Cognitive Prosthetics Architecture: The Evolution of Proxy Apps and the Future of Digital Interaction

Introduction: The Paradigm Shift from Direct Access to Intellectual Mediation

The modern technology industry stands at a bifurcation point comparable in scale and consequence to the transition from command-line interfaces to graphical user interfaces (GUI) at the end of the last century. We are witnessing the sunset of the "Direct-to-Model" era—where human interaction with Artificial Intelligence (AI) was reduced to typing text into a chatbot's empty line—and the dawn of a new, far more complex and nuanced architectural paradigm: Proxy Apps.

This report, prepared by the Data Nexus analytics department, presents a fundamental study of the Proxy App phenomenon. It is based on the thesis that the current "human–prompt–model" interaction model is an evolutionary dead end for interfaces, creating excessive cognitive load and failing to provide the reliability required for mission-critical business processes and mass consumer adoption.

The industry has long been held captive by a technological illusion: it was assumed that access to a universal "oracle" (Large Language Model — LLM) via a chat interface would democratize intelligence and solve the problem of complexity. However, reality proves the opposite: the universal "Blank Slate" interface, popularized by ChatGPT, requires prompt engineering skills, deep contextualization, and constant fact verification from the user—competencies for which the mass market and corporate sector have proven unprepared.

In this context, a Proxy App acts not merely as a graphical add-on or "wrapper," but as a "cognitive prosthesis" or exo-cortex. It is a middleware layer that transforms fuzzy, multimodal user intents into deterministic actions, manages state, ensures security through neuro-symbolic validation, and visualizes results in a human-friendly form, abstracting away the stochastic nature of the underlying models.

This document deconstructs the anatomy of Proxy Apps, analyzes the reasons for their explosive growth, conducts a detailed comparative analysis with classical software development, and examines the architectural patterns necessary to build reliable systems in the age of probabilistic computing.

Chapter 1. Definition and Conceptual Essence of Proxy Apps

1.1. The Ontology of a Proxy App: More Than an Application

A Proxy App is a new class of software functioning as a specialized orchestration layer between the user's biological mind and the stochastic engine of a large language model. In this architecture, the LLM is viewed exclusively as the "engine"—a source of computational energy and semantic processing, devoid of intentionality. Meanwhile, the Proxy App acts as the "vehicle"—a complete product equipped with steering, safety systems, contextual memory, and a specific purpose.

The fundamental difference between a Proxy App and traditional applications or chatbots lies in its hybrid nature, uniting the determinism of code with the probability of neural networks:

1. vs. Classical Apps: Classical software is deterministic. Its logic is built on rigid "If X, then Y" imperatives defined by the developer for all possible scenarios. A Proxy App operates with probabilities but confines them within strict business rules, allowing it to generate solutions for situations not foreseen during coding, yet within permissible boundaries.

2. vs. Chatbots: A chatbot (Chat UX) relies on linear dialogue and short-term session memory. A Proxy App relies on State and Action. It conceals the prompting process behind a dynamic graphical interface (Generative UI) and manages context invisibly to the user, ensuring data persistence and experience continuity.

Philosophically, a Proxy App realizes the concept of "Cognitive Prosthetics." If glasses are a prosthesis for vision, compensating for optical flaws, a Proxy App is a prosthesis for thinking. It compensates for the limitations of human working memory, narrow attention focus, and the inability to instantly analyze heterogeneous data arrays. It assumes the "cognitive friction" of translating an abstract intent into a concrete result.

1.2. The "Topology of Truth" Architecture

At the core of advanced Proxy Apps lies a concept we at Data Nexus define as the "Topology of Truth." This is an architectural approach that acknowledges the fundamental limitation of generative AI: a "pure" neural network (LLM) can generate plausible text but lacks an understanding of the structure of truth or physical reality. It operates on the statistical probability of the next token, which inevitably leads to hallucinations in the absence of external control.

A Proxy App solves this by uniting three functional layers into a single system:

• Perception Layer (Neural): Responsible for intuition, recognizing unstructured patterns (vision, audio), and generating hypotheses. This layer works quickly and heuristically, similar to System 1 in the human brain.

• Reasoning & Rules Layer (Symbolic): Responsible for logic, fact-checking, and adherence to physical, legal, or corporate laws. This is the layer of deterministic rules, ontologies, and mathematical constraints (Physics-Based Priors). It acts as System 2, validating the neural layer's hypotheses.

