NeuralSeek
AI Grounded
Practical guides, case studies, and comparisons on building governed, grounded enterprise AI — model-agnostic, in your own tenant, with guardrails enforced before any action.
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Comparison
Claude 4 vs GPT-4o vs Gemini 2.5: Which Is Best for 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 reach for and when. Updated monthly.
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Guide
Air-Gapped AI: How to Run LLMs Fully On-Premises with Docker, OpenShift, and Kubernetes
A practical guide to deploying LLMs in fully isolated, no-egress environments — container orchestration, model serving, private knowledge bases, and security hardening for government, defense, and finance.
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Case Study
How Adobe Enforces Brand Voice Across AI at a Company With 30,000 Employees
A case study on the prompt engineering and output-control configuration that keeps AI responses on-brand across Adobe — custom instructions, verbosity tuning, profanity filtering, and the governance that stops teams going off-brand.
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Tutorial
How to Build a RAG Pipeline with LangChain and 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 your own sources — with a working repo to clone.
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Financial Services
AI in Financial Services: Building an Audit Trail That Satisfies SEC and SOX Requirements
What financial regulators actually require from AI systems — immutable logs, configuration versioning, attribution trails, and exportable evidence — and how to configure NeuralSeek's audit stack to meet each requirement.
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Telecom
How the 4 Major US Carriers Govern AI at Scale: Lessons from Verizon, AT&T, T-Mobile, and Comcast
A deep-dive into the governance patterns that work at telecom scale — multi-tenant isolation, high-volume caching strategies, real-time abuse detection, and how to maintain consistency across millions of daily AI interactions.
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Benchmarks
We Tested 8 LLMs on Regulated Enterprise Data. Here's What Actually Happened.
Original benchmark data from NeuralSeek's bake-off suite — accuracy, hallucination rate, latency, cost, and confidence calibration across 8 models tested against real regulated-sector knowledge bases. No vendor-supplied benchmarks, just production-representative results.
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Case Study
How Children's Health Hospital Deployed Clinical AI with Zero Hallucination Tolerance
A structured case study — before state, problem, implementation, outcome — covering the guardrail configuration Children's Health uses to govern a clinical knowledge base chatbot serving nurses and pediatricians.
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Case Study
How Verizon Governs AI Across Millions of Daily Customer Interactions
A case study covering the multi-tenant isolation, caching, abuse detection, and governance architecture behind Verizon's NeuralSeek deployment — the scale challenges, the configuration decisions, and the measurable outcomes.
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Case Study
How Itochu Uses NeuralSeek for Secure Cross-Language AI Between English and Japanese Headquarters
A case study on the multilingual governance challenge — business context in translation, legal precision requirements, and the configuration that keeps Itochu's AI accurate and compliant across languages and jurisdictions.
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Changelog
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 platform moving in real time.
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Multilingual
How to Deploy NeuralSeek for a Multilingual Enterprise: Cross-Language Configuration Guide
Covers setting up cross-language query support, configuring per-tenant language fallbacks, and handling the edge cases that break multilingual deployments — tone, business context, and mistranslation risk.
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Retrieval Grounding
How to Control What Your AI Retrieves: A Guide to Retrieval Grounding Guardrails
Covers the full retrieval layer — relevance bands, freshness weighting, document limits, and snippet sizing. The definitive guide for developers tuning their knowledge base retrieval.
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Healthcare
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, and access controls, and what Children's Health implemented in production.
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Security
Prompt Injection in Enterprise AI: Direct Attacks, Indirect Attacks, and How to Stop Both
Prompt injection is the most dangerous and least understood AI security risk in the enterprise. Here's a precise, plain-English breakdown of direct vs. indirect attacks — and the architecture that actually stops both.
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Security
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 testing — and the built-in suite that makes it repeatable.
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Privacy
PII in LLM Pipelines: Why Pattern Matching Alone Isn't Enough (And What to Do Instead)
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 each other, and how to layer both for real enterprise privacy.
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Compliance
The 7 Best AI Governance Frameworks for Regulated Industries in 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 maps to a concrete platform control you can switch on.
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Red Team & Rogue AI
DDoS protection: keep the service up under flood attacks
DDoS protection mitigates flood attacks at the agent and API level, defending the availability that all other trust depends on.
