Superforce AGI builds a four-layer architecture that transforms standard LLMs into systems capable of acquiring, owning, and compounding structured domain knowledge. Unlike conventional AI that merely retrieves from static training weights, Superforce constructs persistent knowledge graphs and memory stores that deepen with every interaction. The platform offers three products—Superforce Learn, Superforce Agentic, and a developer API—all designed to make intelligence a durable, appreciating asset that survives model upgrades.
Funding
Funding not disclosed
Founders
Product
Problem
Every LLM-based system retrieves patterns from static training weights without building a structured domain model, meaning no intelligence is actually owned by the organization. Each new client deployment or model generation requires starting from scratch, as fine-tuning and domain adaptation reset with each foundation model upgrade. RAG pipelines and vector stores are patches that help retrieval but do not create compounding intelligence assets.
Solution
Superforce AGI provides a four-layer architecture—Ingestion, Reasoning, Memory, and Transfer—that builds structured domain models from every deployment, including entities, causal chains, expert vocabulary, and uncertainty maps. The system's three persistent memory stores (Domain, Feedback, and Pattern Memory) compound with each interaction, making the 50th interaction measurably better than the first. The architecture is model-agnostic, sitting above the base LLM so that when new models ship, all accumulated domain models and calibrations carry forward untouched. Superforce delivers this through three products: Superforce Learn for expert knowledge capture, Superforce Agentic for human-gated autonomous workflows, and a developer API for embedding domain intelligence into custom applications.
Target Audience
Primary customers are enterprises and domain experts who need to capture institutional knowledge permanently, as well as developers and organizations deploying AI systems that require compounding, auditable domain intelligence across multiple industries and regulatory contexts.
Features
- Ingestion layer builds structured knowledge graphs with entity mapping, causal chains, vocabulary graphs, regulatory trees, and uncertainty flags
- Three-stage auditable reasoning pipeline: Recall, Synthesise, and Judge, with uncertainty surfaced rather than hidden
- Three persistent memory stores: Domain Memory for structured domain knowledge, Feedback Memory for calibration records, and Pattern Memory for cross-domain structural patterns
- Transfer layer applies patterns from prior domains to accelerate acquisition of new domains, with demonstrated execution across 12 countries and 120+ languages
- Expert correction interface that converts every correction into a Feedback Memory rule with full provenance tracking
- Human gate architecture in Superforce Agentic that pauses workflows at configurable decision points
- Automatic training harvest that generates before/after pairs from every completed agent run
- REST API with usage-based pricing that provides domain intelligence rather than just text generation