• Execution Layer (Agentic): Responsible for executing actions in the external world (API calls, bank transactions, IoT control). This layer turns verified information into reality modification.

Thus, a Proxy App is not just an interface, but an operating system for behavior that translates chaotic and multimodal human intents into structured, verified, and secure digital actions.

Chapter 2. Reasons for the Boom: Market, Psychological, and Technological Drivers

The explosive growth in popularity and investment appeal of Proxy Apps in 2024–2025 is not accidental. It is driven by the convergence of several fundamental factors: from user neuropsychology to the economics of cloud computing.

2.1. The "Blank Line" Crisis and the Phenomenon of Cognitive Friction

The main barrier to the mass adoption of Generative AI (GenAI) beyond the circle of enthusiasts is cognitive friction. A user opening ChatGPT or a similar tool faces the "Blank Slate Problem." To get a useful result, they must perform complex intellectual work:

1. Formulation: Transform a vague sense of need (e.g., "something is wrong with my budget") into a clear, structured linguistic request.

2. Contextualization: Manually input necessary data the model doesn't know (income, expenses, goals, constraints) or upload relevant documents.

3. Validation: Critically evaluate the received answer for hallucinations, logical errors, and relevance.

The mass user is subject to behavioral inertia. They do not want to become a "prompt engineer" or spend cognitive resources training a machine. They seek solutions with minimal effort. Proxy Apps remove this barrier by inverting the interaction: they replace active formulation (writing) with passive selection or multimodal capture.1 Instead of writing a complex prompt like "Create a workout plan considering my meniscus injury and available dumbbells," the user simply points a camera at the equipment, and the Proxy App, already aware of the injury from the profile, generates the plan automatically.

2.2. Niche Fragmentation and the "Storefront Effect"

Universal models (General Purpose LLMs) like GPT-4 are good at everything to some degree, but perfect at nothing specific. Fitness requires deep knowledge of biomechanics and periodization; law requires knowledge of local jurisdiction and precedents; finance requires mathematical precision and compliance.

The market is moving from the "one super-app for everything" paradigm to the "thousands of apps for every pain" paradigm. Proxy Apps allow for the rapid and effective creation of specialized solutions (Vertical AI) for micro-niches: Type 1 diabetes management, CFA Level II exam prep, or commercial lease analysis in a specific state.

This creates the "Storefront Effect": App Stores and corporate catalogs fill with a new class of products that solve specific user problems "out of the box" better, faster, and more reliably than a universal chatbot. The Time-to-Market for such products is radically faster, stimulating a supply boom.

2.3. Declining Cost of Intelligence as a Commodity

Previously, creating a "personal financial advisor" or "legal assistant" function required a complex algorithmic base, huge R&D budgets, and years of development. With the advent of accessible model APIs (GPT-5.2, Claude 4.5 Sonnet, Gemini 3 Pro), the cost of "intelligence" as a raw material has fallen to near zero.

The primary product value has shifted from algorithms (which became a commodity) to:

• Context: Accumulated data about the user and their history.

• Interface: Interaction convenience and workflow integration.

• Trust: Guarantees of security and accuracy.

Proxy Apps capitalize on this shift, focusing on UX, data integration, and orchestration rather than training their own foundational models. This makes development accessible to thousands of startups and internal company teams.5

2.4. Need for Verification and Corporate Governance

For business, using "naked" LLMs is often unacceptable due to hallucination risks, unpredictable behavior, and confidential data leakage. Corporate clients demand guarantees.

Proxy Apps provide the necessary Governance layer:

• They filter input data, removing personal information (PII Redaction) before sending it to the cloud.

• They check output data against internal policies and safety standards (Guardrails).

• They ensure a deterministic response format (Structured Outputs), suitable for direct integration into corporate ERP and CRM systems, which is impossible with a standard chat interface.

Chapter 3. Comparative Analysis: Proxy Apps vs. Classical Development

The transition to Proxy App architecture changes not only the user experience but also the economics, processes, and methodology of software creation. Below is a detailed comparative analysis by key metrics.

3.1. Architectural Paradigm and Logic

3.2. Budgets and Cost Structure (CapEx vs. OpEx)

In classical development, the main costs (CapEx — Capital Expenditures) occur at the initial stage: prolonged design of all scenarios, drawing hundreds of screens, writing and debugging complex business logic.

