FairSight: AI Safety & Observability Platform (Prototype PRD)
Overview and Vision
FairSight is a hybrid SDK-and-backend platform that transparently augments OpenAI API usage with real-time safety, bias, and compliance checks. The FairSight SDK drops in as a wrapper around OpenAI’s standard API calls (e.g. ChatCompletion.create), adding automated logging, moderation, bias/fairness tests, and response correction. The backend Guardian API analyzes content, scores outputs, and stores telemetry for a live dashboard. This end-to-end system ensures trust and transparency in LLM-driven applications by detecting unsafe content and intervening automatically. Enterprises adopting generative AI increasingly emphasize explainability and risk management – for example, 40% of AI decision-makers cite explainability as a key adoption risk[1], and new regulations (like the EU AI Act) require detailed transparency about model decisions[2]. FairSight addresses these demands by providing audit logs, explanations, and policy enforcement for every API call.
User Personas & Use Cases
Application Developer: Integrates FairSight to shield their app from unsafe or biased LLM outputs. They require a drop-in SDK that mimics openai.ChatCompletion.create but returns safety metadata alongside responses. Developers use FairSight to automatically log all LLM interactions (for debugging and compliance) and to receive safe output alternatives when content is flagged. In practice, a developer writes existing OpenAI calls; the SDK intercepts responses and either returns them with safety scores or blocks/rewrites them as configured.
AI/ML Operations Team: Manages deployed LLMs and monitors system health and fairness. They configure FairSight policies, review aggregated metrics, and investigate anomalies. For example, an ML Ops engineer might use the dashboard to see trends in toxicity scores or fairness over time, adjusting model prompts or thresholds. FairSight’s observability data (metrics, logs, and incident details) helps identify issues like model drift or demographic bias in outputs.
Compliance/Risk Officer: Ensures outputs meet regulatory and corporate policy standards. They use FairSight’s dashboard to audit flagged incidents (e.g. hate-speech or bias cases) and generate compliance reports. For instance, a compliance officer might view a real-time incident feed or historical logs to verify that content adhered to guidelines. FairSight provides explanations for flagged content and records all actions, supporting requirements of transparency laws (e.g. being able to “explain the logic behind decisions” as mandated for high-risk AI systems[2]).
Each persona benefits from FairSight’s fusion of automated safeguards and human-interpretability. Developers and AI teams get minimal friction integration, while compliance and executives gain governance controls.
Functional Requirements
FairSight SDK (Client Library)
API Compatibility: Functions identically to openai.ChatCompletion.create (and related OpenAI calls). The SDK exposes the same methods and parameters as OpenAI’s API, so integration requires minimal code change.
Safety-Enhanced Responses: The SDK intercepts responses and appends safety metadata. For example, the return value includes fields like toxicity_score, bias_flags, jailbreak_flag, and a list of moderation categories triggered (e.g. hate/self-harm/violence)[3]. If content is flagged, the SDK can either raise an exception, drop the output, or supply a remediated_response (see below).
Automated Remediation: When a response violates a safety policy, the SDK can automatically re-query the LLM (or a smaller detox model) to produce a sanitized version. For instance, if profanity is detected, it may issue a prompt like “Re-write this response more respectfully.” This implements text detoxification (removing toxicity while preserving meaning) as described in GPT-Detox and related work[4]. The final returned object indicates if a rewrite was applied.
Logging: Every API call (request and response) is logged. The SDK asynchronously sends log data (prompt text, response text, model, user/tenant ID, timestamps, token counts, etc.) to the Guardian backend via a /log or /analyze endpoint. This ensures auditability without blocking user threads.
Configurable Enforcement: Developers can set policies on the fly (e.g. thresholds for toxicity, which categories to enforce). The SDK accepts options like safety_level and on_flag (values: “strict” abort, “auto-correct”, “warn-only”) to control behavior when a violation occurs.
Minimal Overhead: To meet latency requirements, the SDK performs lightweight checks locally (e.g. simple regex for obvious bad words) or calls the Guardian in parallel threads. By default it will at least call OpenAI’s Moderation API for toxicity in-band, since that endpoint is free and fast[3].
Guardian API (Backend Service)
Endpoints:
POST /v1/analysis/toxicity: Accepts text (input or output) and returns a toxicity score and triggered categories, using OpenAI’s moderation or an internal classifier.
POST /v1/analysis/bias: Performs demographic fairness checks on text. For example, it may accept a prompt and a set of demographic qualifiers, run paired queries, and return a bias_score or flags if outputs differ significantly[5].
