The words founders Google before they buy.

A whole business function (Sales, Content, Ops, Support) operated for you by AI agents on a monthly retainer, instead of being built with a salary stack.

A part-time Chief AI Officer engagement that gives funded teams strategic AI direction without the cost of a full-time executive hire.

A part-time Chief Marketing Officer on retainer who runs strategy without taking a permanent seat, often paired with an AI Content Department for execution.

A part-time COO on retainer who gives funded teams the operations leadership they need without the cost of a full-time C-suite hire.

The annualized value of a customer contract, used to compare deal sizes across multi-year, annual, and monthly contracts as a foundational SaaS sales metric.

Cold outbound (email, LinkedIn, sometimes voice) where an AI agent handles every step from prospect research through follow-up, with a human on the warm-reply handoff.

An AI agent that handles SDR work end to end: sourcing, enrichment, personalization, sequencing, and follow-up until a prospect replies.

The discipline of getting cold outbound emails into the inbox, not the spam folder. Covers domain warming, sending volume caps, reputation, and spam-trap monitoring.

Gradually building sending volume on a new domain over weeks so spam filters trust it. Required before any meaningful cold outbound from a fresh domain.

Behavioral signals indicating a prospect is in-market for a solution. Sources include website visits, search behavior, content downloads, and third-party providers.

The process of attaching additional context (firmographic, demographic, technographic, behavioral) to a raw lead so outreach can be relevant.

Total pipeline value divided by quota for an upcoming period, with healthy coverage at 3x to 4x of quota and lower ratios indicating a likely miss.

Growth motion where the product itself acquires and expands users through free signups, viral loops, and freemium to paid conversion. Sales and marketing support the product instead of leading it.

The percentage of sales reps hitting quota in a given quarter, with healthy SaaS sales orgs running 55 to 65% and under 40% indicating broken quotas or mismatched reps.

Tools that identify anonymous B2B visitors to a website by company and sometimes person, so outbound can target real intent signals.

The percentage of cold outbound emails that get a reply, positive or negative. Honest industry rate is 1-2%; AI-personalized cadences run 4-6%.

The schedule and channel mix of an outbound sequence: which messages go out on which days, mixing email, LinkedIn, and occasionally voice.

MQL is a contact with marketing engagement. SQL has passed handoff criteria and is actively worked by sales. The line between them is where most pipeline arguments happen.

The total committed dollar value over a contract's full term, with multi-year deals booking TCV higher than ACV and bookings typically reported in TCV.

A positive response from a prospect to outbound that is qualified enough to hand off to a human rep for a discovery call.

A continuously running content production layer (articles, social, landing pages, email) operated by AI agents on a single retainer, instead of a marketing team plus an agency.

Google AI-generated summary at the top of search results, citing a few sources. Being cited drives high-quality traffic; not being cited reduces clicks.

An automated workflow that drafts, schedules, and publishes social content (LinkedIn, X, Instagram) on cadence, with the founder approving in minutes rather than writing from scratch.

An AI writing model fine-tuned or prompt-tuned against a brand existing copy so output preserves voice, style, and positioning at scale.

Google framework for evaluating content quality across Experience, Expertise, Authoritativeness, and Trustworthiness. Higher E-E-A-T pages rank better.

Producing hundreds or thousands of search-targeted pages by combining a content template with data inputs, so each page targets a long-tail query.

Machine-readable metadata that tells search engines what a page is (Article, Service, FAQ, Product). Powers rich results and AI Overview citations.

An automated reporting workflow that pulls source data (Stripe, HubSpot, Notion, banking), refreshes live dashboards, and drafts the narrative for board updates without the COO stitching it on Sunday.

Total revenue divided by number of orders. E-commerce lever where bundles, upsells, and free-shipping thresholds raise gross profit without raising acquisition cost.

Total revenue divided by number of customers or accounts. Used to track price-mix shifts over time and identify upsell opportunity inside specific cohorts.

AI-generated written commentary attached to a dashboard or board update that explains what numbers moved, why, and what to do about it.

Months required to recover customer acquisition cost from a customer through gross profit. Healthy SaaS lands under 12 months. Above 18 months signals unit economics at risk.

Percentage of customers or revenue lost over a period. The most important SaaS metric most ops teams cannot calculate accurately.

MRR sliced by acquisition cohort (signup month or quarter) so each cohort retention and expansion can be measured against others. Foundational SaaS ops metric.

Percentage of customers from a starting cohort still active after N months. Truth-teller of product-market fit, and the upstream source of net dollar retention.

The inevitable end state of every dashboarding tool deployed without a function to own it. Looker, Tableau, Metabase boards drift out of date within a quarter and nobody trusts them.

Average number of days a company takes to collect payment after a sale. Critical metric for B2B services, agencies, and any business invoicing on net terms.

New revenue generated from existing customers through upsell, cross-sell, or seat expansion, the engine behind net dollar retention above 100%.