In the Proxy Apps model, the cost structure changes radically:

• Lower CapEx: No need to code every logic branch. Orchestration via LLM replaces thousands of lines of imperative code. A complex product MVP can be assembled by a compact team of 3-4 people (Backend, Frontend, AI Engineer/Prompt Architect) in weeks, not months. This democratizes access to complex software creation.

• Higher OpEx: A new, significant operational expense item appears — Inference Cost (token cost). Every user action, every model request costs money. However, with falling API prices (e.g., efficient models like GPT-4o-mini or Claude Haiku), these costs become marginally efficient and predictable.

• QA Focus Shift: Testing budgets shift from manual interface checking to creating Automated Evaluation Systems (Evals). Instead of QA engineers clicking buttons, algorithmic quality assessment processes are required to check model responses for regression and jailbreak resistance.

3.3. Timelines and Time-to-Market

• Classical: Development cycles are measured in months (3-6 months to MVP). Any business logic change requires code changes, recompilation, backend redeploy, and app store updates, taking days or weeks.

• Proxy App: Iteration cycles are measured in days or even hours. Changing behavior logic often boils down to updating the system prompt, adding examples to the Few-Shot set, or updating the knowledge base (RAG). This can be done on the fly, without re-releasing the app binary (Over-the-Air updates for logic). This ensures unprecedented flexibility and speed in adapting the product to market changes and user feedback.

Chapter 4. Technical Architecture: Four Layers of Truth

Creating a reliable Proxy App requires abandoning the primitive "Frontend -> API -> LLM" scheme. Professional architecture must be multi-layered, ensuring strict control, deep context, and data security. Analysis of advanced solutions highlights four key architecture layers.

4.1. Intake & Intent Resolution Layer

These are the "senses" of the application. At this stage, traditional text input is replaced by multimodal context capture.

• Capture UI: The app actively reads reality, minimizing user effort.

• In a fitness app (like TrAIner), it's the camera recognizing machine types and weights.

• In a finance tracker, it's parsing bank SMS or scanning receipts.

• In a personal assistant (TwinMind), it's a microphone working in "Always-on" mode (with local on-device processing for privacy), capturing the day's verbal context.

• Intent Resolution: The incoming "raw" signal (audio, video, text) passes through a specialized classifier. This can be a lightweight model (e.g., BERT, DistilBERT, or distilled Llama 3 8B) working with minimal latency. Its task is to determine what exactly the user wants before launching a heavy and expensive generative model.

• Security Mechanism: If the intent is defined as high-risk (e.g., "Medical complaint about acute pain" or "Legal request with signs of crime"), the system activates a strict security protocol (Hard Guardrails) and redirects the user to a specialist or issues a stub, without contacting the LLM at all. This is a critical first-level filter.

4.2. Context Assembly Layer (RAG)

An LLM without context is a genius with amnesia. The contextualization layer provides the application with memory and domain knowledge.

• Context Builder: This module forms the "context package" for the request. It's not just "chat history." It is a dynamic assembly including:

• User profile (Preferences, Goals).

• Relevant document fragments from the vector database (Retrieval-Augmented Generation — RAG).

• Current sensor data (time, geolocation, heart rate, weather).

• Context Filtering: To avoid Context Window Overflow and quality degradation due to the "Lost in the Middle" effect, the system aggressively filters data, keeping only mission-critical facts.

• Grounding: A mechanism for anchoring generation to reliable sources. The Proxy App technically forces the model to cite specific documents when forming an answer, rather than generating facts from its weights. This turns a probabilistic answer into a verifiable one.

4.3. Orchestration & Routing Layer

This is the system's "brain," making the managerial decision of who (which model or agent) and how the task will be executed. This layer abstracts business logic from specific AI providers.

• LLM Gateway: A single gateway through which all requests pass. It allows hot-swapping models (e.g., switching from OpenAI to Anthropic during an API outage) without changing app code.

• Dynamic Routing Strategies:

• Latency-Based: The request is sent to the provider with the lowest response time (Time To First Token) in the region. Critical for voice interfaces.

• Cost-Based: Simple tasks (classification, date extraction, summarization) are routed to cheap models (GPT-4o-mini, Haiku). This is the basis of Proxy App unit economics.