POST /v1/analysis/jailbreak: Analyzes prompts/outputs for instruction-hijacking or jailbreak patterns (e.g. “Ignore previous instructions…”). Returns a boolean jailbreak_flag.
POST /v1/analysis/explain: Given flagged content, returns an explanation (e.g. “the word ‘stupid’ triggered the insult category”). It may highlight offending spans and map them to violation categories (span-based annotation).
POST /v1/analysis/remediate: Takes an unsafe output and returns a safe reformulation (using a detox model or LLM prompt engineering).
POST /v1/log: Ingests raw API call logs from the SDK (prompt, response, user/tenant ID, timestamp, metadata).
GET /v1/metrics: Returns aggregated metrics (e.g. counts, averages) per tenant or global.
GET /v1/incidents: Queries flagged incidents (filters by date, severity, category).
Security and Multi-tenancy: All endpoints require authentication (API keys or OAuth tokens) and a tenant_id to segregate data. Each tenant’s data is siloed (no sharing across organizations)[6]. Role-based permissions allow developers to submit data, while admins/compliance roles can view and configure tenant data.
Scaling and Reliability: The Guardian API is designed as stateless microservices behind a load balancer. Services can autoscale (e.g. Kubernetes pods or serverless functions) under load. Critical analysis (toxicity, bias) is implemented as lightweight microservices (for example, callouts to small in-house models or GPU instances), to keep latency low[7][8].
Analysis Engine (Safety & Observability Services)
Toxicity Detector: Utilizes OpenAI’s Moderation API or a similar text classification model. It checks each output for categories like hate, harassment, self-harm, violence, sexual content[3] and returns a toxicity_score (0–1) plus a boolean flagged status. This score and categorical breakdown are logged.
Demographic Bias Flagging: Implements paired-prompt fairness tests. For example, the engine can take a source prompt and variant prompts with demographic terms swapped (e.g., “doctor” vs “female doctor”) and compare LLM outputs. If changes exceed a threshold, it raises a bias_flag. This mirrors approaches like Meta-Fair which ensure that adding a demographic qualifier should not change factual content[5]. In practice, this might require multiple LLM calls per check, so it can be run asynchronously (e.g. sampling responses in the background) and logged when differences emerge. The output includes a bias_score indicating consistency.
Prompt Injection & Jailbreak Detection: Scans input prompts and candidate outputs for signs of instruction bypass (e.g. “ignore previous instructions”, hidden URLs, code execution requests). This may use pattern matching or a specialized classifier. A true detection sets jailbreak_flag = true and the request can be blocked or rewritten. NVIDIA’s Guardrails includes a dedicated jailbreak service for this purpose[9].
Explainability: When any flag triggers, the engine generates an explanation. At minimum, this highlights spans of text that caused the issue (e.g. “The phrase ‘hurtful term’ triggered the harassment policy”). It may also produce a short natural-language justification (using the LLM as a “judge”) describing the problem. This helps users understand why content was flagged, meeting governance needs for transparency[1][2].
Automatic Remediation: For flagged outputs, a remediation subroutine tries to produce a safe version. Techniques include prompt-based rephrasing (similar to GPT-Detox and style-transfer models) that “clean” the text while preserving intent[4]. The original and remediated outputs are both logged, and the SDK returns the safer variant. For example, profanity may be replaced with polite synonyms, or a biased statement rewritten neutrally. The engine also logs a response_modification flag indicating the action taken.
Dashboard & UI (Real-Time Observability)
Real-Time Metrics Dashboard: Visualizes key system metrics by tenant and time. Expected widgets include:
Toxicity Over Time: Line/bar chart of average toxicity score or percentage of flagged outputs per hour/day.
Bias Incidents: Count or ratio of biased vs. neutral outputs (from paired-prompt tests).
Latency: Average API round-trip time (including FairSight overhead) and distribution. (This is tracked to verify the <200ms overhead target).
Usage and Billing: Safety Inference Units (SIUs) consumed over time vs. credit budget. This functions like OpenAI’s credit usage dashboard[10], but for safety analyses.
Incident Feed: A live-updating table of flagged incidents (time, user/tenant, category, severity). Each row links to details.
Incident Drill-Down: Clicking an incident shows full context: original prompt, the raw response, safety labels (toxicity categories, bias notes, jailbreak alerts), and the explained spans. If remediation occurred, both the original and safe versions are shown side-by-side, along with a note of the rewrite. This detail helps compliance officers audit exactly what happened.