Total value of goods transacted on a marketplace before commissions and fees. Headline marketplace metric where take rate times GMV equals platform revenue.

An AI agent trained on a company internal knowledge (wiki, Notion, Drive, Slack history) that answers tier-1 internal questions so engineers and operators are not the help desk.

Lifetime value of a customer divided by cost to acquire them. Healthy SaaS is 3:1 or higher with sub-12-month payback.

Revenue from a cohort of customers a year later divided by their original revenue, including expansion. Above 110% is good SaaS; above 130% is exceptional.

A prospect who has demonstrated value-realizing behavior inside a product (key actions, usage thresholds) and is therefore qualified for sales contact based on usage rather than firmographic fit.

The function that unifies sales, marketing, and customer success operations under one roof, owning CRM, reporting, funnel hygiene, and pipeline forecasting.

Percentage of GMV the marketplace keeps as revenue through commissions and fees. Higher take rates accelerate economics but stress the supply side and invite disintermediation.

An AI agent trained on a company knowledge base, product docs, and policies that handles routine support questions without human involvement.

Scoring customer accounts on probability of cancellation using usage signals, ticket sentiment, engagement drops, and billing events so the team can intervene early.

A post-interaction survey score, typically 1 to 5 stars or thumbs up/down, that serves as a lagging indicator of support quality with healthy SaaS support running above 90%.

An AI agent ingested with a company documentation, help articles, and historical support transcripts so its answers stay grounded in actual product behavior.

Customer support inside a SaaS context where every ticket carries account-specific context (plan tier, integrations enabled, API behavior, admin permissions) that must be read before responding.

A classification system for support tickets by complexity, with Tier 1 covering routine queries, Tier 2 handling intermediate debugging, and Tier 3 escalated to engineering or security.

A numerical vector that represents text, images, or other data so AI models can compare meaning, with similar items ending up near each other in vector space.

Training an existing AI model further on a specific dataset to specialize it for a domain. Used for brand voice, internal copilots, and domain-specific tasks.

Hard-coded rules and filters that constrain AI agent behavior, layered above the model itself to enforce policy the model cannot be trusted to follow on its own.

An AI model confidently generating false information, the single biggest production risk for customer-facing AI, mitigated by grounding, guardrails, and supervision.

The per-token cost of running an AI model in production, tracked as dollars per million input tokens and dollars per million output tokens, defining the unit economics of any AI workflow.

A large language model deployed on private hardware (Llama 3, Qwen, Mistral) rather than accessed via a hosted API like OpenAI or Anthropic.

AI models that handle multiple input and output types: text, image, audio, and video, with GPT-4o, Claude 3.5 Sonnet, and Gemini 1.5 Pro as the current frontier examples.

An AI agent running on hardware controlled by the buyer (on-prem server, local workstation, isolated VM) with no inference traffic leaving the network.

A security attack where an attacker manipulates an AI agent's instructions through user input, critical to defend against in any customer-facing AI system.

AI technique where the model retrieves relevant context from a document store before generating, so answers stay grounded in your data instead of hallucinating.

Search that retrieves by meaning rather than exact keyword match. Uses vector embeddings to compare query intent against indexed content. Foundation of modern AI copilots and internal search.

A database built to store and query high-dimensional vector embeddings by semantic similarity, foundational for RAG and semantic search systems.

HIPAA contract between a covered entity (healthcare provider, payer) and a business associate (vendor handling PHI). Required before any PHI-touching workflow can begin.

California Consumer Privacy Act and successor CPRA. Grants California residents rights over their personal data including opt-out of sale, deletion, and access. Other US states have followed.

Contract between a data controller and processor required under GDPR. Defines scope, security measures, subprocessors, and liability.

EU regulation on personal data. Requires lawful basis for processing, data subject rights, breach notification, and DPO appointment for larger processors.

AI agents configured for healthcare workloads where PHI is involved, requiring a signed BAA, sometimes on-device deployment, and clinician escalation guardrails.

Hong Kong Monetary Authority. Regulates banking and payment services in Hong Kong, and the key authority for fintech operating in HK including stored value, retail payment, and virtual banking.

AI agents that handle Know Your Customer and Anti Money Laundering workflows for regulated financial businesses, often deployed on-device to keep customer PII inside the perimeter.

Singapore central bank and integrated financial regulator. Runs a fintech sandbox program and requires licensing for payment services, digital banking, and digital token issuance.

Payment Card Industry Data Security Standard. Required for any business storing, processing, or transmitting card data. Twelve main requirements across six control areas.

AICPA audit framework covering security, availability, processing integrity, confidentiality, and privacy. Enterprise SaaS sales gate.

EOI standard fractional engagement starting cadence. Days 1-3 audit, 4-10 build, 11-14 handoff. By end of week 4 the department is operating autonomously.

A half-day workshop and written roadmap that maps a company AI opportunities to its stack, team, and goals with prioritized recommendations.