• Complexity/Quality Waterfall: The system first tries to solve the task with a cheap model. If the confidence score is low, the task is escalated to a more powerful and expensive model (GPT-5.2, Claude 4.5 Sonnet).

• Semantic Routing: Vector analysis of the request determines its topic. Coding tasks go to coding-optimized models (DeepSeek Coder, Claude); creative writing tasks go elsewhere.

• Privacy-Aware Routing: Requests containing sensitive data are routed to local models (On-premise Llama) or a secure perimeter (Azure OpenAI), bypassing public APIs.

• Semantic Caching: If a user asks a question semantically close to one already answered (even if phrasing differs), the gateway returns the stored answer from the vector cache. This reduces latency and costs by 80-90% for frequent queries.

4.4. Unification & Output Layer

The final layer transforms probability into strict data structure suitable for use in software code.

• Structured Outputs: Using constrained decoding mechanisms (e.g., JSON Schema Enforcing) guarantees the model generates valid JSON matching the defined schema, not free text. This is critical for integration: the app must know exactly where the "transaction amount" is and where the "category" is in the response.

• Self-Correction Loop: If, despite constraints, the model returns broken JSON or violates a logic rule, the post-processing layer intercepts the error, generates a validation error message, and sends it back to the model with a request to fix the structure. This process happens invisibly to the user, ensuring reliability.

• Unified Interface: Standardizing responses from different models (which may have different API formats) to a single internal standard. The app frontend shouldn't know which specific model worked under the hood—it receives a unified data object.

Chapter 5. Security and Governance: The "Privacy Airlock" Pattern

Integrating AI into corporate environments or handling sensitive personal data (health, finance) is impossible without security guarantees. The Proxy App implements the Privacy Airlock architectural pattern—an isolated perimeter that filters data before it leaves the application.

5.1. Multi-Level Anonymization (PII Redaction Pipeline)

Simply transmitting user data to the OpenAI cloud is unacceptable for banks or medical clinics due to regulations (GDPR, HIPAA). The gateway implements a PII (Personally Identifiable Information) processing pipeline:

1. Detection:

• Regex Filters: Instant detection of structured data (credit card numbers, SSNs, emails, phones).

• NER Models (Named Entity Recognition): Using NLP models to detect contextual entities (names, company names, physical addresses) that regex cannot catch.

2. Transformation:

• Masking: Replacing data with stubs ([NAME]). Safe, but can destroy context for the model.

• Synthetic Replacement: An advanced method. Real data is replaced with realistic fakes (using libraries like Faker). "John Doe, balance $1,000,000" becomes "Alex Smith, balance $500,000." The model analyzes the situation on fake data, maintaining context understanding (that it's a person and money) but without access to the truth.

3. Re-identification: Upon receiving the response from the model, the gateway performs a reverse swap of tokens or synthetic data to real values before showing them to the user. The model provider (OpenAI/Anthropic) never sees the client's true data.1

5.2. Guardrails

Guardrails are programmable safety rules acting at the model's input and output to ensure compliance with ethical and business norms.

• Input Rails: Blocking "jailbreak" attempts where a user tries to manipulate the prompt to bypass model restrictions.

• Output Rails: Checking the generated response for toxicity, bias, and domain policy compliance. For example, a financial advisor (Robo-advisor) must not give medical advice or guarantee investment returns.

• Topical Rails: Keeping the dialogue within the defined topic. If a user starts discussing politics with a banking support bot, the Guardrail gently steers the conversation back to banking services.

Chapter 6. TrAIner Case Study: Proxy App Biomechanics in Action

To illustrate how this architecture works in practice, let's examine a detailed breakdown of the TrAIner case—a next-generation fitness application. This example demonstrates the power of Proxy Apps in the B2C segment, where context, personalization, and physical safety are critical.

6.1. The Classical Approach Problem

In a classical fitness app, the user must find the exercise in a library themselves, recall their working weights from the last workout, and manually set set parameters. If using ChatGPT, the user would have to write a long, complex prompt describing their current equipment, listing injuries, and workout history. Both options create high cognitive friction and error risk.

6.2. TrAIner Solution: Step-by-Step User Flow and Architecture

The interaction process in TrAIner looks as follows:

1. Capture & Perception:

• Action: User points the smartphone camera at a machine in the gym. No text input.

