# NeuralSeek > NeuralSeek is an enterprise AI Answers-as-a-Service platform that grounds large-language-model responses in your knowledge base. It ships **118 configurable AI guardrails across 18 categories**, is **model-agnostic across 120+ LLMs from 9 providers**, and includes **18 live governance modules** spanning the LLM and agent layers — all built in, not bolted on. Born from thousands of regulated-sector go-lives. The platform has seven core surfaces: - **Seek** — query an LLM against your knowledge base with grounding, semantic scoring, and confidence enforcement. - **KnowledgeBase** — ingest, index, and govern source content (documents, URLs, structured data) across ELSER + KNN hybrid retrieval. - **mAIstro** — visual agentic workflow builder for multi-step, multi-tool, multi-model agents. - **NeuralEdit** — answer curation, intent management, and prompt-template authoring. - **Governance** — 18 live observability and audit modules at both the LLM and agent layers. - **Run Agents** — runtime execution surface for deployed agents. - **Admin Tools** — user, role, secret, and configuration management. NeuralSeek differs from generic LLM platforms in three ways: (1) it ships the full enterprise guardrail surface in the box rather than as a separate add-on, (2) it is model-agnostic by design — customers can swap the underlying LLM at the API, workflow, or platform level with a single parameter change, and (3) every guardrail change is versioned, attributed to a named user, and exportable to the customer's SIEM. ## Product - [NeuralSeek Platform Overview](https://neuralseek.com/): Hero, where NeuralSeek sits in the AI stack, healthcare agent stack example, control levels (Node · Workflow · Multi-Agent), runtime control surface, regulated go-lives, demo video, contact form. - Markdown: https://neuralseek.com/index.md - [Pricing](https://neuralseek.com/pricing): NeuralSeek plans and pricing. - Markdown: https://neuralseek.com/pricing.md - [Contact NeuralSeek](https://neuralseek.com/contact): The single lead form for demos, scoping conversations, pricing and deployment questions. Replaces the previous booking scheduler. - Markdown: https://neuralseek.com/contact.md - [The NeuralSeek Team](https://neuralseek.com/team): Founders and leadership — Marc Martina (CIO), Garrett Rowe (CEO), Robert Reed (Founding Engineer) — plus company facts: founded 2022, Miami FL, US veteran-owned, 30 and growing. - Markdown: https://neuralseek.com/team.md ## AI Guardrails & Governance - [AI Guardrails](https://neuralseek.com/guardrails): 118 individually-configurable guardrails across 18 categories — retrieval grounding, hallucination prevention, prompt injection, PII & sensitive data, answer confidence, profanity, attribute protection, intent & routing, hybrid search, LLM control, memory, multi-language, output rendering, audit & compliance, prompt engineering, secrets, model-agnostic, red team. Every one auditable per call, versioned, and attributable to a named user. - Markdown: https://neuralseek.com/guardrails.md - Definition of "AI guardrails" (vendor-neutral): https://neuralseek.com/guardrails#what-are-guardrails - Anti-hallucination guardrails (definition + four production techniques): https://neuralseek.com/guardrails#anti-hallucination - FAQ (JSON-LD FAQPage): https://neuralseek.com/guardrails#faq - [LLM Governance](https://neuralseek.com/llm-governance): AI visibility at the LLM layer. 9 runtime governance modules: live Health Dashboard, hallucination forensics with click-to-allowlist, content-performance per document, per-intent ridge-plot drift detection (1–30 day lookback), token / cost telemetry, 200+ model cost ranking on live data, SIEM-grade audit log, persistent LLM bake-offs, and Git-style version control for AI configuration. - Markdown: https://neuralseek.com/llm-governance.md - [Agent Governance](https://neuralseek.com/agent-governance): AI visibility at the agent layer. 9 runtime governance modules: 5-axis component-attribution APM (Parallel · LLM · KB · ML · REST), AI-graded red-team testing with auto-generated remediation, agent run forensics, FinOps with cache savings, agent-aware model bake-offs (Agent Score ≠ LLM Score), customizable governance dashboards, identity-aware user audit, three-tier infrastructure APM. - Markdown: https://neuralseek.com/agent-governance.md - [AI Risk Atlas](https://neuralseek.com/ai-risk-atlas): IBM AI Risk Atlas taxonomy (arXiv:2503.05780) rendered under the NeuralSeek brand. 84 named AI risks across 5 lifecycle stages (Training Data, Inference, Output, Non-Technical, Agentic) and 5 classification tiers (Traditional, Amplified by GenAI, Specific to GenAI, Amplified by Agentic, Specific to Agentic). Two-axis filter UI; risk names and descriptions sourced 1:1 from the IBM paper. - [NeuralSeek on AWS](https://neuralseek.com/aws): Running NeuralSeek on AWS — grounded conversational Q&A over corporate knowledge, streamlined AWS Bedrock LLM selection and orchestration, auto-built AWS Lex virtual agents, answer-to-source mapping, and governance built in. Available on AWS Marketplace. - Markdown: https://neuralseek.com/aws.md - [NeuralSeek on Microsoft Azure](https://neuralseek.com/azure): Running NeuralSeek on Azure — agentic AI over corporate knowledge bases, virtual agents and contact centers, with LLM selection and orchestration across the Azure ML model catalog. Available on Azure Marketplace. - Markdown: https://neuralseek.com/azure.md - [NeuralSeek on IBM Cloud](https://neuralseek.com/ibm-cloud): Running NeuralSeek on IBM Cloud — retrieval-grounded answers for IBM Watson Assistant, model orchestration including watsonx, and auditable governance. Available from the IBM Cloud catalog. Distinct from the IBM Fusion go-to-market partnership. - Markdown: https://neuralseek.com/ibm-cloud.md - [NeuralSeek & eDelta Partnership](https://neuralseek.com/edelta): Strategic partnership with eDelta Consulting delivering enterprise AI for regulated sectors — a no-code, on-premises-capable AI decisioning platform combined with 25+ years of IT audit and regulatory expertise. - Markdown: