Enterprise AI Analysis
Interfaze: The Future of AI is built on Task-Specific Small Models
Interfaze presents a novel system for LLM applications, focusing on context building and action rather than relying solely on monolithic models. It integrates heterogeneous DNNs and small language models for perception across modalities, a context-construction layer for external sources, and an action layer with a thin controller. This approach offloads complex computation to specialized, smaller models, delivering competitive accuracy with reduced computational cost.
Key Performance Indicators & Impact
Interfaze-Beta demonstrates strong performance across challenging benchmarks by leveraging its small-model and tool stack.
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MMLU-Pro Accuracy
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AIME-2025 Score
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AI2D Diagram Understanding
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MMMU (val) Score
Deep Analysis & Enterprise Applications
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System Architecture
Perceptual Encoders
Results & Discussion
Context-Centric System Architecture
Interfaze is built on a context-centric architecture comprising four main components: an ingress stage for input normalization and safety checks, a small-model and perception stack for processing raw content across modalities, a context construction layer that aggregates and structures external information, and an action layer with a lightweight controller. This modular design ensures that large LLMs only operate on highly distilled context, optimizing for cost and efficiency.
The system avoids running a single large frontier model over raw, extensive inputs like full PDFs or long audio files, which is both expensive and brittle. Instead, small, task-specific models handle initial perception and data processing, creating a compact, structured representation of the context that is then passed to a user-selected LLM for final reasoning.
Specialized Perceptual Encoders
The perceptual stack consists of heterogeneous deep networks and small language models designed for specific tasks across various modalities. For audio processing, a multilingual ASR system with diarization transcribes segments with timestamps and speaker labels. Waveforms are converted to time-frequency representations, processed by convolutional and self-attention blocks, and decoded into subword tokens. This ensures that the large LLM never sees raw audio, only structured, annotated transcripts.
For document parsing and OCR, lightweight vision and sequence models extract word-level text, reconstruct reading order, and perform schema-guided extraction from complex multilingual inputs. This includes rasterization, detection-recognition cascades, line grouping, and fine-grained bounds correction. Object detection and GUI layout parsing further enhance visual understanding for images and interactive interfaces, combining open-vocabulary detection, segmentation, and specialized layout parsers to localize objects from natural-language prompts.
Benchmark Results & Insights
Interfaze-Beta achieves competitive or state-of-the-art results on several challenging reasoning and multimodal benchmarks. It leads on AIME-2025 (90.0%), MMLU (91.38%), and AI2D (91.51%). It also delivers strong scores on MMLU-Pro (83.6%), GPQA-Diamond (81.31%), LiveCodeBench v5 (57.77%), MMMU (val) (77.33%), ChartQA (90.88%), and Common Voice v16 (90.8%).
These results highlight that the majority of performance gains stem from the small-model and tool stack and the effective context compilation, rather than from relying solely on a larger general-purpose model. This approach demonstrates that context filtering, structuring, and budgeting are more critical than simply enlarging the model or its context window.
Enterprise Process Flow: Interfaze Architecture
User Query & Ingress Stage
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Small-Model Stack (Perception)
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Context Construction Layer (Merge & Structure)
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Action Layer (Tool Selection)
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Generalist LLM (Final Answer)
$15M
Estimated annual savings by optimizing LLM inference with Interfaze's approach to small models and context distillation.
| Feature | Monolithic LLM Approach | Interfaze (Small Model + Tools) |
|---|---|---|
| Raw Input Handling | Limited, costly for large/multimodal |
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| Context Management | Large context window, prone to noise |
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| Computational Cost | High, especially for large inputs |
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| Modality Support | Often text-centric, multimodal limited |
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| Robustness | Brittle with noisy/irrelevant data |
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Estimate Your AI ROI
Calculate potential savings and efficiency gains by adopting a specialized, context-centric AI architecture.
Your Strategic Implementation Roadmap
A phased approach to integrate Interfaze's task-specific small models into your enterprise workflow.
Phase 1: Discovery & Context Mapping
Initial consultation to understand existing workflows, data sources, and specific task requirements. Map out key contexts for small-model specialization.
Phase 2: Small Model Deployment & Integration
Deploy and fine-tune specialized DNNs and SLMs for perception (OCR, ASR, Object Detection) and initial context construction, integrating with your data pipelines.
Phase 3: Context Layer Refinement & Tooling
Develop and optimize the context-construction layer to crawl, index, and parse external and internal sources into compact, structured state. Integrate necessary action layer tools.
Phase 4: Generalist LLM & Controller Orchestration
Configure the final generalist LLM to operate on distilled context, fine-tune the action layer controller for optimal tool selection and cost efficiency, and conduct extensive testing.
Phase 5: Monitoring, Optimization & Scale
Continuous monitoring, performance optimization, and scaling of the Interfaze system across your enterprise, with ongoing support and iteration.
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