u/Responsible_Egg_3391

Been building outbound systems for B2B clients and this is the pipeline I run end to end. Sharing the whole thing because I'd rather have it torn apart here than in a client's dead campaign. Diagram attached — flow below.

The pipeline:

1. Define ICP — as a testable hypothesis, not a fixed profile. Every attribute is tagged "data" (from real customers) or "guess" (assumption to validate).
2. Deliverability setup, in parallel — separate domains, mailboxes, warmup. Kicked off at the same time as the ICP work because of the ~2–3 week warmup lag.
3. Pull companies matching the ICP → pull contacts at those companies → enrich → verify emails (hard gate — nothing unverified gets sent) → load into a central data store.
4. Draft copy with an LLM — email sequences, LinkedIn scripts, cold-call talk tracks, per segment.
5. Run 3 channels — email sequencer, LinkedIn automation, manual calls to high-intent accounts.
6. Track replies + performance → log to CRM → work the pipeline.
7. Feedback loop: reply data flows back into the ICP, turning "guesses" into "data" for the next campaign.

What I actually want your take on:

• Three channels for a lean operation — overkill? I keep going back and forth on whether cold calling earns its slot or whether I should run email + LinkedIn well instead of three thinly.
• Where do you put enrichment vs. verification? I verify after enrich, before the DB. Some people verify twice. Curious what breaks for you at scale.
• The feedback loop is the weakest part of my setup. In practice it's manual — I eyeball reply data and adjust. How are you closing that loop without it turning into a full-time analysis job?

What does your flow have that mine doesn't? Genuinely trying to find the step I'm missing before a client does.

Hey everyone,
I’m a GTM Engineer / RevOps professional looking to drastically speed up my daily workflow using Claude (Sonnet 3.5).
I already use it for basic tasks like writing standard regex, drafting simple formulas, and cleaning up basic Python/Javascript scripts for data enrichment. However, I feel like I'm barely scratching the surface of what's possible with advanced prompting, custom Projects/Artifacts, or specific "skills" built out for our domain.
I'm curious: Have any of you built specific methods, custom system prompts, or automated workflows inside Claude that make your GTM/RevOps work significantly faster?
Specifically, I’d love to know if anyone has found clever ways to leverage Claude for:
1 Complex Schema Alignment: Mapping messy data sources into Salesforce/HubSpot architectures without spending hours manually mapping fields.
2 API Integration Logic: Accelerating the setup of webhooks or middleware (like Zapier, Make, or custom API endpoints).
3 Advanced Data QA: Feeding it large chunks of data or log files to quickly spot routing errors, lead-scoring bugs, or sync failures.
4 Writing Complex Documentation: Automatically turning a messy piece of code or a custom workflow into clear internal documentation for the team.
If you have any specific prompts, framework mental models, or Projects setups you’ve built out that you'd be willing to share, I would map them to my workflow in a heartbeat.
Thanks in advance!

Hey everyone,
I’m a GTM Engineer (focused heavily on RevOps automation, integrations, internal tooling, and data pipelines) and I’m looking to fully integrate Claude Code into my daily workflow.
Given that our domain often involves a mix of chaotic API integrations (Salesforce, HubSpot, Marketo, Segment), custom scripts, data transformations, and internal dashboards, I want to make sure I'm maximizing Claude's capabilities.
If you are a GTM/RevOps Engineer, Analytics Engineer, or full-stack dev doing heavy integration work, how are you using it? Specifically:
API Chaos & Schemas: How well does it handle navigating messy, poorly documented third-party REST/GraphQL APIs or writing robust error-handling for webhooks?
Context Window Strategies: How do you feed it massive payloads, JSON schemas, or CRM object metadata without hitting a wall or burning tokens inefficiently?
Testing & Data Validation: Do you trust it to write data validation scripts or unit tests for sync pipelines?
Your Stack: What does your specific stack look like alongside Claude Code (e.g., Python, Node.js, dbt, SQL, etc.)?
Would love to hear about your favorite prompts, specific workflows, or even things it isn't good at so I can avoid those rabbit holes.
Thanks in advance!