https://neuralseek.com/edelta.md - [Marketplace](https://neuralseek.com/marketplace): 80 ready-to-run NeuralSeek agent templates including guardrail enforcers, sandbox runners, RAG patterns, OCR enhancement, SQL generation, and image / video / PDF loopers — organized by Core Toolkit, Insurance, IT, Rev Ops, Sales, Marketing, and Legal. Each template deploys inside the customer's tenant against their own data, under their governance plane. ## Competitors & Comparisons - [NeuralSeek vs the Alternatives](https://neuralseek.com/competitors): Honest, web-verified head-to-head comparisons (June 2026) of NeuralSeek against other ways to build AI (watsonx Orchestrate, Azure AI Foundry, n8n, LangChain/LangGraph, Moveworks) and packaged point solutions (Microsoft 365 Copilot, Fireflies.ai, Read AI, ChatGPT Enterprise, Claude Enterprise, Glean, FortiAI). NeuralSeek's constant edge across every comparison: any LLM (swap with no rebuild), your data in your own tenant (cloud or on-prem), 118 guardrails enforced before any action, one container that runs anywhere, per-task pricing. - Markdown: https://neuralseek.com/competitors.md - NeuralSeek vs IBM watsonx Orchestrate: https://neuralseek.com/competitors/watsonx-orchestrate - NeuralSeek vs Azure AI Foundry: https://neuralseek.com/competitors/azure-ai-foundry - NeuralSeek vs n8n: https://neuralseek.com/competitors/n8n - NeuralSeek vs LangChain: https://neuralseek.com/competitors/langchain - NeuralSeek vs LangGraph: https://neuralseek.com/competitors/langgraph - NeuralSeek vs Moveworks: https://neuralseek.com/competitors/moveworks - NeuralSeek vs Microsoft 365 Copilot: https://neuralseek.com/competitors/microsoft-copilot - NeuralSeek vs Fireflies.ai: https://neuralseek.com/competitors/fireflies - NeuralSeek vs Read AI: https://neuralseek.com/competitors/read-ai - NeuralSeek vs ChatGPT Enterprise: https://neuralseek.com/competitors/chatgpt-enterprise - NeuralSeek vs Claude Enterprise: https://neuralseek.com/competitors/claude-enterprise - NeuralSeek vs Glean: https://neuralseek.com/competitors/glean - NeuralSeek vs FortiAI: https://neuralseek.com/competitors/fortiai ## Customer Highlights - [Great Day Improvements (yETI)](https://neuralseek.com/highlights/great-day-improvements): A ChatGPT-style chat experience for Great Day Improvements covering two lines of business (HR + Call Center) with multi-LLM selection, secure authentication, Great Day's knowledge pre-loaded, and a Phase II path into the customer's Databricks datalake. Deployed end-to-end inside Great Day's existing Azure environment. - Markdown: https://neuralseek.com/highlights/great-day-improvements.md - [Itochu International (Itochu Insight)](https://neuralseek.com/highlights/itochu): Hybrid RAG-powered financial intelligence combining SEC EDGAR filings, live web research, and private documents, orchestrated through a graph of 107 specialized agents across 8 categories. 5-year XBRL financial dashboard, cited multi-source analyst chat, and on-demand PDF + editable PPTX deck generation. Deployed inside Itochu's Azure environment with Microsoft Entra SSO and JWT-validated sessions. - Markdown: https://neuralseek.com/highlights/itochu.md ## AI Grounded — Articles & Guides > 141 practical guides, case studies, and comparisons on governed, grounded enterprise AI. Full index: https://neuralseek.com/ai-grounded (markdown: https://neuralseek.com/ai-grounded.md). Each article has a clean markdown mirror at /ai-grounded/.md. - [Claude 4 vs GPT-4o vs Gemini 2.5: Which Is Best for Code Generation?](https://neuralseek.com/ai-grounded/claude-4-vs-gpt-4o-vs-gemini-2-5-code-generation): A head-to-head benchmark of the three leading models on code generation — accuracy, reasoning, speed, and cost — with a clear verdict on which to rea… - [Air-Gapped AI: How to Run LLMs Fully On-Premises with Docker, OpenShift, and Kubernetes](https://neuralseek.com/ai-grounded/air-gapped-ai-run-llms-on-premises-docker-openshift-kubernetes): A practical guide to deploying LLMs in fully isolated, no-egress environments — container orchestration, model serving, private knowledge bases, and… - [How Adobe Enforces Brand Voice Across AI at a Company With 30,000 Employees](https://neuralseek.com/ai-grounded/how-adobe-enforces-brand-voice-across-ai-30000-employees): A case study on the prompt engineering and output-control configuration that keeps AI responses on-brand across Adobe — custom instructions, verbosit… - [How to Build a RAG Pipeline with LangChain and Claude](https://neuralseek.com/ai-grounded/build-rag-pipeline-langchain-claude): A step-by-step, copy-pasteable tutorial: load and chunk your docs, embed and index them, retrieve the right context, and ground Claude's answers in y… - [AI in Financial Services: Building an Audit Trail That Satisfies SEC and SOX Requirements](https://neuralseek.com/ai-grounded/ai-in-financial-services-audit-trail-sec-sox-requirements): What financial regulators actually require from AI systems — immutable logs, configuration versioning, attribution trails, and exportable evidence —… - [How the 4 Major US Carriers Govern AI at Scale: Lessons from Verizon, AT&T, T-Mobile, and Comcast](https://neuralseek.com/ai-grounded/how-4-major-us-carriers-govern-ai-at-scale): A deep-dive into the governance patterns that work at telecom scale — multi-tenant isolation, high-volume caching strategies, real-time abuse detecti… - [We Tested 8 LLMs on Regulated Enterprise Data. Here's What Actually Happened.](https://neuralseek.com/ai-grounded/we-tested-8-llms-on-regulated-enterprise-data): Original benchmark data from NeuralSeek's bake-off suite — accuracy, hallucination rate, latency, cost, and confidence calibration across 8 models te… - [How Verizon Governs AI Across Millions of Daily Customer Interactions](https://neuralseek.com/ai-grounded/how-verizon-governs-ai-across-millions-of-daily-interactions): A case study covering the multi-tenant isolation, caching, abuse detection, and governance architecture behind Verizon's NeuralSeek deployment — the… - [How Itochu Uses NeuralSeek for Secure Cross-Language AI Between English and Japanese