• Technology: A Vision model (on the Intake layer) processes the image.

• Result: System recognizes object: "Machine: Leg Press, 45-degree angle."

2. Context Orchestration:

• Action: The system (Context Builder) instantly pulls data from "Exo-Memory."

• Data:

• Medical Profile: Meniscus tear, left knee.

• Biometrics (Apple Health): 4 hours sleep, Low HRV (Heart Rate Variability) — indicating poor CNS recovery.

• History: Last workout used 100 kg (RPE 6/10 — easy).

3. Neuro-Symbolic Inference Core:

• This is the key stage where Proxy App magic happens.

• Neural Network (System 1 - Intuition): Based on history (it was easy), the model suggests load progression: "Increase weight to 110 kg."

• Symbolic Validator (System 2 - Logic/Safety): The Physics-Based Priors layer activates. A rigid safety rule states: "If Knee Injury (Condition A) AND CNS Fatigue signs (Condition B) -> INTENSITY INCREASE FORBIDDEN. Must reduce axial load or limit amplitude."

• Conflict & Resolution: The validator blocks the neural network's proposal (110 kg) and forcibly corrects workout parameters based on the rehabilitation protocol.

4. Actionable Imperative Generation:

• Output: User sees not text advice, but an interactive task card (Generative UI):

• Exercise: Leg Press.

• Weight: 100 kg (maintained, increase blocked by safety system).

• Constraint: "⚠️ Warning: Amplitude max 60 degrees (Meniscus protection)."

• Tempo: 3-0-3 (slow, controlled).

5. Feedback Loop:

• After completing the set, the user taps one button (RPE - effort rating). This data instantly updates the context for the next set.

6.3. Case Conclusion

TrAIner doesn't ask the user "what do you want to do?", shifting responsibility to them. It says "do this," based on deep analysis of context, physiology, and safety rules. This is the essence of cognitive prosthetics: the user delegates planning, calculation, and safety control to the machine, keeping only physical execution for themselves. The system delivers a result unattainable for a "naked" chatbot.

Chapter 7. B2B and Agent Systems: Scaling Reasoning

While B2C Proxy Apps focus on convenience and personality context, in the corporate sector (B2B), their main task is solving problems of scale and complexity inaccessible to a single human.

7.1. Hebbia Matrix and Agent Swarm Technology

One of the main problems in document work is the limitation of context windows and attention. A human (and even a standard LLM) cannot effectively hold focus on 10,000 pages of legal documentation during an M&A deal.

The solution implemented in Hebbia Matrix uses Agent Swarm architecture.

The Proxy App breaks a global task ("Find all financial risks and termination conditions in 500 subsidiary contracts") into thousands of micro-tasks. Each micro-task is delegated to a separate specialized AI agent that reads a specific document.

The results of thousands of agents are aggregated and synthesized into a single summary table.

The key feature here is the Verifiable Fact Layer. Every number or statement in the final report is an active link to the source scan of the specific document. This eliminates the "black box" problem and creates the trust level necessary for billion-dollar financial decisions.

7.2. TwinMind and Exo-Memory

The TwinMind application implements the concept of continuous Exo-Memory. In corporate environments, knowledge is lost after a meeting ends. TwinMind uses an "Always-on listening" model (with local processing on Edge AI for privacy).

The system indexes the user's entire verbal and digital life: meetings, hallway conversations, read articles. This creates a "second brain" that can be queried: "What did I agree on with the logistics partner at last Tuesday's meeting? What deadlines did we discuss?". The Proxy App acts as an external hard drive for the brain, compensating for natural forgetfulness and cognitive biases, increasing employee productivity.

Chapter 8. Neuro-Symbolic AI: Uniting Intuition and Logic

The future of Proxy App architecture lies in the development of Neuro-Symbolic AI (NeSy). This approach aims to eliminate fundamental flaws of pure deep learning.

Pure neural networks (Deep Learning / LLM) offer powerful intuition, generalization, and fuzzy data handling, but have weak logic, lack causal understanding, and cannot perform precise calculations.

Symbolic systems (classical code, mathematical models) offer perfect logic and precision, but zero flexibility and inability to handle the "noisy" real world.

8.1. Hybrid Architecture (System 1 + System 2)

A Proxy App implements a cognitive architecture analogous to the human brain (System 1 and System 2 by Daniel Kahneman):

• LLM (System 1): Used to translate user's fuzzy intent into a formalized plan or hypothesis.

