34 stacks indexed · Curated tool combinations for ops leaders — what to deploy together, in what order, for which use case.
Run an account-based motion end-to-end — from intent signal capture through prioritized outbound through deal-cycle conversation analysis — for an enterprise B2B SaaS team chasing $100K+ deals.
A 3-10 person ops team running always-on AI agents over its existing SaaS, with enrichment, an approval gate, and no engineer on the org chart.
Run an AI-led outbound motion — list build, account research, sequence drafting, sending, and CRM sync — with a human QA layer between the agent and the inbox, for a $2-20M ARR B2B team replacing one to three SDR seats with software.
S 
Run a dedicated sourcing function — search, contact discovery, outbound sequencing, rediscovery, attribution — for a team of 2-8 sourcers filling senior, technical, or specialized roles that do not close from inbound.
A support org deploying an AI agent where the number on the invoice, the number in the board deck, and the number of customers who actually got helped are the same number.
D Z P 
P +1 Run AI-enriched, ICP-scored outbound at scale — from list-build through personalized first-touch through CRM sync — for a $5-50M ARR B2B SaaS sales team.
Run AI-augmented recruiting end-to-end — sourcing, screening, scheduling, structured interviews, debrief synthesis — for a 100-1,000 employee tech company hiring 50-200 roles per year with a small recruiting team.
A modern CRM operating system for the 2026 founders and RevOps leaders who don't want to start with Salesforce or HubSpot — built around AI-native primitives from day one.
Running high-volume cold outreach on isolated sending domains without spending the primary domain's reputation — domain and mailbox provisioning, warmup, list verification, send rotation, and the reply-to-pipeline handoff.
S I Capture every customer conversation, surface deal signals automatically, and turn rep coaching into a daily practice instead of a quarterly event.
A CS/onboarding team compressing time-to-value with structured plans, customer-facing portals, and in-product guidance.
V A mid-market-to-enterprise CS org instrumenting health, surfacing risk early, and protecting GRR through the renewal cycle.
Z P Identify expansion signals before the customer asks, surface churn risk before the renewal call, and turn customer success into the second-largest revenue channel after new business.
AmLaw-grade e-discovery review at scale
Enterprise contract lifecycle with AI-assisted drafting and review
Small GTM-engineering team automating signal-to-action loops — from community and product signals through enrichment and routing through CRM sync.
Hourly / frontline at-scale hiring
A remote-first hiring team that has to demonstrate identity continuity from application through onboarding, not just run a background check at the end.
C Lean in-house legal team layering an AI copilot and contract review onto an existing CLM
Turn an inbound demo request into a booked, correctly-owned meeting in under 60 seconds — for a B2B SaaS team on Salesforce taking 100+ form fills a month.
Record, score, and debrief every interview against one rubric on Greenhouse — with the recorder chosen by the ATS and conferencing platform you already run, now that the category has consolidated into them.
P Deploying legal AI inside an AmLaw or mid-size firm under conflicts, ethical-wall, and court-certification obligations rather than on the honor system.
L Issuing and defending preservation when half the responsive data is AI prompts, chat messages, and hyperlinked cloud files rather than email and attachments.
Solo in-house counsel running legal ops single-handed
B A complete AI-native legal operating system for in-house teams running 10-50 attorneys with mid-market contract volume and litigation overflow to outside counsel.
Partner-led growth with ecosystem account-mapping — surface overlaps between your book of business and your partners', route warm intros and co-sell assists through Salesforce, and catch the digital signals that precede partner-sourced pipeline in Common Room.
Plaintiff PI firm running intake, case workup, and demand assembly with AI in the loop
S E E PLG SaaS company layering a sales motion onto self-serve usage — surfacing product-qualified leads, routing them to the right rep, and closing expansion conversations from usage signals.
A CS and product team instrumenting in-product adoption so CSMs act on what a user did, without buying the same in-app guidance capability from three vendors at once.
Annual GTM planning run end to end — territory and quota carve, capacity model, comp plan, and the forecast commitment that gets measured against it.
C A CS team covering a long tail of accounts with automation, in-app guidance, and AI instead of a CSM per account.
Run a 3-30 recruiter perm, contingency, or retained search desk where client business development and candidate delivery come off the same records — job orders won by outbound, filled from a mix of owned database and external search.
L Run a standardized technical screening pipeline for 20-200 engineering hires a year — async assessment, a human-led live screen, candidate identity verification, and one structured record in the ATS.
K A GTM team that already runs Snowflake or BigQuery and wants segment definitions to live in dbt rather than in a vendor's audience builder — with reverse-ETL activation instead of a vendor-owned CDP.