Headquarters](https://neuralseek.com/ai-grounded/itochu-secure-cross-language-ai-english-japanese): A case study on the multilingual governance challenge — business context in translation, legal precision requirements, and the configuration that kee… - [NeuralSeek Platform Changelog — June 2026](https://neuralseek.com/ai-grounded/neuralseek-platform-changelog-june-2026): A running monthly log of every guardrail addition, configuration update, new LLM integration, and governance module release — so buyers can see the p… - [How to Deploy NeuralSeek for a Multilingual Enterprise: Cross-Language Configuration Guide](https://neuralseek.com/ai-grounded/how-to-deploy-neuralseek-multilingual-enterprise-cross-language-guide): Covers setting up cross-language query support, configuring per-tenant language fallbacks, and handling the edge cases that break multilingual deploy… - [How to Control What Your AI Retrieves: A Guide to Retrieval Grounding Guardrails](https://neuralseek.com/ai-grounded/how-to-control-what-your-ai-retrieves-retrieval-grounding-guardrails): Covers the full retrieval layer — relevance bands, freshness weighting, document limits, and snippet sizing. The definitive guide for developers tuni… - [AI in Healthcare: How to Meet HIPAA Requirements at the LLM Layer](https://neuralseek.com/ai-grounded/ai-in-healthcare-how-to-meet-hipaa-requirements-at-the-llm-layer): A practical compliance guide for healthcare CTOs — what HIPAA actually requires from your AI layer, how to configure PII redaction, audit logging, an… - [Prompt Injection in Enterprise AI: Direct Attacks, Indirect Attacks, and How to Stop Both](https://neuralseek.com/ai-grounded/prompt-injection-enterprise-ai-direct-indirect): Prompt injection is the most dangerous and least understood AI security risk in the enterprise. Here's a precise, plain-English breakdown of direct v… - [How to Red Team Your AI Before Attackers Do](https://neuralseek.com/ai-grounded/how-to-red-team-your-ai-before-attackers-do): Attackers are already probing your AI for weaknesses. Red teaming means you find them first. Here's a practical, plain-English guide to adversarial t… - [PII in LLM Pipelines: Why Pattern Matching Alone Isn't Enough (And What to Do Instead)](https://neuralseek.com/ai-grounded/pii-in-llm-pipelines-pattern-matching-isnt-enough): Regex catches the PII that looks like PII. It misses the PII that's hidden in plain language. Here's where each approach fails, where they complement… - [The 7 Best AI Governance Frameworks for Regulated Industries in 2026](https://neuralseek.com/ai-grounded/best-ai-governance-frameworks-regulated-industries-2026): ISO 42001, NIST AI RMF, the EU AI Act, HIPAA, FedRAMP, SOC 2, and GDPR — what each one actually requires, who it applies to, and how every standard m… - [DDoS protection: keep the service up under flood attacks](https://neuralseek.com/ai-grounded/ddos-protection): DDoS protection mitigates flood attacks at the agent and API level, defending the availability that all other trust depends on. - [Abuse detection: flag the patterns that signal misuse](https://neuralseek.com/ai-grounded/abuse-detection): Abuse detection flags misuse patterns that throttling alone would miss, adding behavioral awareness to your defenses. - [Rate limiting: cap request volume per tenant and agent](https://neuralseek.com/ai-grounded/rate-limiting): Rate limiting caps request volume per tenant and agent, protecting both stability and spend from runaway usage and abuse. - [Runtime attack detection: catch attacks live, at request time](https://neuralseek.com/ai-grounded/runtime-attack-detection): Runtime attack detection recognizes and flags hostile activity live at request time, adding active defense to the protection testing provides. - [AI-generated remediation guidance: the fix, written for you](https://neuralseek.com/ai-grounded/ai-remediation-guidance): AI-generated remediation guidance turns raw test findings into actionable fixes, closing the gap between detecting a flaw and resolving it. - [Pass/fail scoring report: a clear verdict per agent](https://neuralseek.com/ai-grounded/pass-fail-scoring-report): The Pass/fail scoring report gives each agent a clear, exportable verdict, turning adversarial testing into an unambiguous decision input. - [Self-serve on-demand execution: red-team your own deployment anytime](https://neuralseek.com/ai-grounded/self-serve-execution): Self-serve on-demand execution lets you red-team your own deployment anytime, turning rigorous security testing into a routine action. - [Continuous threat-intel updates: defenses that learn the latest attacks](https://neuralseek.com/ai-grounded/continuous-threat-intel-updates): Continuous threat-intel updates keep the adversarial suite current with newly discovered attacks, so your testing never goes stale. - [Service Disruption test bucket: test resilience against abuse](https://neuralseek.com/ai-grounded/service-disruption-test-bucket): The Service Disruption test bucket stresses the deployment with abuse and DDoS-style scenarios, validating that protections hold under hostile load. - [Unauthorized Access test bucket: test identity and privilege defenses](https://neuralseek.com/ai-grounded/unauthorized-access-test-bucket): The Unauthorized Access test bucket probes for identity spoofing and privilege escalation, validating the boundaries between users, roles, and tenant… - [SQL Injection test bucket: probe back-end query defenses](https://neuralseek.com/ai-grounded/sql-injection-test-bucket): The SQL Injection test bucket probes back-end query defenses with adversarial input, protecting the data layer behind your AI flows. - [Data Exfiltration test bucket: test for leaks before attackers find them](https://neuralseek.com/ai-grounded/data-exfiltration-test-bucket): The Data Exfiltration test bucket probes for PII, training-data, and credential leaks, revealing exposure before an attacker can exploit it. - [Prompt Injection test bucket: probe for direct and indirect attacks](https://neuralseek.com/ai-grounded/prompt-injection-test-bucket): The Prompt Injection test bucket