De-Coupling Logic
from Language.

We treat "Search" as a Compute Problem, not a Language Problem.

The Modulus architecture separates the computational heavy lifting (HPC) from the semantic understanding (AI). The result is a system that processes live data with ultra-low latency and uses Large Language Models strictly for intent understanding and response formatting - never for factual retrieval.

input

Input

Live Data Firehose + User Query

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memory

Modulus HPC Engine

Normalize • Compute • Filter

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layers

Context Bridge

Deterministic Answer Extraction

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psychology

LLM Output

Natural Language Response

How It Works: The 4-Stage Process

01

High-Velocity Ingestion

Unlike standard Vector Databases that rely on periodic indexing, the Modulus Engine preprocesses and ingests data in real-time.

  • check_circle Millions of updates per second
  • check_circle Sub-millisecond normalization
  • check_circle Structured & Unstructured simultaneously
02

Deterministic Pre-Processing

We do not ask the AI to post-process data using custom prompts. We use hard-coded and dynamic HPC logic to preprocess data in real-time.

  • check_circle Physics & Math calculations
  • check_circle Geospatial filtering
  • check_circle Logic applied before AI touch
03

The "Truth" Injection

Once the HPC layer identifies the correct data, it passes those specific records to the Large Language Model. The LLM is architecturally restricted from looking outside this context.

  • check_circle Rigid Context Window
  • check_circle Few to zero AI hallucinations
  • check_circle 100% Grounded responses
04

Semantic Response Generation

The LLM translates the raw, verified data into a conversational, human-readable response that matches the user's tone and intent.

  • check_circle Human-readable format
  • check_circle Tone matching
  • check_circle Citation generation

Built for the Enterprise. Hosted by You.

dns

Deployment Models

On-Premises / Air-Gapped: Fully containerized deployment on your metal. Ideal for Defense, Healthcare, and High-Frequency Trading.

Private Cloud: Deploy within your AWS VPC, Azure, or Google Cloud environment.

shield

Data Sovereignty

Zero Data Exfiltration: We do not train on your data. We do not see your user logs. The entire loop happens inside your perimeter.

Compliance: Architecture supports HIPAA, SOC2, and GDPR requirements by design.

api

Integration

API-First Design: Connects to your existing frontend via simple REST or WebSocket APIs.

Model Agnostic: The Modulus HPC layer works with Llama, GPT, Claude, Gemini, Grok, or your own fine-tuned internal models.

A Defensible Technological Moat

This hybrid approach - injecting real-time HPC data into an LLM context window to force deterministic accuracy - is not just an engineering preference; it is a patented methodology.

Our IP covers the specific mechanisms of synchronizing high-frequency data streams with natural language processing, ensuring that our partners have exclusive access to the most reliable search architecture on the market.