Guides
56 items
- Guide
Evaluating MCP Gateways: Buy Against the Specification, Not the Feature Grid
An MCP gateway's real job is to enforce, at your boundary, controls the Model Context Protocol already makes mandatory. That makes the strongest evaluation questions conformance questions with verbatim answers in the spec — not feature questions, where every vendor says yes. The current revision also changed the session model and deprecated a registration mechanism, so shortlists assembled from 2025 material are testing for the wrong things.
- Guide
Securing LLM Applications: Prompt Injection, the OWASP Top 10, Red Teaming, and API Gateways
The current OWASP list for LLM applications is the 2025 edition, and its first entry is still prompt injection — a class OWASP itself says has no fool-proof prevention. This guide works from that admission: what the platform guardrails from AWS, Microsoft, and Anthropic actually screen (and what their own docs say they skip), which architectural controls survive a bypassed filter, how to red-team the result, and what belongs at the API edge.
- Guide
The Unified GTM AI Stack: ABM, Lead Routing, and Predictive AI for Customer-Facing Teams
Most firms adopt GTM AI as scattered point tools, and federal survey data shows adoption stays narrow — most adopters use AI in three or fewer business functions. The durable alternative is a unified GTM stack built in four layers: resolved identity, predictive scoring, orchestrated routing, and service analytics that feed the loop. This guide covers each layer, the build order, and where unification honestly breaks down.
- Guide
Sectoral AI Regulation and RegTech: SR 11-7, NYDFS 500, and Automating Regulatory Work
The United States regulates enterprise AI through sector regulators, not a single AI law. Bank supervisors apply SR 11-7 model risk management, NYDFS applies its cybersecurity regulation, FDA regulates AI-enabled devices as products, and the FTC polices AI claims everywhere. Map each AI deployment to the sector rule that already covers it, then use RegTech to automate the tracking and reporting work.
- Guide
Multimodal Document Intelligence: Processing Documents, Charts, and Tables with LLMs
Enterprise documents are visual objects — tables, charts, stamps, and multi-column layouts carry meaning that plain text extraction destroys. Modern document intelligence layers three capabilities: deterministic extraction services, LLMs that read pages natively as images, and retrieval that spans text, tables, and figures. The architectural decision is which layer owns which fields, and where the money goes.
- Guide
Enterprise Voice AI: Contact Centers, Meeting Assistants, Analytics, and Deepfake Risk
Enterprise voice AI is really four buying decisions — contact center voice agents, meeting assistants, voice analytics, and voice-fraud defense — that share one speech stack and one threat model. This guide maps the platform choices across AWS, Azure, Google Cloud, and OpenAI, and explains why every voice program now needs a deepfake posture: regulators warn a voice can be cloned from a short audio clip.
- Guide
Multimodal AI in the Enterprise: Architecture, Video Understanding, and Use Cases
Multimodal AI has moved from research demos to metered enterprise spend. This guide explains how vision-language architectures actually work, what image and video inputs cost in tokens on the major platforms, where video understanding is production-ready today, and how to decide between a unified frontier model, a specialized perception service, or a hybrid of both.
- Guide
Vector Database Operations: Index Types and Zero-Downtime Migration
Pick a vector index by your update pattern and memory budget, not by benchmark screenshots: flat scans for exactness at small scale, IVF for memory-constrained batch workloads, HNSW for low-latency dynamic data, quantization when the corpus outgrows RAM. And treat migration as a dual-write, backfill, parity-check, cutover pipeline — never a big-bang export.
- Guide
Training Data and Feedback Loops: Labeling, Synthetic Data, and Human Feedback
Model choice is the least durable part of an enterprise AI stack; the data operation around it compounds. This guide covers the three levers that decide data-layer ROI — how you label (human, ML-assisted, or active learning), when synthetic data helps and when it misleads, and how to climb the feedback ladder from thumbs-up capture to preference optimization.
- Guide
Enterprise Embedding Models: Selection, Fine-Tuning, Dimensions, and Cost
Choose embedding models by benchmarking on your own labeled corpus, not a public leaderboard. Shortlist one or two API models and one open-weight model, measure hit rate, MRR, and nDCG on representative queries, then set dimensions deliberately — dimension count, quantization, and caching drive retrieval cost at scale more than the per-token price of any provider.
- Guide
AI Upskilling the Enterprise Workforce: Training Programs and Roadmaps
The strongest evidence on generative AI at work says the training budget belongs with the broad middle of the workforce, not just specialists: measured gains are largest for less-experienced workers. Build role-based tracks anchored in real workflows, teach prompting from vendor documentation, and measure work outcomes — not course completions.
- Guide
Evaluating AI Vendors and Models: Selection Roadmap, Benchmarks, Model Cards, and Lock-In
Pick AI vendors with a staged process, not a leaderboard: define requirements, shortlist against your own workload, read benchmarks and model cards as vendor-supplied evidence to be verified, and price in deprecation from day one. Model lifecycles now run as short as twelve months, so exit paths and abstraction layers belong in the evaluation itself, not the postmortem.