probes for direct and indirect injection vulnerabilities, validating your defenses before attackers find the gaps. - [Built-in adversarial test suite: red-teaming that ships in the product](https://neuralseek.com/ai-grounded/adversarial-test-suite): The Built-in adversarial test suite ships red-teaming inside the product, making rigorous attack testing a routine, self-serve action. - [Exportable comparison reports: procurement-ready evidence](https://neuralseek.com/ai-grounded/exportable-comparison-reports): Exportable comparison reports turn bake-off results into procurement-ready evidence, making model decisions easy to justify and document. - [Cost projection: forecast spend per call, flow, and tenant](https://neuralseek.com/ai-grounded/cost-projection): Cost projection forecasts spend per call, flow, and tenant and quantifies savings, turning AI cost into a predictable plan. - [Workflow A/B comparison: run two workflow variants head-to-head](https://neuralseek.com/ai-grounded/workflow-ab-comparison): Workflow A/B comparison runs two variants head-to-head, letting you validate workflow changes with evidence instead of intuition. - [Token usage comparison: see which model is most efficient](https://neuralseek.com/ai-grounded/token-usage-comparison-metric): Token usage comparison shows which model answers most efficiently, exposing efficiency differences that drive cost and latency. - [Confidence comparison: see how each model's certainty distributes](https://neuralseek.com/ai-grounded/confidence-comparison-metric): Confidence comparison reveals how each model's certainty distributes, surfacing which models are easiest to govern with confidence gates. - [Hallucination rate comparison: see which model stays grounded](https://neuralseek.com/ai-grounded/hallucination-rate-comparison-metric): Hallucination rate comparison measures how often each model fabricates on your tasks, putting grounding reliability into the selection decision. - [Cost-per-call comparison: see what each model actually costs](https://neuralseek.com/ai-grounded/cost-per-call-comparison-metric): Cost-per-call comparison reveals what each model actually costs on your tasks, central to right-sizing spend without losing quality. - [Latency comparison: see which model responds fastest](https://neuralseek.com/ai-grounded/latency-comparison-metric): Latency comparison measures real response speed across models, keeping user experience in view during selection. - [Accuracy comparison: see which model gets it right most often](https://neuralseek.com/ai-grounded/accuracy-comparison-metric): Accuracy comparison shows which model gets answers right most often on your tasks, grounding model selection in measured correctness. - [Built-in LLM bake-off: benchmark models side by side](https://neuralseek.com/ai-grounded/llm-bake-off): Built-in LLM bake-off benchmarks any number of models side by side on your own tasks, making model selection evidence-based. - [Platform-level default LLM: one global default for every agent](https://neuralseek.com/ai-grounded/platform-default-llm): Platform-level default LLM cascades one global model choice to every agent, giving you a single lever to govern and migrate the platform. - [Workflow-node model selection: pick a model per node in the IDE](https://neuralseek.com/ai-grounded/workflow-node-model-selection): Workflow-node model selection lets each step of a workflow run on its own model, right-sizing capability and cost node by node. - [API-level model swap: change models with one parameter](https://neuralseek.com/ai-grounded/api-level-model-swap): API-level model swap changes the model with a single parameter and no refactor, making provider choice trivial and reversible. - [Secret Value: vault-backed resolution with BYOK and HYOK](https://neuralseek.com/ai-grounded/secret-value): Secret Value resolves credentials at runtime from your own vault across six back-ends with BYOK/HYOK, so the platform never stores your secrets. - [Secret Name: reference credentials by name, never by value](https://neuralseek.com/ai-grounded/secret-name): Secret Name lets flows reference credentials by name, keeping raw values out of definitions, logs, and exports. - [Regex Rules: find-and-replace at the input and output boundary](https://neuralseek.com/ai-grounded/prompt-regex-rules): Regex Rules apply deterministic find-and-replace at the input and output boundary, handling transformations too important to leave to the model. - [Instructions: free-text directives that steer behavior](https://neuralseek.com/ai-grounded/prompt-instructions): Instructions give you a free-text layer of system directives to steer an agent's behavior deliberately and visibly. - [Custom Prompt builder: compose prompts with secrets and variables](https://neuralseek.com/ai-grounded/custom-prompt-builder): Custom Prompt builder composes prompts from variables, secrets, and system vars, turning ad hoc strings into structured, reusable configuration. - [ISO 42001 / NIST AI RMF Mapping: framework alignment out of the box](https://neuralseek.com/ai-grounded/iso-42001-nist-mapping): ISO 42001 / NIST AI RMF Mapping aligns the platform's controls to recognized AI governance frameworks out of the box, accelerating audits. - [Cache Savings Tracking: prove the dollars the cache prevented](https://neuralseek.com/ai-grounded/cache-savings-roi): Cache Savings Tracking quantifies prevented spend in dollars daily, turning caching from an efficiency feature into a reported ROI. - [Configuration Diff & Rollback: see the redline and rewind instantly](https://neuralseek.com/ai-grounded/configuration-diff-rollback): Configuration Diff & Rollback shows visual redlines and enables instant point-in-time recovery, making every configuration change safe to undo. - [Configuration Version Control: Git-style history for every setting](https://neuralseek.com/ai-grounded/configuration-version-control): Configuration Version Control gives every setting a Git-style, attributable history — who changed what, when, and exactly how. - [Hide Keys: auto-redact sensitive data from