• Symbolic Engine (System 2): Used to verify this hypothesis.

• Example: An engineer asks the system "Calculate a bridge design for this canyon." The LLM generates a design (drawing). This design is passed to the symbolic layer (physics engine), which tests it for structural integrity using strength of materials equations. If the bridge collapses in simulation, the LLM receives an error signal (penalty) and redesigns it.

8.2. Physics-Based Priors

Embedding laws of physics, logic, and jurisprudence as rigid constraints (Priors) prevents hallucinations. We don't try to "teach" the model physics via examples (which is unreliable); we forbid it from violating physics laws at the architectural level. This creates systems that "understand" the world not statistically (as a set of probable words) but structurally (as a set of interacting objects). Data Nexus calls this approach the "Topology of Truth"—truth has a structure that cannot be violated.

Chapter 9. Success Factors and Risks: The Human Factor

9.1. Critical Success Factors

Market analysis highlights factors distinguishing successful Proxy Apps from failed "wrappers":

1. Frictionless: A successful Proxy App must require less effort than a direct prompt in ChatGPT. The principle "Capture > Prompt" is decisive. If it's easier for the user to type in chat than open your app, you lose.

2. Context Ownership: Product value is not in the model used (it's the same for everyone via API), but in the accumulated user context (State). The more the app knows about the user (history, habits, documents), the higher the switching cost and the more accurate the "prosthesis" works.

3. Trust and Security: In B2B, trust is more important than "smartness." Implementing Privacy Airlock, deterministic outputs, and source linking is a mandatory market entry condition.

4. Vertical Integration: Winners are vertical solutions deeply integrated into specific niche processes (LegalTech, MedTech, FinTech) knowing domain specifics, not horizontal tools "for everything."

9.2. Risks, Challenges, and Ethical Dilemmas

Implementing cognitive prostheses brings not only benefits but also serious risks requiring attention.

1. Cognitive Atrophy and Debt: MIT Media Lab studies ("Your Brain on ChatGPT") show alarming results. Groups actively using AI for tasks demonstrate reduced neural activity in brain areas responsible for critical thinking and memory. A "Cognitive Debt" effect arises: after removing the AI assistant, users perform worse than those who never used it. The brain, accustomed to the "exoskeleton," loses tone.

• Solution: Implementing "Cognitive Scaffolding." App design shouldn't replace the human entirely but support their development. The system should ask metacognitive questions ("Why did you choose this solution?"), require confirmation for critical actions, and gradually reduce assistance levels as user competence grows.

2. Epistemic Paternalism: Proxy App algorithms begin filtering reality for the user, deciding what info to show and what to hide (for "safety" or "brevity"). There is a risk of losing human agency and falling into an algorithmically constructed information bubble.2

3. The Deference Problem: Users tend to over-trust the machine, even when it errs (Automation Bias). A Proxy App must be able to signal its uncertainty and explicitly hand over control to the human in ambiguous situations.

Conclusion: Homo Syntheticus and the Operating System for Behavior

The analysis confirms that the emergence of Proxy Apps is not a temporary marketing trend but a logical and inevitable stage in digital interaction evolution. We are moving towards the concept of Homo Syntheticus—a synthetic human whose cognitive abilities are inextricably woven, expanded, and enhanced by reliable digital architecture.

The Proxy App effectively becomes the operating system for human behavior. It resolves fundamental contradictions of the Generative AI era:

• The contradiction between neural network probability chaos and the need for business and life determinism.

• The contradiction between universal model power and the need for deep, narrow specialization.

• The contradiction between the desire for cognitive comfort and the necessity of maintaining control and safety.

For Data Nexus and the entire tech industry, this means a strategic focus shift: from an arms race in training ever-larger models to the engineering and design of cognitive resonance architectures. The future belongs not to those who create the "smartest" model in a vacuum, but to those who create the most reliable, convenient, and safe cognitive prosthesis capable of seamlessly integrating artificial intelligence into the complex fabric of biological and social human life.

Table: Evolution of Interaction Paradigms

If you are building a Proxy App, do not start with the model. Start with the system: intent, state, validation, and execution. Data Nexus designs decision architectures that make LLMs reliable in production. If you want us to review your concept or blueprint the Proxy layer, reach out.