Role-Based Views:
Developer View: Focuses on their own API usage, with filters by date or model. Developers can tag incidents as “false positive” or add comments, feeding back into system tuning.
Admin/Compliance View: Can see aggregate metrics across teams or global, set organization-wide policies (like toxicity thresholds), and manage user/tenant access. They can also export reports (e.g. monthly summary of flagged content for regulators).
Wireframe Concept: The main dashboard page has top navigation (tenant selector, timeframe controls) and summary stats. Below, left panel: time-series charts (toxicity, bias, latency), right panel: incident list table. Incident detail is a modal or separate page with text annotations. The UI updates in near-real-time (e.g. via WebSockets) as new data arrives.
Metrics and Logging per Call
Every FairSight API invocation logs a rich set of metrics. Key fields per call include:
toxicity_score (float): 0–1 indicating the degree of toxic/harmful content as computed by the moderation endpoint.
toxicity_categories (list of strings): Which moderation categories (e.g. “hate”, “self-harm”) were flagged[3].
bias_score (float or flags): A measure (or boolean flag) of demographic bias detected by paired-prompt tests[5].
jailbreak_flag (boolean): True if the output or prompt triggered a jailbreak/injection rule.
response_modification (enum): e.g. “none”, “rephrased”, “blocked” to indicate if remediation occurred.
latency_ms: Total time in milliseconds for the API call including FairSight processing (should target <200ms overhead).
tokens_input/output: Number of tokens in prompt and completion (for usage accounting).
safety_units_used: Credits consumed (e.g. 1 SIU per 100 tokens or per check). This enables credit-based billing. FairSight’s Usage & Billing treats “safety inference units” like a prepaid budget (akin to OpenAI’s credit model[10]). The dashboard will accumulate SIU usage per tenant.
tenant_id, user_id, model_name, timestamp: Identifiers for multi-tenant tracking.
All logs are stored durably (e.g. in a time-series DB or log store) for later analysis. This supports alerts (e.g. sudden spike in toxicity incidents) and downstream monitoring. Observability best practices (like those in Langfuse and Phoenix) suggest recording custom metrics such as cost, latency, and toxicity to drive alerts and dashboards[11].
Data Control, Privacy, and Multi-Tenancy
Data Isolation: Each tenant’s logs and metrics are stored in a separate namespace or database, preventing cross-tenant access[6]. This ensures privacy and meets corporate data governance. For added security, each tenant’s data can be encrypted with separate keys.
Access Control: Role-based access control (RBAC) is enforced. Tenant admins and compliance roles can access their organization’s data; developers have limited views. The Guardian API verifies the tenant_id in each request against the API key.
Anonymization & Retention: To protect user privacy, raw user prompts or PII can be optionally hashed or not persisted. Tenants may configure retention policies (e.g. “delete logs after 30 days”). All storage complies with standards like GDPR (e.g. right-to-erasure: we can purge a user’s content on demand).
Configurable Privacy Mode: Some tenants may forbid any third-party analysis of certain categories (e.g. medical or legal queries). FairSight allows disabling specific analyses (bias check, or text logging) per tenant. For example, a healthcare tenant might turn off data collection for patient queries, using only on-device checks.
Audit Logging: All administrative actions (policy changes, user management) are itself audited. This helps compliance officers demonstrate that policies were not silently changed.
Technical Architecture
FairSight is built as a cloud-native microservices system (see figure below). Key components:
Client SDK: Runs in the user’s application runtime. It proxies calls to OpenAI:
On ChatCompletion.create, the SDK concurrently sends the prompt to the Guardian API log service.
It then calls the real OpenAI API for generation.
Upon receiving the response, the SDK calls the Guardian API /analysis/toxicity (and other checks if synchronous) to classify the output, or queues it for async analysis.
If flagged and on_flag=“rewrite”, it calls /analysis/remediate and returns the safe text.
Finally, it returns to the developer both the (possibly modified) text and the collected safety metadata.
Guardian Backend: Hosted on a scalable platform (Kubernetes or cloud services). It consists of:
Analysis Services: Each safety check is a lightweight service or Lambda: toxicity classifier (calls OpenAI’s moderation endpoint), a small bias-testing engine, a jailbreak detector, and an explanation generator. Inspired by NVIDIA’s NeMo Guardrails, these are specialized microservices (portably containerized) that each handle one aspect[7][12]. By applying “multiple lightweight, specialized models as guardrails” developers can cover gaps in general policies[9]. Small models are chosen for low latency[8], e.g. a distilled classifier for hate-speech.