logs](https://neuralseek.com/ai-grounded/hide-keys): Hide Keys auto-redacts sensitive data from logs, letting you capture a thorough audit trail without turning it into a liability. - [Prompt Logging: capture the full prompt and response](https://neuralseek.com/ai-grounded/prompt-logging): Prompt Logging captures the full prompt and response, making every interaction replayable and explainable for deep audits. - [Endpoint: point logging exactly where you need it](https://neuralseek.com/ai-grounded/logger-endpoint): Endpoint gives precise control over exactly where audit logs are routed, complementing the logger type with an exact destination. - [Logger Type: send logs to S3, Splunk, Datadog, or your SIEM](https://neuralseek.com/ai-grounded/logger-type): Logger Type routes audit logs to S3, Splunk, Datadog, or your SIEM, fitting the platform into the tools your teams already use. - [Corp Logging: the master switch for enterprise logging](https://neuralseek.com/ai-grounded/corp-logging): Corp Logging is the master switch for enterprise-grade logging, the foundation every audit and compliance capability builds on. - [Corp Filter: per-tenant control over which documents are in play](https://neuralseek.com/ai-grounded/corp-filter): Corp Filter scopes retrieval per tenant, ensuring each draws only on the documents it's entitled to. - [HTML Clean: sanitize markup before it's ever displayed](https://neuralseek.com/ai-grounded/html-clean): HTML Clean sanitizes markup before delivery, guaranteeing answers render safely and correctly in any web surface. - [Stopwords: strip noise words at output time](https://neuralseek.com/ai-grounded/stopwords): Stopwords strips configured noise terms from output at delivery time, sharpening the final answer. - [Unique Links: dedupe repeated source links](https://neuralseek.com/ai-grounded/unique-links): Unique Links dedupes repeated source links, keeping cited answers clean, readable, and professional. - [Embed Links: inline source links right in the answer](https://neuralseek.com/ai-grounded/embed-links): Embed Links inlines source links so users can verify any claim by following the answer back to its source. - [VA Format: shape answers for voice and telephony](https://neuralseek.com/ai-grounded/va-format): VA Format shapes answers for voice and telephony, producing responses that work when spoken rather than read. - [Log Alt: capture alternate generations for review](https://neuralseek.com/ai-grounded/log-alt): Log Alt captures the alternate generations the model considered, giving teams deeper insight for tuning and review. - [Stream Plan: show the multi-step plan as it unfolds](https://neuralseek.com/ai-grounded/stream-plan): Stream Plan reveals the system's multi-step reasoning as it unfolds, building trust during complex answers. - [Relax Filters: loosen retrieval filters only when it's safe](https://neuralseek.com/ai-grounded/relax-filters): Relax Filters conditionally loosens retrieval to recover answers when strict filtering would otherwise return nothing. - [Default Language: the per-tenant language fallback](https://neuralseek.com/ai-grounded/default-language): Default Language sets the per-tenant fallback locale, anchoring the multilingual experience with a sensible baseline. - [Cross Language: answer in the user's language automatically](https://neuralseek.com/ai-grounded/cross-language-toggle): Cross Language auto-translates queries so a single knowledge base can serve a global audience in any language. - [Force Context: guarantee the conversation is always carried](https://neuralseek.com/ai-grounded/force-context): Force Context guarantees conversational continuity in flows where every turn depends on the last, overriding automatic detection. - [Context Detect: automatically know when history matters](https://neuralseek.com/ai-grounded/context-detect): Context Detect automatically applies conversational memory only when a question actually needs it, keeping answers efficient and accurate. - [User TTL: per-tenant, user-isolated memory lifetimes](https://neuralseek.com/ai-grounded/user-ttl): User TTL enforces per-tenant, user-isolated memory lifetimes, guaranteeing one user's context never bleeds into another's. - [Session TTL: how long a conversation stays alive](https://neuralseek.com/ai-grounded/session-ttl): Session TTL controls how long a conversation persists, keeping state fresh and clearing it before it goes stale. - [Context Turns: how many turns of conversation the system remembers](https://neuralseek.com/ai-grounded/context-turns): Context Turns tunes how many prior turns the assistant remembers, balancing conversational coherence against relevance and cost. - [LG Timeout: bound language generation so it never hangs](https://neuralseek.com/ai-grounded/lg-timeout): LG Timeout bounds the language-generation step so conversations stay responsive and never hang. - [Timeout (per call): bound how long a single call can run](https://neuralseek.com/ai-grounded/llm-control-timeout): Timeout (per call) bounds how long any single call can run, keeping latency predictable and preventing one slow call from stalling a workflow. - [Images (multimodal): govern how the model handles attached images](https://neuralseek.com/ai-grounded/images-multimodal): Images (multimodal) brings image handling under governance, controlling how visual input is attached and processed alongside text. - [Prepend: inject system instructions ahead of the prompt](https://neuralseek.com/ai-grounded/prepend): Prepend injects consistent system-level instructions ahead of each prompt, making standing behavior reliable across calls. - [Cache (per call): reuse model responses where it's safe](https://neuralseek.com/ai-grounded/llm-control-cache): Cache (per call) gives fine-grained control over response reuse, cutting cost on safe steps while keeping critical calls fresh. - [Model selection: set the default LLM for the platform](https://neuralseek.com/ai-grounded/model-selection): Model selection sets the platform-wide default LLM that cascades to every agent, giving you one governed place to change models system-wide. - [Per-Call