Friday AI offers an Adaptive Framework API that intercepts LLM requests and automatically reshapes prompts for optimal efficiency. It combines activity detection, semantic complexity scoring, and dynamic context window scaling (12 K–1 M tokens) to compress inputs and route each request to the most cost‑effective model, reducing token usage, latency, and overall cost. The platform includes real‑time monitoring, enterprise‑grade security, and SDKs for seamless integration with AI applications.
Funding
Funding not disclosed
Founders
Product
Problem
Developers and enterprises using large language models often face high token consumption and compute costs because static context windows and manual model selection do not adapt to the varying complexity of inputs. Inefficient prompt sizing leads to slower response times and inflated expenses, especially at scale.
Solution
Friday AI’s Adaptive Framework API sits between an application and any LLM to automatically reshape each request for optimal efficiency. An Activity Detection Engine classifies the task (e.g., chat, code, research) and determines an appropriate base context range. A Semantic Analysis Engine scores input richness, removes redundant tokens, and compresses the prompt while preserving meaning. The Adaptive Context Manager then scales the context window in real time—from 12 K to 1 M tokens—matching resource allocation to the task’s complexity. Finally, a Model Routing layer forwards the optimized request to the most suitable model, reducing token usage, latency, and overall cost. All steps are observable through a monitoring dashboard and can be deployed on cloud, hybrid, or on‑premise environments.
Target Audience
The primary customers are AI developers and product teams building LLM‑powered applications, as well as large enterprises that run high‑volume AI workloads and need cost‑effective, scalable inference pipelines.
Features
- Dynamic context window scaling (12 K
- 1 M tokens) with smooth, gradual transitions based on real‑time analysis
- Hybrid activity detection (semantic + keyword) that auto‑classifies tasks such as chat, code generation, research, or creative writing
- Semantic complexity scoring (density, richness, depth) to filter redundant context and compress prompts without loss of meaning
- Automatic model routing to the most cost‑effective LLM for each request (OpenAI, Anthropic, Grok, etc.)
- Node‑level context lifecycle management and graph‑based indexing for fast recall (<300 ms)
- Integrated SDKs (Python, Node.js, FastAPI) and REST API for plug‑and‑play integration with minimal code
- Enterprise‑grade security: SOC 2 compliance, encryption in transit and at rest, fine‑grained access controls
- Admin dashboard providing real‑time token usage, latency, and scaling efficiency metrics, with alerting and SLA monitoring