Storage: A time-series database for metrics (toxicity over time, latency trends), and a document store for incidents and logs. Data is partitioned by tenant.
Dashboard UI: A web application (e.g. React frontend) subscribes to a real-time stream of events (via WebSocket or SSE) and queries APIs for historical data. The UI calls backend endpoints for filtering incidents and retrieving metrics.
Auth Service: Manages tenant accounts, API keys, and permissions.
Billing Service: Tracks SIU consumption per tenant (deducts credits as calls are logged) and enforces limits.
High-Level Flow: Developer → FairSight SDK → (1) Guardian for logging, (2) OpenAI API for chat completion → FairSight SDK (receives response) → Guardian for analysis and remediation → FairSight SDK (returns final response) → Developer. Meanwhile, all data flows into databases and the UI. The system supports horizontal scaling (each microservice can scale independently).
This hybrid design (client + cloud) balances performance and power: critical checks like toxicity moderation can be done in the cloud (at some latency cost) while lighter checks could eventually run at the edge. We leverage existing infrastructure and models (initially OpenAI and possibly open-source classifiers) to minimize build time.
API Specifications
FairSight provides both a client-side SDK interface and RESTful backend endpoints. For clarity, here are key API contracts:
Client SDK Method:
FairSight.ChatCompletion.create(model, messages, safety_config=None) -> SafeCompletionResponse
Inputs: identical to OpenAI’s API (model name, messages, etc.), plus an optional safety_config dict (fields like thresholds, on_flag).
Returns: A SafeCompletionResponse object containing: the LLM’s choices (text completions) plus metadata fields: toxicity_score, bias_flags, jailbreak_flag, response_modification (e.g. "detoxified"), and any explanation. Example usage:
{  "id": "...",  "choices": [...],  "usage": {...},  "safety_scores": {    "toxicity_score": 0.92,    "toxicity_categories": ["hate", "harassment"],    "bias_flags": ["gender_stereotype"],    "jailbreak_flag": false,    "response_modification": "rephrased"  }}
Guardian REST Endpoints:
POST /v1/analysis/toxicity
Request JSON: { "text": "...", "tenant_id": "org123" }
Response JSON: { "toxicity_score": 0.85, "flagged": true, "categories": ["hate", "harassment"] }[3].
POST /v1/analysis/bias
Request: { "prompt": "...", "variants": ["prompt with female", "prompt with male"], "tenant_id": "org123" }
Response: { "bias_score": 0.15, "flags": ["gender_bias"] }. (This calls the model for each variant internally and compares results.)
POST /v1/analysis/jailbreak
Request: { "text": "...", "tenant_id": "org123" }
Response: { "jailbreak_flag": true } if an injection pattern is detected.
POST /v1/analysis/explain
Request: { "text": "...", "issues": ["toxicity"], "tenant_id": "org123" }
Response: { "explanation": "The term *\'stupid\'* triggered harassment policy." }.
POST /v1/analysis/remediate
Request: { "text": "...", "issue": "toxicity", "tenant_id": "org123" }
Response: { "remediated_text": "I disagree with that opinion.", "original_score": 0.78, "new_score": 0.12 }.
POST /v1/log
Request: Full call context (prompt, response, user ID, model, metrics).
Response: 200 OK. (This only logs data; no body.)
GET /v1/metrics (admin endpoint) returns aggregates, e.g. average toxicity or count of incidents per day.
GET /v1/incidents queries the incident store (with filters like date range, severity, category).
All API data is exchanged as JSON over HTTPS. Detailed OpenAPI/Swagger docs will be provided.
Dashboard UX and Wireframes
The FairSight web dashboard provides an at-a-glance view of safety metrics and incidents. Key UX elements include:
Overview Page: Shows overall statistics and charts. At the top, a date-picker and tenant-selector filter the view. Below are summary panels: total API calls, flagged incidents, average toxicity score, and safety credits used this period.
Real-Time Charts: Left side displays time-series graphs (toxicity, bias incidence rate, system latency) updated live. For example, a line chart of “Toxicity Score Over Time” and a bar chart of “Flagged Incidents by Category”.
Incident Table: Right side shows a table of recent flagged incidents (columns: timestamp, user/tenant ID, type [toxicity/bias/jailbreak], severity, status [e.g. “addressed” or “pending”]). Users can sort or filter by these fields. New incidents appear at the top as they occur.