model selection: pick the right model for each step](https://neuralseek.com/ai-grounded/per-call-model-selection): Per-Call model selection right-sizes each step to the model it actually needs, optimizing cost and quality node by node. - [Streaming: show answers as they form, per node](https://neuralseek.com/ai-grounded/streaming): Streaming shows answers as they form for a faster feel, with per-node control for contexts that need the complete response first. - [Min Tokens: a floor so answers aren't cut short](https://neuralseek.com/ai-grounded/min-tokens): Min Tokens floors generation length so answers reach a useful, complete form instead of stopping short. - [Max Tokens: a hard cap on how much the model can generate](https://neuralseek.com/ai-grounded/max-tokens): Max Tokens caps generation length, turning an open-ended cost risk into a predictable, bounded line item. - [Frequency Penalty: stop the model from repeating itself](https://neuralseek.com/ai-grounded/frequency-penalty): Frequency Penalty discourages repetition, keeping answers clean, varied, and economical with tokens. - [Top-P: cap the model's word choices with nucleus sampling](https://neuralseek.com/ai-grounded/top-p): Top-P caps the model's sampling pool, keeping output focused on likely choices and reducing erratic word selection. - [Temperature: dial answers from deterministic to creative](https://neuralseek.com/ai-grounded/temperature): Temperature controls output randomness per call, keeping factual answers consistent while allowing creativity where it helps. - [Re-Sort priority values: apply your business priorities after retrieval](https://neuralseek.com/ai-grounded/re-sort-priority-values): Re-Sort priority values let business rules shape the final ranking after retrieval, so authority and recency get the last word. - [KNN Vector query: bring your own custom vector search](https://neuralseek.com/ai-grounded/knn-vector-query): KNN Vector query gives advanced teams full, custom control over semantic nearest-neighbor search via JSON. - [ELSER: sparse-encoder retrieval, configured your way](https://neuralseek.com/ai-grounded/elser): ELSER brings configurable sparse-encoder retrieval into the search mix, blending keyword precision with semantic understanding. - [Query Type: choose Lucene, vector, or hybrid retrieval](https://neuralseek.com/ai-grounded/query-type): Query Type selects keyword, semantic, or hybrid retrieval so the search strategy matches your content and the way users ask. - [Cache KB: tie cached answers to the exact knowledge base they came from](https://neuralseek.com/ai-grounded/cache-kb): Cache KB ties each cached answer to its originating knowledge base, ensuring reuse never crosses sources and serves the wrong content. - [Cache Context: only reuse an answer when the conversation matches](https://neuralseek.com/ai-grounded/cache-context): Cache Context binds reuse to the surrounding conversation, so cached answers are only served when the situation genuinely matches. - [Multi-Agent routing: send each question to the specialist that handles it](https://neuralseek.com/ai-grounded/multi-agent-routing): Multi-Agent routing directs each question to the specialist agent best equipped to answer it, raising quality across the whole system. - [Normal Cache: reuse auto-generated answers to cut cost and latency](https://neuralseek.com/ai-grounded/normal-cache): Normal Cache reuses auto-generated answers for repeat questions, cutting both latency and token cost with a freshness window you control. - [Edit Cache: serve your hand-curated answers instantly](https://neuralseek.com/ai-grounded/edit-cache): Edit Cache serves your hand-curated answers consistently and instantly, protecting the quality you invested in editing them. - [Intent Match Threshold %: how sure before the system commits to an intent](https://neuralseek.com/ai-grounded/intent-match-threshold): Intent Match Threshold % prevents confident misrouting by requiring real certainty before the system commits a question to an intent. - [Match Type: how the system decides what a question means](https://neuralseek.com/ai-grounded/match-type): Match Type tunes how the system interprets user questions, from exact matching to fuzzy semantic similarity. - [Misinformation Tolerance: dial brand caution from rigid to standard](https://neuralseek.com/ai-grounded/misinformation-tolerance-slider): The Misinformation Tolerance slider expresses your brand's risk appetite as a single dial, from rigidly cautious to standard helpfulness. - [Blocked Reply Text: control exactly what users see when content is blocked](https://neuralseek.com/ai-grounded/blocked-reply-text): Blocked Reply Text turns a refusal into an on-brand moment, replacing generic errors with a message you control. - [Filter Mode: choose how profanity gets caught](https://neuralseek.com/ai-grounded/profanity-filter-mode): Filter Mode lets each channel pick the profanity defense that fits — nuanced LLM moderation, fast native filtering, or off in trusted contexts. - [Force KB: refuse to answer from anything but your knowledge base](https://neuralseek.com/ai-grounded/force-kb): Force KB locks the assistant to your knowledge base, guaranteeing every answer reflects approved sources rather than the model's general training. - [Verbosity: one dial from terse to thorough](https://neuralseek.com/ai-grounded/verbosity): Verbosity gives you one dial to match answer depth to your audience, from terse expert replies to thorough explanations. - [Max Words: cap rambling answers before they lose the point](https://neuralseek.com/ai-grounded/max-words): Max Words caps answer length so responses stay concise, on-point, and right-sized for their channel. - [Min Words: reject answers too short to actually help](https://neuralseek.com/ai-grounded/min-words): Min Words filters out hollow, too-short responses so users only receive answers substantial enough to actually help. - [Minimum Confidence % for URL: suppress links the system isn't sure about](https://neuralseek.com/ai-grounded/minimum-confidence-url): Minimum Confidence % for URL holds links