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Red Team & Rogue AI
Abuse detection: flag the patterns that signal misuse
Abuse detection flags misuse patterns that throttling alone would miss, adding behavioral awareness to your defenses.
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Red Team & Rogue AI
Rate limiting: cap request volume per tenant and agent
Rate limiting caps request volume per tenant and agent, protecting both stability and spend from runaway usage and abuse.
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Red Team & Rogue AI
Runtime attack detection: catch attacks live, at request time
Runtime attack detection recognizes and flags hostile activity live at request time, adding active defense to the protection testing provides.
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Red Team & Rogue AI
AI-generated remediation guidance: the fix, written for you
AI-generated remediation guidance turns raw test findings into actionable fixes, closing the gap between detecting a flaw and resolving it.
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Red Team & Rogue AI
Pass/fail scoring report: a clear verdict per agent
The Pass/fail scoring report gives each agent a clear, exportable verdict, turning adversarial testing into an unambiguous decision input.
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Red Team & Rogue AI
Self-serve on-demand execution: red-team your own deployment anytime
Self-serve on-demand execution lets you red-team your own deployment anytime, turning rigorous security testing into a routine action.
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Red Team & Rogue AI
Continuous threat-intel updates: defenses that learn the latest attacks
Continuous threat-intel updates keep the adversarial suite current with newly discovered attacks, so your testing never goes stale.
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Red Team & Rogue AI
Service Disruption test bucket: test resilience against abuse
The Service Disruption test bucket stresses the deployment with abuse and DDoS-style scenarios, validating that protections hold under hostile load.
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Red Team & Rogue AI
Unauthorized Access test bucket: test identity and privilege defenses
The Unauthorized Access test bucket probes for identity spoofing and privilege escalation, validating the boundaries between users, roles, and tenants.
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Red Team & Rogue AI
SQL Injection test bucket: probe back-end query defenses
The SQL Injection test bucket probes back-end query defenses with adversarial input, protecting the data layer behind your AI flows.
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Red Team & Rogue AI
Data Exfiltration test bucket: test for leaks before attackers find them
The Data Exfiltration test bucket probes for PII, training-data, and credential leaks, revealing exposure before an attacker can exploit it.
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Red Team & Rogue AI
Prompt Injection test bucket: probe for direct and indirect attacks
The Prompt Injection test bucket probes for direct and indirect injection vulnerabilities, validating your defenses before attackers find the gaps.
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Red Team & Rogue AI
Built-in adversarial test suite: red-teaming that ships in the product
The Built-in adversarial test suite ships red-teaming inside the product, making rigorous attack testing a routine, self-serve action.
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Model-Agnostic
Exportable comparison reports: procurement-ready evidence
Exportable comparison reports turn bake-off results into procurement-ready evidence, making model decisions easy to justify and document.
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Model-Agnostic
Cost projection: forecast spend per call, flow, and tenant
Cost projection forecasts spend per call, flow, and tenant and quantifies savings, turning AI cost into a predictable plan.
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Model-Agnostic
Workflow A/B comparison: run two workflow variants head-to-head
Workflow A/B comparison runs two variants head-to-head, letting you validate workflow changes with evidence instead of intuition.
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Model-Agnostic
Token usage comparison: see which model is most efficient
Token usage comparison shows which model answers most efficiently, exposing efficiency differences that drive cost and latency.
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Model-Agnostic
Confidence comparison: see how each model's certainty distributes
Confidence comparison reveals how each model's certainty distributes, surfacing which models are easiest to govern with confidence gates.
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Model-Agnostic
Hallucination rate comparison: see which model stays grounded
Hallucination rate comparison measures how often each model fabricates on your tasks, putting grounding reliability into the selection decision.
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Model-Agnostic
Cost-per-call comparison: see what each model actually costs
Cost-per-call comparison reveals what each model actually costs on your tasks, central to right-sizing spend without losing quality.
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Model-Agnostic
Latency comparison: see which model responds fastest
Latency comparison measures real response speed across models, keeping user experience in view during selection.
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Model-Agnostic
Accuracy comparison: see which model gets it right most often
Accuracy comparison shows which model gets answers right most often on your tasks, grounding model selection in measured correctness.
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Model-Agnostic
Built-in LLM bake-off: benchmark models side by side
Built-in LLM bake-off benchmarks any number of models side by side on your own tasks, making model selection evidence-based.