Incident Detail View: Clicking a row opens a detail pane. This pane shows the original prompt and generated response. Offending text spans are highlighted (e.g. in red). Below, FairSight’s annotations list the issues: e.g. “Harassment (score 0.92), Gender bias”. If remediation was applied, it shows the “Safe Rewriting” alongside the original. An explanation text (e.g. “word ‘idiot’ is offensive”) appears below. The user can then acknowledge or override flags (e.g. mark a false positive) to adjust the system.
Usage/Billing Page: Displays credit consumption. A donut chart shows SIUs used vs remaining budget. A table breaks down SIU usage by category (e.g. 200 SIUs on toxicity checks, 50 on bias checks). This allows tenants to monitor their “safety credits” akin to OpenAI’s credit dashboard[10].
Admin Controls: Separate tab for admins to define policies (toggle specific checks on/off), manage users, and download audit reports.
Overall, the UX is designed for clarity and quick scanning. Key metrics and incidents are front-and-center, enabling developers and officers to spot trends or issues immediately.
Logged Metrics per Call
FairSight logs the following key metrics for every API call, enabling detailed monitoring and billing:
toxicity_score (float): Numeric safety score (0–1) from the moderation/classifier.
toxicity_categories (list): Any violation categories flagged (e.g. “hate”, “self-harm”)[3].
bias_flags (list): Identifiers of any detected demographic bias issues from paired tests[5].
jailbreak_flag (bool): Whether a jailbreak pattern was detected.
latency_ms (int): End-to-end latency added by FairSight (including Guardian analysis). This must average under 200ms to meet performance requirements. In practice, we may parallelize checks or skip non-critical analyses to stay below this threshold. (Designs like NVIDIA’s use small, efficient models to keep latency low[8].)
tokens_in, tokens_out (int): Number of input and output tokens for usage tracking.
safety_units (float): Number of Safety Inference Units consumed (for billing). For example, 1 SIU per 100 output tokens plus 0.5 SIU per check. The backend debits from the tenant’s credit balance. (Langfuse-like systems explicitly track cost and latency as custom metrics[11].)
response_modification (string): “none”, “rephrased”, or “blocked” – indicating if remediation was applied.
tenant_id, user_id, model_name, timestamp: For audit and multi-tenant analytics.
These are stored in a metrics/time-series database. Aggregated data (averages, percentiles) feed the dashboard graphs. Raw logs feed the incident database for the UI.
Admin & Tenant Data Controls
Data Isolation: Each tenant’s data (logs, metrics, incidents) is strictly isolated. We follow best practices for multi-tenant AI systems: each tenant’s information is stored separately and encrypted, preventing cross-tenant leakage[6].
Access Management: API keys and UI accounts are scoped by tenant. Users see only their tenant’s data. Role-based Access Control (RBAC) allows tenant admins to assign developer or compliance roles to their team, with varying privileges.
Configuration: Tenants can configure which checks are active and adjust thresholds. For instance, a tenant may disable “bias” checks if they have a use case where paired testing is irrelevant, or raise the toxicity threshold for their domain. These settings are enforced by the Guardian engine per tenant.
Privacy & Compliance: By default, FairSight does not share raw user content externally. Tenants can enable “anonymization mode” where prompt/response text is hashed before storage. All logs can be purged on request to comply with “right to be forgotten”. We use encryption in transit and at rest, and support data residency options if required. A comprehensive audit trail is maintained for all analysis actions (who changed settings, who reviewed an incident) to satisfy regulatory inquiries.
Implementation Scope & Trade-offs (3-Week Prototype)
Given the tight 3-week prototype timeline, we prioritize core functionality and defer non-essential features:
Week 1 (Infrastructure & SDK): Build the FairSight SDK as a thin proxy for OpenAI’s API. Implement asynchronous logging to a simple backend (e.g. a Node.js service with a MongoDB or SQLite store). Wire up OpenAI’s Moderation endpoint for toxicity. Return toxicity scores in the SDK. Develop the basic database schema (incidents, metrics).
Week 2 (Analysis & Dashboard): Implement the Guardian services for detection and remediation. For bias, start with a simple test (e.g. detect gendered pronouns causing change). For explainability, just highlight violating words. Build a minimal dashboard (React or simple charts) showing live incident stream and a few charts (toxicity over time, incident counts).