to a stricter standard than text, suppressing them whenever the system isn't sure enough to be safe. - [Minimum Confidence %: the floor below which the system won't answer](https://neuralseek.com/ai-grounded/minimum-confidence-percent): Minimum Confidence % sets the hard floor beneath which the assistant declines rather than guesses — restraint turned into an enforceable guarantee. - [Warning %: flag a shaky answer instead of hiding the doubt](https://neuralseek.com/ai-grounded/warning-percent): Warning % surfaces a candid low-confidence signal on shaky answers, letting users weigh them instead of trusting them blindly. - [Trust Words: allow-list the safe terms so they're never redacted](https://neuralseek.com/ai-grounded/trust-words): Trust Words allow-lists known-safe terms so aggressive privacy controls never mangle legitimate content with needless redaction. - [Out-of-the-box Detector Library: 13 sensitive-data categories on day one](https://neuralseek.com/ai-grounded/pii-detector-library): The Out-of-the-box Detector Library delivers 13 categories of sensitive-data coverage from day one, making strong privacy the default rather than a b… - [LLM-Based PII Detection: contextual catching of what patterns miss](https://neuralseek.com/ai-grounded/llm-based-pii-detection): LLM-Based PII Detection adds contextual judgment to privacy enforcement, catching the sensitive data that pattern matching alone would miss. - [Pre-LLM Regex: redact sensitive data before the model ever sees it](https://neuralseek.com/ai-grounded/pre-llm-regex): Pre-LLM Regex deterministically redacts structured sensitive data before it reaches the model — protection at the earliest possible point. - [PII Action: mask, flag, hide, or delete — you choose the response](https://neuralseek.com/ai-grounded/pii-action): PII Action gives you five precise enforcement options so every category of personal data is handled exactly as its sensitivity demands. - [Indirect Prompt Injection Protection: catch attacks hidden in your own documents](https://neuralseek.com/ai-grounded/indirect-prompt-injection-protection): Indirect Prompt Injection Protection screens retrieved documents, URLs, and tool outputs for hidden attacks, closing the blind spot that direct-input… - [Blocked Word List: managed and custom terms you never want through](https://neuralseek.com/ai-grounded/blocked-word-list): Blocked Word List pairs a maintained baseline with per-tenant custom terms, so the system catches both universal risks and the ones unique to your bu… - [Blocked Word Action: decide what happens when a forbidden term appears](https://neuralseek.com/ai-grounded/blocked-word-action): Blocked Word Action turns detection into enforcement, giving each tenant a predictable, policy-aligned response when a forbidden term appears. - [Prompt Injection Block Threshold: the line where a request gets refused outright](https://neuralseek.com/ai-grounded/prompt-injection-block-threshold): Prompt Injection Block Threshold draws the line where a request is too clearly malicious to clean and must be refused outright — the strongest tier o… - [Prompt Injection Removal Threshold: surgically strip the attack, keep the request](https://neuralseek.com/ai-grounded/prompt-injection-removal-threshold): Prompt Injection Removal Threshold neutralizes attacks mid-stream while preserving the legitimate request — precise defense instead of a blunt reject… - [Hallucinated Term Allowlist: closed-loop remediation in one click](https://neuralseek.com/ai-grounded/hallucinated-term-allowlist): Hallucinated Term Allowlist closes the loop — click a flagged term on the dashboard to allow-list it permanently, turning false positives into a one-… - [Hallucination KW Removal: sentence-level surgery on ungrounded claims](https://neuralseek.com/ai-grounded/hallucination-kw-removal): Hallucination KW Removal strips individual sentences when their proper nouns aren't in the source — precision editing instead of blunt rejection. - [Re-Rank Min Coverage %: a floor on how much an answer is backed](https://neuralseek.com/ai-grounded/re-rank-min-coverage): Re-Rank Min Coverage % drops answers that fall below a coverage threshold — a hard floor beneath which a response simply won't ship. - [Total Coverage Weight: reward the sources that carry the answer](https://neuralseek.com/ai-grounded/total-coverage-weight): Total Coverage Weight weights passages by how much of the answer they actually support — concentrating grounding where it matters. - [Source Jump Penalty: stop answers stitched from unrelated docs](https://neuralseek.com/ai-grounded/source-jump-penalty): Source Jump Penalty penalizes answers stitched together across unrelated documents — a classic recipe for confident nonsense. - [Term Penalty: enforce the vocabulary the answer must include](https://neuralseek.com/ai-grounded/term-penalty): Term Penalty penalizes answers missing required terms, giving you a direct lever on the vocabulary every answer must cover. - [Key Term Penalty: don't drop the names that matter](https://neuralseek.com/ai-grounded/key-term-penalty): Key Term Penalty docks answers that omit the key entities present in the source — catching subtle drift before it ships. - [Check URLs: only cite links the source actually supports](https://neuralseek.com/ai-grounded/check-urls): Check URLs requires URL-level grounding so the assistant never invents or misattributes a link in its answer. - [Check Titles: grounding answers at the document level](https://neuralseek.com/ai-grounded/check-titles): Check Titles requires title-level grounding, tying answers back to the specific documents they came from for clean attribution. - [Re-Rank: putting the best evidence first](https://neuralseek.com/ai-grounded/re-rank): Re-Rank reorders retrieved documents by true semantic relevance, so the model reasons from the strongest evidence first. - [Semantic Score Threshold: proof the answer matches its source](https://neuralseek.com/ai-grounded/semantic-score-threshold): Semantic Score Threshold enforces a minimum semantic match between the answer and its source — the core