System and method for adaptive multi-tier processing of generative artificial intelligence input communications
A system and method for adaptive semantic routing of artificial intelligence (AI) communications is disclosed. A semantic router receives AI incoming communications and generates dense embedding vectors. A similarity score is computed between input embeddings and cached semantic objects stored in a semantic representation repository. A direct cache retrieval is performed when similarity exceeds a first confidence threshold, full generative AI processing when similarity is below a second threshold, or a hybrid template-based assembly combining cached static segments with dynamically generated content when similarity falls between thresholds. For voice applications, specialized components including audio to intent encoder, prosodic normalizer for natural speech assembly, dynamic slot synthesizer for variable content, and codec transformer for telephony compatibility enable audio waveform caching with sub-200 ms latency are used. A reward-based learning optimizer continuously adjusts routing thresholds based on quality feedback collected across cache hit rates, response quality, slot accuracy, latency, and prosodic naturalness.
1 . A system for adaptive semantic routing of communication for generative artificial intelligence (AI) systems, the system comprising:
one or more processors, a memory, and a plurality of programming instructions stored in the memory, the plurality of programming instructions when executed by the one or more processors causes the one or more processors to:
receive, at a semantic router, an incoming communication requiring AI processing, wherein the incoming communication comprises at least one of text data, audio data, image data, or video data;
encode the incoming communication into a dense embedding vector within a multi-dimensional semantic embedding space using a neural network encoder, wherein the dense embedding vector captures semantic meaning of the incoming communication;
determine a similarity score between the dense embedding vector and a plurality of cached semantic data records stored in a semantic representation repository, wherein each of the cached semantic data records comprises an intent identifier, a semantic vector, an associated response, and a quality metric computed from historical evaluation data associated with prior responses;
responsive to the similarity score exceeding a first confidence threshold, retrieve a cached response from the semantic representation repository;
responsive to the similarity score falling below a second confidence threshold, transmit the incoming communication to the external generative artificial intelligence service for generating a response; and
responsive to the similarity score falling between the first confidence threshold and the second confidence threshold, generate a composite response by combining one or more cached response segments retrieved from the semantic representation repository with dynamically generated response content produced by the external generative artificial intelligence service;
wherein the first confidence threshold and the second confidence threshold are dynamically adjusted based on response quality feedback and are configured to control selection between cached response retrieval, hybrid response generation, and external generative artificial intelligence processing.
2 . The system of claim 1 , wherein to transmit the input communication to the external generative artificial intelligence service, the plurality of programming instructions, when executed by the one or more processors, causes the one or more processors to:
receive a generated response from the external generative artificial intelligence service;
compute a token cost associated with the generated response;
evaluate, a quality score for the generated response, wherein the quality score is computed based on user feedback, conversation continuation metrics, task completion indicators, or semantic coherence measures; and
responsive to the quality score exceeding a promotional threshold, store the generated response and corresponding semantic vector in the semantic representation repository for subsequent semantic matching.
3 . The system of claim 2 , wherein to store the generated response, the plurality of programming instructions, when executed by the one or more processors, causes the one or more processors to:
index the semantic vector for similarity search;
associate the generated response with the indexed vector; and
record quality metadata for future cache promotion decisions.
4 . The system of claim 2 , wherein the plurality of programming instructions, when executed by the one or more processors, causes the one or more processors to:
update, by a reward-based learning optimizer, the semantic representation repository based on the quality score, wherein update comprises increasing a priority ranking of cached semantic objects associated with high quality scores, decreasing a priority ranking or evicting cached semantic objects associated with low quality scores.
5 . The system of claim 1 , wherein to generate a composite response, the plurality of programming instructions, when executed by the one or more processors, causes the one or more processors to:
retrieve a template response from the semantic representation repository, wherein the template response comprises static segments and dynamic slot identifiers;
generate dynamic content for each dynamic slot identifier using a constrained generation process; and
assemble the composite response by combining the static segments with the generated dynamic content.
6 . The system of claim 1 , wherein to determine the similarity score between the dense embedding vector and a plurality of cached semantic objects, the plurality of programming instructions, when executed by the one or more processors, causes the one or more processors to:
compute cosine similarity between vector representations;
compute Euclidean distances in the semantic embedding space; or
compute dot product similarity measures.