- Guide
AI Vendor Pricing and Contract Negotiation: Pricing Models, SLAs, and Renewals
AI vendor negotiation starts with the meter, not the discount. Learn the four pricing models and their fine print, what Bedrock, Azure OpenAI, and Vertex AI uptime SLAs actually commit to, which contract terms outlast the price — data rights, indemnity, deprecation — and how to run a renewal on evidence instead of anecdote.
- Guide
Making the AI Business Case: Board Presentations, Portfolio Management, and Realistic Expectations
A defensible AI business case prices the investment from published rates, forecasts benefits from measured evidence rather than vendor decks, and commits to portfolio discipline before the first dollar moves. This guide covers how to build that case, present it to a board, manage a suite of AI investments, and set expectations you can still defend a year later.
- Guide
Measuring AI ROI: Attribution, Dashboards, and the Metrics Executives Trust
Executives trust AI ROI numbers built on a counterfactual — a control group, a holdout, or a defensible baseline — and discount everything else. Anchor the business case in randomized evidence, attribute outcomes with controls and uplift analysis, separate time saved from output increased, and report cost, revenue, risk, and speed with every figure labeled measured, modeled, or assumed.
- Guide
AI Pilots and Maturity: Picking the First Project, 90-Day Metrics, and Why Pilots Fail
Run an AI pilot as a purchase of information, not a demo. Pick a workflow with a logged per-unit metric, baseline it before day one, measure against pre-agreed 30/60/90-day gates, and end with an explicit scale, iterate, or kill decision. Most pilot failures are decision failures — no owner, no metric, no gate — not model failures.
- Guide
AI Change Management and Adoption: Overcoming Resistance and Measuring Uptake
AI programs rarely fail at procurement; they fail at adoption. This guide gives enterprise leaders a working playbook for the human side of AI rollouts: diagnosing fear, skepticism, and inertia; running communications, champions, and early-win programs that actually change behavior; and measuring uptake with an instrumentation ladder that separates real adoption from login theater.
- Guide
The AI Center of Excellence Playbook: Operating Models, Funding, Tooling, and KPIs
An AI Center of Excellence succeeds or fails on four design choices: the operating model (centralized, hub-and-spoke, or federated), the funding mechanism (showback before chargeback), a tooling stack it standardizes rather than hoards, and KPIs that measure delivered value instead of activity. This playbook walks through each decision, the failure modes, and a staged path from launch to scale.
- Guide
Production ML Pipelines: CI/CD, DAGs, Event-Driven Architectures, and Error Handling
Production ML pipelines succeed on four decisions: make the training pipeline — not the model — your deployable unit; express every workflow as a DAG of idempotent, durable-handoff steps; adopt event-driven triggers only where a freshness SLA demands it; and engineer retries around an explicit taxonomy of transient versus permanent failures. This guide walks each decision with primary-sourced patterns from Google Cloud, AWS, and Azure.
- Guide
Model Monitoring in Production: Drift, Degradation Alerts, and the Tool Landscape
Production model monitoring comes down to four decisions: which signals you compute (data drift, prediction drift, data quality, feature attribution drift, performance), what reference data defines normal, what thresholds turn a metric into an alert, and which tool owns the loop. Decide the signal taxonomy and thresholds first — the tool is the last choice, not the first.
- Guide
LLM Observability and Safe Releases: Logging, Metrics, Canary, and A/B Testing
Treat every model, prompt, or routing change as a production release: structured logs for every invocation, four metric families (latency, tokens and cost, output quality, drift), canary rollouts with automated rollback criteria, and A/B tests reserved for proving effectiveness once stability is proven. The platform tooling already exists; this guide covers what to capture, what to alert on, and when to promote or roll back.
- Guide
Data Quality and Governance for AI: Contracts, Lineage, and Observability
Most production AI failures are data failures, and heroic cleanup does not scale. What scales is a four-layer control stack: validation rules that measure quality, data contracts that stop bad data at the producer boundary, observability that detects the failures you never wrote a rule for, and lineage that diagnoses incidents and documents processing for regulators.
- Guide
Responsible AI in Practice: Bias Testing, Explainability, and Ethics Training
Responsible AI fails as a poster and works as a control system. This guide turns three commitments into operating practice: bias testing built on NIST's three-category taxonomy of systemic, statistical, and human bias; explainability grounded in NIST's four principles and the SHAP, LIME, and attention-visualization toolbox; and ethics training run as a measured control on human judgment.
- Guide
Model Risk Management: Third-Party Models, Validation, Remediation, and Rollback
Model risk management is not a discipline you need to invent for AI. SR 11-7 defined it in 2011: validate every model you use — including the ones you buy — monitor it continuously, and keep the authority and the machinery to restrict, fix, or roll it back when it fails. This guide maps that framework onto third-party AI models end to end.