gate that blocks ungrounded claims. - [Max Raw Score: normalizing retrieval before re-ranking](https://neuralseek.com/ai-grounded/max-raw-score): Max Raw Score caps raw retrieval scores before re-ranking, keeping the scoring pipeline calibrated and outliers from distorting results. - [Snippet Size: how much of each source the model gets to see](https://neuralseek.com/ai-grounded/snippet-size): Snippet Size controls how much of each source paragraph is forwarded as context — balancing completeness against token efficiency. - [Max Docs: the hard ceiling on what reaches the model](https://neuralseek.com/ai-grounded/max-docs): Max Docs caps how many sources reach the LLM per call — protecting answer quality and cost from context overload. - [Query Cache: reuse smart answers, stop paying twice](https://neuralseek.com/ai-grounded/query-cache): Query Cache reuses retrievals for identical questions within a window — cutting latency and token cost without sacrificing freshness. - [Date Penalty: freshness weighting that retires stale answers](https://neuralseek.com/ai-grounded/date-penalty): Date Penalty quietly down-ranks stale documents so the model leans on what's current — without you manually pruning the knowledge base. - [Document Score Range: the relevance band that keeps answers on-topic](https://neuralseek.com/ai-grounded/document-score-range): Document Score Range sets the relevance band for what gets pulled from your knowledge base — so the model only ever sees sources worth answering from. - [Why we built AI Grounded](https://neuralseek.com/ai-grounded/why-we-built-ai-grounded): Enterprise AI moves fast and breaks trust. AI Grounded is where we slow down, show our work, and keep the conversation honest. - [Why all four major U.S. carriers run on NeuralSeek](https://neuralseek.com/ai-grounded/all-four-us-carriers-use-neuralseek): Verizon, AT&T, T-Mobile, and Comcast (Xfinity Mobile) all use NeuralSeek to power AI — from customer self-service to internal employee assistants. He… - [The top 10 grounded AI governance programs in 2026 (ranked)](https://neuralseek.com/ai-grounded/top-10-ai-governance-programs-2026): AI governance is no longer optional in regulated industries. Here are the ten programs that actually deliver in 2026 — ranked by how well they serve… - [Guardrails that actually hold](https://neuralseek.com/ai-grounded/guardrails-that-actually-hold): A guardrail you can't audit is a guess. Here's how we think about building controls that survive contact with production. - [The real cost of runaway AI](https://neuralseek.com/ai-grounded/the-real-cost-of-runaway-ai): Token bills come due. We break down where enterprise AI spend actually goes — and how governance keeps it predictable. ## Documentation - [Documentation Home](https://documentation.neuralseek.com/): Full product documentation. - [Quick Start Guide](https://documentation.neuralseek.com/ui/home/): Get from zero to first deployment. - [API Reference](https://documentation.neuralseek.com/): REST API for Seek queries, KnowledgeBase management, and agent execution. - [Guardrail Configuration Guide](https://documentation.neuralseek.com/ui/configure/): All 118 guardrails explained with examples. - [mAIstro Workflow Builder Guide](https://documentation.neuralseek.com/ui/maistro/): Designing and deploying agentic workflows. - [Integration Guides](https://documentation.neuralseek.com/ui/integrate/): Slack, Microsoft Teams, web widget (ChatSDK), voice, telephony, WhatsApp, SMS. - [Supported LLM Providers](https://documentation.neuralseek.com/ui/integrate/integrations/supported_llms/supported_llms/): Per-provider configuration and the full supported-model matrix. - [Bring Your Own LLM (BYOLLM)](https://documentation.neuralseek.com/features/multi_llm/): Connect any OpenAI-compatible endpoint or self-hosted model. ## Links - Website: https://neuralseek.com - Documentation: https://documentation.neuralseek.com/ - Trust Center: https://neuralseek.com/trust-center - Pricing: https://neuralseek.com/pricing - Platform: https://neuralseek.com/platform - Sitemap: https://neuralseek.com/sitemap.xml ## Optional - [CodeGen](https://neuralseek.com/codegen): AI code generation surface. - Markdown: https://neuralseek.com/codegen.md - [What is Docker AI?](https://neuralseek.com/docker_ai): NeuralSeek is Docker for AI — every component a custom AI application needs (the model, your data, knowledge, actions, guardrails, and orchestration) bundled into one container that runs the same everywhere and can build anything. - [Agentic AI Internship — Apply](https://neuralseek.com/apply-for-internship): Six-week, fully remote, unpaid internship for undergraduate and graduate students worldwide (international applicants welcome). Six-week curriculum from data fundamentals through data audit, product research, competitive analysis, and mock pitches to a final Build & Hackathon week. Interns ship an Agentic AI solution on mAIstro and earn NeuralSeek Certificates 1, 2, and 3. Covers logistics, what we look for, F-1/CPT and work-authorization requirements, weekly expectations, open cohorts, and the application form. - Markdown: https://neuralseek.com/apply-for-internship.md - [Secured AI — Webinar Registration](https://neuralseek.com/webinar/secured-ai): Registration page for a free 30-minute webinar hosted by TechD on September 15 2026 at 1:00 PM ET, covering "Secured AI: Containerized on IBM Fusion, powered by NeuralSeek". Aimed at IT, security and compliance leaders in healthcare and financial services who need generative AI to stay inside their own infrastructure to meet HIPAA, PCI DSS, SOX or GLBA requirements. Covers why public-cloud AI tools stall in regulated industries, how IBM Fusion provides a containerized on-prem behind-the-firewall foundation (immutable air-gapped backup, access control, encryption, single-copy governance), a live demo of an internal AI chat grounded only in approved documents, and how to evaluate the approach. Includes the session agenda, who should attend, presenters, and the registration form. - Markdown: https://neuralseek.com/webinar/secured-ai.md