Sentia is a selective conversational intelligence engine. It detects when users pivot mid-conversation and passes only relevant context — cutting token costs while eliminating confusion.
Most AI systems send the entire conversation to the LLM on every turn. Sentia sends only what matters.
Irrelevant data, higher cost, slower, confused context
Precise context, lower cost, faster, coherent response
Real calculations for a typical 10–15 minute loan origination call with 8 LLM inference calls.
Traditional costs grow quadratically (each call sends more history). Sentia stays flat.
Based on GPT-4o API pricing ($2.50/1M input tokens). Self-hosted costs scale proportionally with compute.
Calculations based on 8-turn, 10–15 min conversations with GPT-4o pricing ($2.50/1M input tokens). Longer conversations and self-hosted models yield even greater savings. Output token costs (~$1,600/100K convos) remain the same in both approaches.
Research consistently shows that irrelevant context degrades LLM performance. Sentia addresses this at the architectural level.
As input tokens increase, models struggle with ambiguous distractors. Research shows irrelevant context causes confident but incorrect outputs — even when correct information is present in the prompt.
Time-to-first-token scales with input size. At Turn 8, Sentia processes 2,100 tokens vs 4,900 — cutting prefill time by ~57%. For self-hosted 7B models, this difference is the gap between real-time and perceptible lag.
Research on Maximum Effective Context Window (MECW) shows real-world LLM accuracy drops sharply as token count exceeds task-relevant needs. Keeping context tight keeps accuracy high — especially in agentic workflows.
Real-time analysis of every utterance for topic markers and intent signals
Identifies when user switches topic using semantic similarity analysis
Selectively passes only relevant context segments to the model
Model generates focused response without irrelevant noise
Borrower discusses income, then asks about interest rates, then returns to employment. Sentia keeps each thread clean.
Debtor raises payment concern, pivots to dispute, then requests restructuring. Each handled precisely.
Customer asks about balance, then a product question, then a complaint. Context stays sharp throughout.