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Model-Agnostic
Platform-level default LLM: one global default for every agent
Platform-level default LLM cascades one global model choice to every agent, giving you a single lever to govern and migrate the platform.
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Model-Agnostic
Workflow-node model selection: pick a model per node in the IDE
Workflow-node model selection lets each step of a workflow run on its own model, right-sizing capability and cost node by node.
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Model-Agnostic
API-level model swap: change models with one parameter
API-level model swap changes the model with a single parameter and no refactor, making provider choice trivial and reversible.
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Secrets & Credentials
Secret Value: vault-backed resolution with BYOK and HYOK
Secret Value resolves credentials at runtime from your own vault across six back-ends with BYOK/HYOK, so the platform never stores your secrets.
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Secrets & Credentials
Secret Name: reference credentials by name, never by value
Secret Name lets flows reference credentials by name, keeping raw values out of definitions, logs, and exports.
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Prompt Engineering
Regex Rules: find-and-replace at the input and output boundary
Regex Rules apply deterministic find-and-replace at the input and output boundary, handling transformations too important to leave to the model.
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Prompt Engineering
Instructions: free-text directives that steer behavior
Instructions give you a free-text layer of system directives to steer an agent's behavior deliberately and visibly.
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Prompt Engineering
Custom Prompt builder: compose prompts with secrets and variables
Custom Prompt builder composes prompts from variables, secrets, and system vars, turning ad hoc strings into structured, reusable configuration.
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Audit & Compliance
ISO 42001 / NIST AI RMF Mapping: framework alignment out of the box
ISO 42001 / NIST AI RMF Mapping aligns the platform's controls to recognized AI governance frameworks out of the box, accelerating audits.
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Audit & Compliance
Cache Savings Tracking: prove the dollars the cache prevented
Cache Savings Tracking quantifies prevented spend in dollars daily, turning caching from an efficiency feature into a reported ROI.
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Audit & Compliance
Configuration Diff & Rollback: see the redline and rewind instantly
Configuration Diff & Rollback shows visual redlines and enables instant point-in-time recovery, making every configuration change safe to undo.
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Audit & Compliance
Configuration Version Control: Git-style history for every setting
Configuration Version Control gives every setting a Git-style, attributable history — who changed what, when, and exactly how.
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Audit & Compliance
Hide Keys: auto-redact sensitive data from logs
Hide Keys auto-redacts sensitive data from logs, letting you capture a thorough audit trail without turning it into a liability.
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Audit & Compliance
Prompt Logging: capture the full prompt and response
Prompt Logging captures the full prompt and response, making every interaction replayable and explainable for deep audits.
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Audit & Compliance
Endpoint: point logging exactly where you need it
Endpoint gives precise control over exactly where audit logs are routed, complementing the logger type with an exact destination.
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Audit & Compliance
Logger Type: send logs to S3, Splunk, Datadog, or your SIEM
Logger Type routes audit logs to S3, Splunk, Datadog, or your SIEM, fitting the platform into the tools your teams already use.
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Audit & Compliance
Corp Logging: the master switch for enterprise logging
Corp Logging is the master switch for enterprise-grade logging, the foundation every audit and compliance capability builds on.
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Audit & Compliance
Corp Filter: per-tenant control over which documents are in play
Corp Filter scopes retrieval per tenant, ensuring each draws only on the documents it's entitled to.
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Output Rendering
HTML Clean: sanitize markup before it's ever displayed
HTML Clean sanitizes markup before delivery, guaranteeing answers render safely and correctly in any web surface.
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Output Rendering
Stopwords: strip noise words at output time
Stopwords strips configured noise terms from output at delivery time, sharpening the final answer.
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Output Rendering
Unique Links: dedupe repeated source links
Unique Links dedupes repeated source links, keeping cited answers clean, readable, and professional.
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Output Rendering
Embed Links: inline source links right in the answer
Embed Links inlines source links so users can verify any claim by following the answer back to its source.
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Output Rendering
VA Format: shape answers for voice and telephony
VA Format shapes answers for voice and telephony, producing responses that work when spoken rather than read.
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Output Rendering
Log Alt: capture alternate generations for review
Log Alt captures the alternate generations the model considered, giving teams deeper insight for tuning and review.
