general knowledge.

Use cases

Synap use cases across the enterprise

Digesting and structuring millions of pages (PDFs, emails, audio) turning processing backlogs measured in weeks into hours.

Governance

Sovereignty enforced at the infrastructure layer, not bolted on after

Synap deploys entirely on-premise or on a sovereign private cloud, running open-source models locally rather than routing through a public API. A dedicated processing mode keeps sensitive data and prompts inside the environment you control, so intellectual property never reaches a model you don’t own.