The platform layer for AI that comprehends, remembers, and scales.

An API-addressable, persistent expertise cache. Point it at a corpus, it creates a "locus" of facts; subsequent inference calls to any model are routed through the locus, which injects domain context and prunes irrelevant attention heads in real-time. Turns any stateless foundation model into a stateful domain expert without fine-tuning. Solves catastrophic forgetting and domain drift at the infrastructure level — as a service.

A containerized, intercepting proxy for agentic development. It sits between developer agent code (Devin, Codex, Claude Code, custom scripts) and the model API, applying context compression and inference acceleration to dramatically lower token costs and latency. Decouples agent logic from inference economics — developers run more complex multi-turn agents on smaller, cheaper models that perform like larger ones.

Compliance and audit middleware that generates an immutable, verifiable "receipt" for every AI-generated conclusion. Logs the exact sources consulted, the agent's reasoning path, the mechanical confidence score per claim — chain of custody from query to answer. Modulum's vocabulary restriction provides inference-time hallucination elimination; Hypercore's confidence math provides the provenance audit trail. Sells certainty and defensibility, not just model capability.

A software toolkit for model providers to create domain-specific variants without retraining. Provider integrates the SDK into their inference stack, allowing them to "anneal" a small base model (e.g., 8B) with a customer's domain corpus via Modulum's cache, enabling it to outperform a much larger general model. Inverts the scaling laws for domain-specific tasks — new business model for foundation model providers: cheaper, specialized, high-margin inference instead of competing on parameter count.

A custom inference card that hardwires Modulum's 7 components: 75% noise detection via dedicated attention analyzers, real-time sub-1ms KV pruning. The chip would have dedicated silicon for the components, directly wired to on-chip memory holding the Persistent Domain Schema. When the transformer needs to attend, instead of full dot-product attention over a dense KV cache, it issues a hardware-level query to the PDS block — returning only the ~25% signal. Fuses inference and RAG into a single clock-cycle-level operation.

A fully managed, long-running, vertical-specific AI agent that lives in the customer's VPC. Sold as a solution, not a tool. Customers subscribe to "Sentient Clinical Researcher" or "Sentient Underwriting Analyst" — not an API. Hypernym manages the entire stack: Hypercore continuously ingests new domain data, updating a live PDS; the Modulum-powered agent uses persistent memory to perform its function over weeks and months, becoming progressively more expert without retraining.

An analysis service that ingests a customer's proprietary foundation model and a target corpus, then produces a "Structural Efficiency Map" detailing exactly which attention heads and layers are redundant or contribute only noise for that specific domain. Provides a surgical pruning/distillation strategy. Sells insight, not implementation. Productizes Hypernym's core "75% of attention is noise" discovery as a unique diagnostic capability — a "model MRI."

Streams live corpus updates (real-time sensor data, market feeds, news) into Hypercore workflows for incremental entity resolution; anneals Modulum inference to clean and persist evolving expertise without full re-inference; outputs delta-updated fact graphs with confidence deltas tracked per stream event. Turns static transformers into adaptive systems for time-series domains where updates previously required full recompute and lost historical context.