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Output Rendering
Stream Plan: show the multi-step plan as it unfolds
Stream Plan reveals the system's multi-step reasoning as it unfolds, building trust during complex answers.
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Output Rendering
Relax Filters: loosen retrieval filters only when it's safe
Relax Filters conditionally loosens retrieval to recover answers when strict filtering would otherwise return nothing.
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Multi-Language
Default Language: the per-tenant language fallback
Default Language sets the per-tenant fallback locale, anchoring the multilingual experience with a sensible baseline.
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Multi-Language
Cross Language: answer in the user's language automatically
Cross Language auto-translates queries so a single knowledge base can serve a global audience in any language.
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Memory
Force Context: guarantee the conversation is always carried
Force Context guarantees conversational continuity in flows where every turn depends on the last, overriding automatic detection.
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Memory
Context Detect: automatically know when history matters
Context Detect automatically applies conversational memory only when a question actually needs it, keeping answers efficient and accurate.
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Memory
User TTL: per-tenant, user-isolated memory lifetimes
User TTL enforces per-tenant, user-isolated memory lifetimes, guaranteeing one user's context never bleeds into another's.
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Memory
Session TTL: how long a conversation stays alive
Session TTL controls how long a conversation persists, keeping state fresh and clearing it before it goes stale.
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Memory
Context Turns: how many turns of conversation the system remembers
Context Turns tunes how many prior turns the assistant remembers, balancing conversational coherence against relevance and cost.
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Memory
LG Timeout: bound language generation so it never hangs
LG Timeout bounds the language-generation step so conversations stay responsive and never hang.
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LLM Control
Timeout (per call): bound how long a single call can run
Timeout (per call) bounds how long any single call can run, keeping latency predictable and preventing one slow call from stalling a workflow.
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LLM Control
Images (multimodal): govern how the model handles attached images
Images (multimodal) brings image handling under governance, controlling how visual input is attached and processed alongside text.
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LLM Control
Prepend: inject system instructions ahead of the prompt
Prepend injects consistent system-level instructions ahead of each prompt, making standing behavior reliable across calls.
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LLM Control
Cache (per call): reuse model responses where it's safe
Cache (per call) gives fine-grained control over response reuse, cutting cost on safe steps while keeping critical calls fresh.
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LLM Control
Model selection: set the default LLM for the platform
Model selection sets the platform-wide default LLM that cascades to every agent, giving you one governed place to change models system-wide.
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LLM Control
Per-Call model selection: pick the right model for each step
Per-Call model selection right-sizes each step to the model it actually needs, optimizing cost and quality node by node.
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LLM Control
Streaming: show answers as they form, per node
Streaming shows answers as they form for a faster feel, with per-node control for contexts that need the complete response first.
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LLM Control
Min Tokens: a floor so answers aren't cut short
Min Tokens floors generation length so answers reach a useful, complete form instead of stopping short.
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LLM Control
Max Tokens: a hard cap on how much the model can generate
Max Tokens caps generation length, turning an open-ended cost risk into a predictable, bounded line item.
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LLM Control
Frequency Penalty: stop the model from repeating itself
Frequency Penalty discourages repetition, keeping answers clean, varied, and economical with tokens.
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LLM Control
Top-P: cap the model's word choices with nucleus sampling
Top-P caps the model's sampling pool, keeping output focused on likely choices and reducing erratic word selection.
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LLM Control
Temperature: dial answers from deterministic to creative
Temperature controls output randomness per call, keeping factual answers consistent while allowing creativity where it helps.
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Hybrid Search
Re-Sort priority values: apply your business priorities after retrieval
Re-Sort priority values let business rules shape the final ranking after retrieval, so authority and recency get the last word.
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Hybrid Search
KNN Vector query: bring your own custom vector search
KNN Vector query gives advanced teams full, custom control over semantic nearest-neighbor search via JSON.
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Hybrid Search
ELSER: sparse-encoder retrieval, configured your way
ELSER brings configurable sparse-encoder retrieval into the search mix, blending keyword precision with semantic understanding.
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Hybrid Search
Query Type: choose Lucene, vector, or hybrid retrieval
Query Type selects keyword, semantic, or hybrid retrieval so the search strategy matches your content and the way users ask.
