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Domain Intelligence Infrastructure

Your institution's knowledge is its most valuable asset. SOVRIA makes it usable.

Knowledge graphs, vector search, fine-tuned neural networks, and API infrastructure for organizations sitting on decades of unstructured domain expertise.

How It Works Work With Us
The Problem

Decades of expertise, trapped in formats machines can't reason over

Scientific archives, institutional registries, research corpora. Frontier LLMs have never seen this data. The gap between general-purpose and domain-specific intelligence is not a model problem. It is a data and architecture problem.

Accuracy: Domain-Specific vs. General-Purpose

GPT-4 (zero-shot, biomedical NER) 59.9%
Fine-tuned domain model (biomedical NER) 90.9%

Cost and Energy per Million Tokens

Frontier API (GPT-4 class) ~$3.75
Self-hosted fine-tuned 7B model ~$0.13

Sources: LoRA Land (Predibase, 2024) · Chen et al. (Nature Comms, 2025) · TokenPowerBench (Niu et al., 2024)   Full analysis →

Technology

Four layers. One intelligence API.

Every engagement produces the same architecture: a composable, API-first stack that any frontend can consume.

Layer 01

Verified Data Layer

Structured, provenance-documented datasets extracted from institutional archives.

Source materials (PDFs, spreadsheets, legacy databases) are ingested, normalized, and stored with full provenance chains. Every record traces back to its origin document, page, and extraction confidence score. No data enters the system without verification.
Layer 02

Semantic Engine

Vector embeddings, semantic search, and knowledge graph relationships across the corpus.

pgvector BGE Embeddings RAG
Domain-specific embedding models encode meaning, not just keywords. pgvector-powered similarity search surfaces connections across decades of institutional knowledge. Relationships between entities (people, concepts, records) are mapped and queryable.
Layer 03

Domain Models

Fine-tuned models (7B-13B parameters) trained on verified, provenance-documented corpora.

vLLM MLX LoRA / PEFT Mistral
Small, efficient models trained exclusively on verified institutional data. Every model publishes its training corpus, compute requirements, and provenance documentation. Domain-specific accuracy exceeds frontier models at a fraction of the cost.
Layer 04

Intelligence API

RESTful + MCP endpoints. One pipe, any consumer: websites, platforms, third-party tools.

The API is the product, not the website. Your public site, internal tools, partner platforms, and future applications all consume the same structured intelligence layer. No vendor lock-in on the presentation layer. Any technology, any designer, any team.
Services

We build your intelligence layer. You own the infrastructure.

Short, high-value engagements that transform unstructured domain data into knowledge graphs, searchable embeddings, and domain-tuned models behind a single API.

Unstructured Data

PDFs, spreadsheets, legacy databases, institutional archives

SOVRIA Engagement

Knowledge graphs, embeddings, domain models, API layer

Structured API

Working reference frontend with full API documentation

Your Design Team

Any designer, any framework, any frontend technology

See full deliverables →
Products

From infrastructure to intelligence

The Sovria stack powers products that bring domain-specific intelligence to specialized fields.

Live

Cladari™

Verified taxonomic data, breeding genetics, provenance tracking, and AI-powered specimen verification.

Visit cladari.co
Coming Soon

Domain Models

Open-weight models fine-tuned on verified institutional data. Full training provenance and data lineage published.

In Development

Verification Infrastructure

Provenance tracking, human-in-the-loop scoring, and multi-source cross-referencing for high-stakes domains.

Proof of Work

Built with real data, not slide decks

Cladari™ is a live botanical intelligence platform built on the Sovria stack.

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Production database tables with full relational integrity
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Structured care records with temporal tracking
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Verified specimen photographs with metadata
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Taxonomic reference records with vector embeddings
See the full stack →
Principles

How we build

Non-negotiable commitments that shape every engagement and every line of infrastructure.

01

Data Sovereignty

Your data stays under your control. No dependency on Sovria for ongoing operations.

02

Source of Truth

Every record traces to its origin with full provenance chains.

03

Efficient by Design

Smaller models on better data outperform larger models on everything.

04

Transparency

Every model publishes its training corpus, compute requirements, and data lineage. No black boxes.

05

FAIR Data Alignment

Findable, Accessible, Interoperable, Reusable. Open standards by default.

06

Composable Architecture

API-first. Every component independently deployable. No vendor lock-in on any layer.

Get In Touch

Let's structure your domain intelligence

Scientific societies, research institutions, specialty publishers, or any organization with deep unstructured domain data.

Or email us directly at info@sovria.com