Valiantsina H.

Great points, Jerome and Pat — context is everything. We've seen automation succeed when it's tied to a clear business outcome, not just 'let's automate everything.' Vern, if you're looking at sales/deal automation, we've done this recently with Salesforce CPQ + DocuSign for a trade association — automated quote generation, approvals, and e-signatures. Cut turnaround time significantly. If it's more infrastructure/DevOps — we handle that too (Terraform, Azure, CI/CD). Happy to share what worked (and what didn't) depending on your focus.

Hey Yann! Welcome. Splinemoon here — .NET and Azure team for enterprise backend and AI. Signal-based enrichment + CRM hygiene — sounds like a solid product. Curious — what's your tech stack? .NET, Python, something else? Also interested in how you handle GDPR compliance for intent data (EU-based ourselves). Let's connect.

Asad M. Asad, makes sense. Intent for emerging categories is hard because buyers don't know what to search for yet. We've seen the same. Quick question — on the technology side, what's your stack for agentic automation? .NET, Python, something else? The reason I ask: we specialize in .NET/Azure for enterprise AI (fintech/insurance). If you ever need help with backend, scaling, or integrations, happy to be a resource. No pressure — just exchanging notes.

Interesting problem — selling an "emerging category" product (agentic automation) that's hard to keyword. We've seen similar challenges with .NET/Azure development services — "backend modernization" is too generic, "microservices on Azure" too narrow. Not an ABM expert, but we've learned that intent signals often miss emerging categories because buyers don't know what to search for yet. Firmographics + direct outreach to likely buyers (based on role, company size, pain points) worked better for us. Would love to learn more about your product — sounds like something our enterprise clients (fintech/insurance) might need. Happy to exchange notes. What's your company?

For a startup wanting AI-driven + all-in-one, check out Attio (modern, flexible, great UX) or Close (built around email/calls). If you need more power and don't mind complexity, EngageBay or Pipedium (not fully AI-native but solid). Quick advice from someone who integrates these things with custom backends (ERP, product data, payment systems): Avoid "all-in-one" lock-in — you'll outgrow it faster than you think Prioritize API quality over features. Check their API docs before signing. Bad API = future pain Think about data portability — can you export everything (including metadata) when you leave? If you end up needing to sync your CRM with your product database, payment systems, or automate complex multi-step workflows that off-the-shelf tools can't handle — happy to chat. We build that kind of integration layer for B2B startups. Good luck with the selection!

This is such a sharp breakdown — you've articulated exactly where the AI "agent" promise hits reality. We deal with a very similar problem on the engineering side, but with different sources: ERP systems, payment gateways, LDAP, digital signatures, legacy databases with 6k-line stored procedures. The "Reach" problem is identical. A few things we've learned (not AI-specific, but integration-specific): On historical context — no, we haven't solved it without significant engineering. The pattern that works: event sourcing + a unified read model. But that requires each source to emit events, which most SaaS CRMs and ad platforms don't. So you end up with poll-based ingestion + idempotent processing, which is ugly but works. On multi-tenant for agencies — we've done both. Client-owned infra is cleaner for compliance but slower to iterate. Managing it yourself gets faster but you own the PII risk. Hybrid pattern: thin per-tenant ingestion layer (client's VPC) + centralized control plane. Is it solvable at scale? I think yes, but not with off-the-shelf connectors. The hard part isn't connecting — it's handling schema drift, rate limits, partial failures, and backfills. Once you build that substrate once, it becomes reusable. Most teams just rebuild it per project. You're not alone hitting this ceiling. The gap between "AI as copilot" and "AI as operator" is 90% data infrastructure, 10% prompting. What's your current stack for ingestion? Airflow? Dagster? Something custom?

100% agreed. We've seen this with dev shops too — building is the easy part (technically). Getting the right clients to trust you with their complex .NET/Azure project is a whole different game. What worked for us: case studies. Showing not telling. "We rebuilt Aquarium Insurance from legacy mess to microservices on Azure" opens more doors than any cold DM. Good luck with the distribution grind — it gets easier once you have one or two wins to point to.

What you're describing (headless CRM + AI operator) is exactly where we see the market going. The UI becomes invisible, but the integration layer becomes more critical than ever. That's where we live. Curious — what stack are you using for the headless layer? Kafka? Azure Functions? Something else? Good luck with the experiments — and if you ever need dev firepower for custom Salesforce extensions, we're here.

Hey Osayi, welcome! Impressive results with the webinar program. We're a .NET/Azure backend team (Europe, but work with US/Canada). Not a pitch — just noting that if any of your B2B tech clients ever need dev firepower (portals, integrations, automation), happy to be on your radar. Good to have you here.

That probably also changes how teams hire and grow engineers. Technical depth still matters, but communication, context-switching, and decision-making seem to be becoming much bigger differentiators now. Especially when products and teams start scaling faster than the underlying engineering processes — we see that become a real pain point for growing SaaS companies pretty often.