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Intent & Routing
Cache KB: tie cached answers to the exact knowledge base they came from
Cache KB ties each cached answer to its originating knowledge base, ensuring reuse never crosses sources and serves the wrong content.
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Intent & Routing
Cache Context: only reuse an answer when the conversation matches
Cache Context binds reuse to the surrounding conversation, so cached answers are only served when the situation genuinely matches.
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Intent & Routing
Multi-Agent routing: send each question to the specialist that handles it
Multi-Agent routing directs each question to the specialist agent best equipped to answer it, raising quality across the whole system.
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Intent & Routing
Normal Cache: reuse auto-generated answers to cut cost and latency
Normal Cache reuses auto-generated answers for repeat questions, cutting both latency and token cost with a freshness window you control.
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Intent & Routing
Edit Cache: serve your hand-curated answers instantly
Edit Cache serves your hand-curated answers consistently and instantly, protecting the quality you invested in editing them.
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Intent & Routing
Intent Match Threshold %: how sure before the system commits to an intent
Intent Match Threshold % prevents confident misrouting by requiring real certainty before the system commits a question to an intent.
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Intent & Routing
Match Type: how the system decides what a question means
Match Type tunes how the system interprets user questions, from exact matching to fuzzy semantic similarity.
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Attribute Protection
Misinformation Tolerance: dial brand caution from rigid to standard
The Misinformation Tolerance slider expresses your brand's risk appetite as a single dial, from rigidly cautious to standard helpfulness.
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Profanity
Blocked Reply Text: control exactly what users see when content is blocked
Blocked Reply Text turns a refusal into an on-brand moment, replacing generic errors with a message you control.
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Profanity
Filter Mode: choose how profanity gets caught
Filter Mode lets each channel pick the profanity defense that fits — nuanced LLM moderation, fast native filtering, or off in trusted contexts.
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Answer Confidence
Force KB: refuse to answer from anything but your knowledge base
Force KB locks the assistant to your knowledge base, guaranteeing every answer reflects approved sources rather than the model's general training.
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Answer Confidence
Verbosity: one dial from terse to thorough
Verbosity gives you one dial to match answer depth to your audience, from terse expert replies to thorough explanations.
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Answer Confidence
Max Words: cap rambling answers before they lose the point
Max Words caps answer length so responses stay concise, on-point, and right-sized for their channel.
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Answer Confidence
Min Words: reject answers too short to actually help
Min Words filters out hollow, too-short responses so users only receive answers substantial enough to actually help.
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Answer Confidence
Minimum Confidence % for URL: suppress links the system isn't sure about
Minimum Confidence % for URL holds links to a stricter standard than text, suppressing them whenever the system isn't sure enough to be safe.
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Answer Confidence
Minimum Confidence %: the floor below which the system won't answer
Minimum Confidence % sets the hard floor beneath which the assistant declines rather than guesses — restraint turned into an enforceable guarantee.
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Answer Confidence
Warning %: flag a shaky answer instead of hiding the doubt
Warning % surfaces a candid low-confidence signal on shaky answers, letting users weigh them instead of trusting them blindly.
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PII & Sensitive Data
Trust Words: allow-list the safe terms so they're never redacted
Trust Words allow-lists known-safe terms so aggressive privacy controls never mangle legitimate content with needless redaction.
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PII & Sensitive Data
Out-of-the-box Detector Library: 13 sensitive-data categories on day one
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 build.
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PII & Sensitive Data
LLM-Based PII Detection: contextual catching of what patterns miss
LLM-Based PII Detection adds contextual judgment to privacy enforcement, catching the sensitive data that pattern matching alone would miss.
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PII & Sensitive Data
Pre-LLM Regex: redact sensitive data before the model ever sees it
Pre-LLM Regex deterministically redacts structured sensitive data before it reaches the model — protection at the earliest possible point.
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PII & Sensitive Data
PII Action: mask, flag, hide, or delete — you choose the response
PII Action gives you five precise enforcement options so every category of personal data is handled exactly as its sensitivity demands.
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Prompt Injection
Indirect Prompt Injection Protection: catch attacks hidden in your own documents
Indirect Prompt Injection Protection screens retrieved documents, URLs, and tool outputs for hidden attacks, closing the blind spot that direct-input filters miss.