Week 3 (Finishing Touches): Add role-based auth (could be mocked), polish SDK error handling, and simulate credit usage. Improve UI features (filters, detail view). Conduct integration tests and a demo run with sample prompts showing safe/unsafe flows.
Key Trade-offs:- We will not build a full language-based fairness evaluation engine in prototype; rather, demonstrate with a few fixed examples. Full metamorphic testing (like Meta-Fair’s 13 relations[5]) is out of scope.- Explainability will be basic span-highlighting (full natural-language explanations are costly).- Admin features (multi-tenant onboarding, detailed audit logs) will be simplified.- To meet the sub-200ms overhead target, some checks may run async after the response is returned (e.g. deep bias tests), while immediate checks (toxicity) remain synchronous. As NVIDIA suggests, we use small models or light heuristics to keep real-time latency low[8].
This MVP will demonstrate core FairSight value: safe responses and an interactive safety dashboard. Subsequent versions can optimize accuracy, add more analysis, and flesh out billing/reporting.
Non-Functional Requirements
Latency: FairSight must add <200ms overhead to each API call on average. This is achieved by using efficient inference pipelines and asynchronous processing where possible. Benchmarks (e.g. from NVIDIA) show small guardrail models can run in <100ms[8].
Scalability: The system is horizontally scalable. The API and analysis services can run on multiple containers/instances behind a load balancer. We should use cloud autoscaling (e.g. Kubernetes or serverless functions) to handle load spikes.
Reliability: We target 99.9% uptime. Components will use health checks and auto-restart on failure. Logs and metrics about the system itself (CPU, memory, error rates) are collected for ops monitoring.
Security: All services use TLS. The backend authenticates requests via bearer tokens. Stored data is encrypted at rest. We will follow security best-practices (e.g. OWASP Top 10 mitigations for our web UI/API).
Logging & Monitoring: In addition to data logs, FairSight emits telemetry for infrastructure health (through Prometheus, Grafana, or cloud equivalents). We also log administrative actions (who changed policy, etc.) for audit.
Maintainability: The prototype will be documented and containerized. We’ll use standard frameworks and open-source libraries to avoid vendor lock-in.
Privacy Compliance: System design adheres to privacy laws. Sensitive fields (PII) in inputs/outputs should be optionally scrubbed. Tenants can opt into data anonymization.
By meeting these non-functional requirements, FairSight ensures that adding safety does not compromise performance or security, while being robust enough for enterprise demo use.
Sources: We base FairSight’s design on industry standards in AI observability and safety. For example, NVIDIA’s NeMo Guardrails demonstrates using specialized microservices for content safety and jailbreak detection[12][8]. Observability platforms like Langfuse explicitly track custom metrics (cost, latency, toxicity)[11]. OpenAI’s own tools (Moderation API, credit system) inform our approach to scoring and billing[3][10]. Finally, AI governance literature stresses data isolation and explainability as core requirements[6][2]. These principles underpin the FairSight prototype to deliver a working demo of real-time LLM auditing and a safety dashboard.

[1] [2] Building trust in AI: The role of explainability | McKinsey
https://www.mckinsey.com/capabilities/quantumblack/our-insights/building-ai-trust-the-key-role-of-explainability
[3] OpenAI’s Moderation API: A Step by Step Guide to Ensuring Safer Content with a GUI Application | by Dr. Ernesto Lee | Medium
https://drlee.io/openais-moderation-api-a-step-by-step-guide-to-ensuring-safer-content-d22a649d51ac?gi=d74fde6e6c04
[4] GPT-Detox: An In-Context Learning-Based Paraphraser for Text Detoxification
https://arxiv.org/html/2404.03052v1
[5] Meta-Fair: AI-Assisted Fairness Testing of Large Language Models
https://arxiv.org/html/2507.02533v1
[6] Chapter 13 - Multi-tenant Architecture | AI in Production Guide
https://azure.github.io/AI-in-Production-Guide/chapters/chapter_13_building_for_everyone_multitenant_architecture
[7] [8] [9] [12] NVIDIA Releases NIM Microservices to Safeguard Applications for Agentic AI | NVIDIA Blog
https://blogs.nvidia.com/blog/nemo-guardrails-nim-microservices/
[10] How to Buy OpenAI Credits: 5 Steps
https://www.getmagical.com/blog/how-to-buy-openai-credits
[11] Top 12 AI Evaluation Tools for GenAI Systems in 2025 | Galileo
https://galileo.ai/blog/mastering-llm-evaluation-metrics-frameworks-and-techniques