The core of DAKSH’s intelligence lies in a hierarchical neural architecture meticulously designed to capture linguistic nuance, contextual dependencies, and structural intent. The model is composed of specialized blocks that work in tandem to transform inputs into highly accurate, contextually grounded, and schema-conforming outputs — while remaining efficient enough for deployment across serverless and GPU-accelerated environments.
At the base layer is the Input Embedding and Positional Encoding module. Here, incoming token sequences — derived from DAKSH’s proprietary tokenizer — are transformed into dense vector representations. These embeddings incorporate both token identity and positional context using a hybrid of relative and absolute encoding techniques. This ensures the model understands the sequence, structure, and emphasis of each component, which is essential for tasks like document understanding, conditional question answering, or hierarchical reasoning.
Above this lies the Encoder Stack with Retrieval Fusion. This component is responsible for processing the user’s query alongside externally retrieved context (via DAKSH’s vector search engine). Using multi-head self-attention and cross-attention mechanisms, the encoder stack integrates retrieved chunks in a gated and relevance-weighted manner. A custom scoring module assigns contextual weight to each chunk, allowing the encoder to focus attention on the most relevant segments and de-emphasize peripheral or noisy content.
Once encoding is complete, the Decoder and Generator component takes over. This autoregressive module generates the response token-by-token while incorporating schema-constrained guidance. Unlike generic LLMs, the decoder includes logic to conform output to structured templates — whether the target format is a JSON object, tabular summary, FAQ list, or regulatory clause. This ensures output reliability, especially in automation-dependent use cases.
Finally, the Structure-Aware Output Filter acts as a validation layer. It soft-constrains the output to comply with enterprise-defined formats by using syntactic and semantic checks. If an anomaly or schema mismatch is detected, the generation is either corrected or gracefully rerouted.
While internal architectural specifications — such as the number of layers, attention head dimensions, or training curves — are proprietary, the system is optimized for high-accuracy, low-latency inference. This makes it well-suited for scalable cloud deployments, edge inference, and integration into time-sensitive enterprise workflows.