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Prompt Injection
Blocked Word List: managed and custom terms you never want through
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 business.
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Prompt Injection
Blocked Word Action: decide what happens when a forbidden term appears
Blocked Word Action turns detection into enforcement, giving each tenant a predictable, policy-aligned response when a forbidden term appears.
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Prompt Injection
Prompt Injection Block Threshold: the line where a request gets refused outright
Prompt Injection Block Threshold draws the line where a request is too clearly malicious to clean and must be refused outright — the strongest tier of injection defense.
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Prompt Injection
Prompt Injection Removal Threshold: surgically strip the attack, keep the request
Prompt Injection Removal Threshold neutralizes attacks mid-stream while preserving the legitimate request — precise defense instead of a blunt rejection.
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Hallucination Prevention
Hallucinated Term Allowlist: closed-loop remediation in one click
Hallucinated Term Allowlist closes the loop — click a flagged term on the dashboard to allow-list it permanently, turning false positives into a one-time fix.
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Hallucination Prevention
Hallucination KW Removal: sentence-level surgery on ungrounded claims
Hallucination KW Removal strips individual sentences when their proper nouns aren't in the source — precision editing instead of blunt rejection.
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Hallucination Prevention
Re-Rank Min Coverage %: a floor on how much an answer is backed
Re-Rank Min Coverage % drops answers that fall below a coverage threshold — a hard floor beneath which a response simply won't ship.
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Hallucination Prevention
Total Coverage Weight: reward the sources that carry the answer
Total Coverage Weight weights passages by how much of the answer they actually support — concentrating grounding where it matters.
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Hallucination Prevention
Source Jump Penalty: stop answers stitched from unrelated docs
Source Jump Penalty penalizes answers stitched together across unrelated documents — a classic recipe for confident nonsense.
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Hallucination Prevention
Term Penalty: enforce the vocabulary the answer must include
Term Penalty penalizes answers missing required terms, giving you a direct lever on the vocabulary every answer must cover.
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Hallucination Prevention
Key Term Penalty: don't drop the names that matter
Key Term Penalty docks answers that omit the key entities present in the source — catching subtle drift before it ships.
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Hallucination Prevention
Check URLs: only cite links the source actually supports
Check URLs requires URL-level grounding so the assistant never invents or misattributes a link in its answer.
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Hallucination Prevention
Check Titles: grounding answers at the document level
Check Titles requires title-level grounding, tying answers back to the specific documents they came from for clean attribution.
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Hallucination Prevention
Re-Rank: putting the best evidence first
Re-Rank reorders retrieved documents by true semantic relevance, so the model reasons from the strongest evidence first.
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Hallucination Prevention
Semantic Score Threshold: proof the answer matches its source
Semantic Score Threshold enforces a minimum semantic match between the answer and its source — the core gate that blocks ungrounded claims.
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Retrieval Grounding
Max Raw Score: normalizing retrieval before re-ranking
Max Raw Score caps raw retrieval scores before re-ranking, keeping the scoring pipeline calibrated and outliers from distorting results.
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Retrieval Grounding
Snippet Size: how much of each source the model gets to see
Snippet Size controls how much of each source paragraph is forwarded as context — balancing completeness against token efficiency.
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Retrieval Grounding
Max Docs: the hard ceiling on what reaches the model
Max Docs caps how many sources reach the LLM per call — protecting answer quality and cost from context overload.
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Retrieval Grounding
Query Cache: reuse smart answers, stop paying twice
Query Cache reuses retrievals for identical questions within a window — cutting latency and token cost without sacrificing freshness.
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Retrieval Grounding
Date Penalty: freshness weighting that retires stale answers
Date Penalty quietly down-ranks stale documents so the model leans on what's current — without you manually pruning the knowledge base.
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Retrieval Grounding
Document Score Range: the relevance band that keeps answers on-topic
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.
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Perspective
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.
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Customer Impact
Why all four major U.S. carriers run on NeuralSeek
Verizon, AT&T, T-Mobile, and Comcast (Xfinity Mobile) all use NeuralSeek to power AI — from customer self-service to internal employee assistants. Here's why telecom trusts governed AI.
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Governance
The top 10 grounded AI governance programs in 2026 (ranked)
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 regulated enterprise environments, with NeuralSeek at #1.
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Governance
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.
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Economics
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.