7 . The system of claim 1 , wherein to dynamically adjust the first confidence threshold and the second confidence threshold, the plurality of programming instructions, when executed by the one or more processors, causes the one or more processors to:
monitor cache hit rates and response quality feedback for every response generated;
adjust at least one of the first confidence threshold or the second confidence threshold based on observed cache hit rates, response quality metrics, and cost-performance tradeoffs.
8 . The system of claim 7 , wherein to adjust the first confidence threshold and the second confidence thresholds, the plurality of programming instructions, when executed by the one or more processors, causes the one or more processors to:
compute a quality delta between observed quality scores and target quality thresholds;
responsive to the quality scores exceeding a target quality threshold, lower at least one confidence threshold using the quality delta, wherein lowering the confidence threshold increases cache hit rates; and
responsive to the quality scores falling below the target quality threshold, raise at least one confidence threshold using the quality delta, wherein raising the confidence threshold improves response accuracy.
9 . The system of claim 1 , wherein the plurality of programming instructions, when executed by the one or more processors, causes the one or more processors to:
process, using a text encoder, text inputs using transformer-based language models generating 768-dimensional embeddings;
convert, using a speech-to-vector encoder, audio inputs directly into semantic vectors without intermediate speech-to-text transcription;
process, using an image encoder visual input; and
handle, using video encoder, temporal visual content, wherein all encoders project their respective inputs into a shared semantic embedding space maintained in the semantic representation repository.
10 . A method for adaptive semantic routing of communication for generative artificial intelligence (AI) systems, the method comprising:
receiving at a semantic router, an incoming communication requiring AI processing, wherein the incoming communication comprises at least one of text data, audio data, image data, or video data;
encoding the incoming communication into a dense embedding vector within a multi-dimensional semantic embedding space using a neural network encoder, wherein the dense embedding vector captures semantic meaning of the incoming communication;
determining a similarity score between the dense embedding vector and a plurality of cached semantic objects stored in a semantic representation repository, wherein each of the cached semantic data records comprises an intent identifier, a semantic vector, an associated response, and a quality metric computed from historical evaluation data associated with prior responses;
responsive to the similarity score exceeding a first confidence threshold, retrieving a cached response from the semantic representation repository;
responsive to the similarity score falling below a second confidence threshold, transmitting the incoming communication to the external generative artificial intelligence service for generating a response; and
responsive to the similarity score falling between the first confidence threshold and the second confidence threshold, generating a composite response by combining one or more cached response segments retrieved from the semantic representation repository with dynamically generated response content from an external generative artificial intelligence service;
wherein the first confidence threshold and the second confidence threshold are dynamically adjusted based on response quality feedback and control selection between cached response retrieval, hybrid response generation, and external generative artificial intelligence processing.
11 . The method of claim 10 , wherein transmitting the input communication to the external generative artificial intelligence service, further comprises the steps of:
receiving a generated response from the external generative artificial intelligence service;
computing a token cost associated with the generated response;
evaluating a quality score for the generated response, wherein the quality score is computed based on user feedback, conversation continuation metrics, task completion indicators, or semantic coherence measures; and
responsive to the quality score exceeding a promotional threshold, storing the generated response and corresponding semantic vector in the semantic representation repository for subsequent semantic matching.
12 . The method of claim 10 , wherein storing the generated response comprises the steps of:
indexing the semantic vector for similarity search;
associating the generated response with the indexed vector; and
recording quality metadata for future cache promotion decisions.
13 . The method of claim 10 , wherein generating the composite response comprises the steps of:
retrieving a template response from the semantic representation repository, wherein the template response comprises static segments and dynamic slot identifiers;
generating dynamic content for each dynamic slot identifier using a constrained generation process; and
assembling the composite response by combining the static segments with the generated dynamic content.
14 . The method of claim 10 , wherein to dynamically adjust the first confidence threshold and the second confidence threshold, the plurality of programming instructions, when executed by the one or more processors, causes the one or more processors to:
monitoring cache hit rates and response quality feedback for every response generated;
adjusting at least one of the first confidence threshold or the second confidence threshold based on observed cache hit rates, response quality metrics, and cost-performance tradeoffs.
15 . The method of claim 10 , wherein adjusting the first confidence threshold and the second confidence threshold comprise the steps of:
computing a quality delta between observed quality scores and target quality thresholds;
responsive to the quality scores exceeding a target quality threshold, lowering at least one confidence threshold using the quality delta, wherein lowering the confidence threshold increases cache hit rates; and
responsive to the quality scores falling below the target quality threshold, raising at least one confidence threshold using the quality delta, wherein raising the confidence threshold improves response accuracy.