Gede AI provides a middleware SDK that intercepts large language model prompts, identifies embedded arithmetic or logical expressions, and evaluates them on a deterministic CPU engine before reinserting the exact result into the model’s response. The library, with native bindings for Python, Swift, and Objective‑C and compatibility with OpenAI, Anthropic, and other APIs, reduces token usage and compute costs while generating an audit‑log for compliance verification.
Funding
Funding not disclosed
Founders
Product
Problem
Large language models frequently generate incorrect numerical or logical results, causing downstream errors, increased token consumption, and compliance concerns for applications that rely on precise calculations. Detecting and correcting these hallucinations manually adds development overhead and operational cost. Organizations therefore need a way to ensure deterministic computation without redesigning their entire AI stack.
Solution
Gede AI provides a middleware SDK that intercepts LLM prompts, automatically identifies embedded arithmetic or logical expressions, and offloads their evaluation to a deterministic CPU execution engine. The computed result is injected back into the model’s response, preserving natural language flow while guaranteeing mathematical correctness. By separating “think” from “compute,” the SDK reduces token usage and AI‑compute expenses by up to 80 % and produces an immutable audit trail for compliance verification. The library offers native bindings for Python, Swift, and Objective‑C and integrates with major providers such as OpenAI and Anthropic via a single import statement. No infrastructure changes or additional orchestration layers are required, enabling production deployment within minutes. The solution also includes a deterministic test suite and logging API that developers can use to certify output integrity.
Target Audience
The primary customers are software engineers and product teams building AI‑driven applications that require reliable numeric or logical output, as well as enterprises in regulated sectors seeking auditable LLM integrations.
Features
- Pattern‑matching and abstract‑syntax‑tree analysis engine that detects mathematical and logical sub‑queries in real time
- Deterministic CPU compute module that executes identified operations with exact precision, independent of GPU‑based LLM inference
- Seamless result injection API that returns enriched LLM responses while preserving original conversational context
- Multi‑language SDK (Python, Swift, Objective‑C) with one‑line integration and no external service dependencies
- Compatibility layer for OpenAI, Anthropic, and other major LLM APIs via standard request wrappers
- Built‑in audit‑log generation and deterministic test suite for compliance reporting and debugging
- Performance metrics dashboard showing token savings, latency reduction, and cost impact per request