What "Purpose-Built CRM" Actually Means
A purpose-built CRM is sales infrastructure designed around how your team actually works, rather than a general-purpose platform you configure and work around.
It can be built from the ground up (our use case) or as a thin agentic interface layer against your current systems to enable safe real-world agentic workflows in complex enterprise environments. So if that’s you - keep reading, this article applies to everyone.
The difference isn't just "fewer features." It's a different design philosophy. Instead of starting with everything a sales team might need and hiding what you don't use, you start with what you actually need and build exactly that.
For us, that means:
- Contact, lead, and opportunity tracking - the core record-keeping that any CRM does
- Automated AI research on inbound leads - when a lead comes in, the system enriches it automatically rather than requiring manual lookup
- Resourcing and forecasting built directly in - not bolted on through integrations, but native to how we think about pipeline
- Nothing we don't need - no complex approval workflows, no granular permission hierarchies, no plugin ecosystem to manage
What's explicitly excluded: enterprise permission systems, workflow automation engines, integration marketplaces. Not because these are bad - they're valuable for organizations that need them. We don't.
And if we do, the system is a lightweight clean business engine that can be update for our needs- we aren’t paralyzed on a 25 year old legacy data model shared with millions of other people, solving no ones needs due needing to solve everyones.
Why wasn’t this a problem before? Build the system we needed was too hard, too expensive, too fragile. No longer.
Why We Stopped Using Salesforce
We used Salesforce for years. Enterprise pricing, enterprise complexity, enterprise assumptions about how work should flow. And like most companies our size, we used maybe 15% of what we paid for.
The CRM was built for a world where you needed software to enforce process. Approval chains. Required fields. Workflow automation that was really workflow restriction. The assumption was that without the system forcing compliance, salespeople would go rogue and data would be chaos.
That assumption made sense when the alternative was spreadsheets and memory. It makes less sense when AI tools can synthesize information from wherever it lives, when the friction of "log this in the CRM" actively degrades data quality because people avoid it or fill fields with garbage to satisfy requirements.
We weren't getting value from Salesforce. We were paying for complexity that slowed us down, and the data quality was still garbage because the fundamental model was wrong.
So we built something different.
What We Learned Building It
CRM#5 - that's what we are calling it internally - took five days to reach functional state. And by function: a salesforce compatible lightweight CRM (for usability) extended with revenue forecasting and AI assistance on key sales flows. Lead-> Accounts/Contacts/Opportunities.
At a trade show we created hierarchical campaign management with revenue attribution and customizable multilingual lead forms for our iPads (English, Korean, and six other languages). In 4 hours.
It’s a stack with the fewest moving pieces and most industry acceptance: React/Next.js, React Server Components, Postgres(Supabase). Deployed on Vercel and Supabase cloud. And using Vercel’s AI library to use the LLM of your choice.
The common accepted platform means it’s extremely agentic friendly. This means we can extend it via Claude Desktop’s Code feature. It’s build, tested, and managed agentically with Rangle’s software talent tending the agentic herd.
What was faster than expected: The core functionality - opportunities, contacts, activity logging, pipeline views - came together quickly. When you're not building enterprise permission systems and workflow engines, there's just less to build. The AI-assisted lead research was straightforward to integrate because the system was designed with that capability in mind from the start.
LLMs know CRMs EXTREMELY well. 25 years of training data! This makes a huge difference.
What was harder than expected: The small UX details that make a CRM actually pleasant to use - those take iteration. The difference between "technically works" and "people will actually use this" is real and can't be skipped. We're still refining. But it was also closer than expected, and in some cases better that what we would have done. LLMs have a lot of CRM training data.
The honest tradeoff: CRM5 is dramatically smaller than Salesforce. But it covers everything we actually used, plus capabilities Salesforce doesn't have that matter specifically to us - like the AI-assisted lead research. We stopped paying for complexity we worked around and started owning infrastructure we can evolve.
When This Doesn't Make Sense
Honestly, if you want agentic revenue operations you need the thin agentic interface layer regardless. Legacy SaaS is not AI-capable. That said, it’s doesn’t make sense if you aren’t looking to change how you do revenue ops.
For teams with large sales organizations, complex approval requirements, or regulatory compliance needs - Salesforce and HubSpot still make sense. They've spent years building that infrastructure. If you need SOC 2 compliance out of the box, if you have 50 salespeople who need different permission levels, if your sales process requires enforced stage gates - use the enterprise tools. They exist for good reasons.
However these tools are awful at agentic, and they will charge you more and lock you into their ecosystem for continue to use substandard legacy SaaS. We can build thin layers so fast now, keep the engine for compliance, but move the UX to something transformational.
The Broader Pattern
This isn't really just about CRM. It's about what happens when building custom infrastructure becomes faster than configuring general-purpose products.
We built a CMS the same way - Tapdance, an agentic CMS that covers 50-60% of what Contentful and Sanity do, built in two days. Same principle: own the code, evolve it with AI tools, stop paying for complexity you don't need.
The article I mentioned predicted the GTM stack would collapse under its own weight. I think that's directionally right, but it misses the mechanism. It's not that these tools stop working. It's that the "build versus buy" calculation has shifted. Building is fast now. Buying means accepting someone else's assumptions about what you need. And you have to rebuild your user interface layers to be agentic-enabled. The interface layer is where agentic wins and loses. The agents are good enough- it’s the orchestration, collaboration, and evolution where they fall down.
The teams that figure out how to build and own their infrastructure will move faster than teams still navigating enterprise admin panels. That's not a prediction about 2026. It's already happening.
What We're Still Figuring Out
Nobody has the complete playbook for this yet. We're learning as we go. Be we have a pretty good playbook already with 10 SaaS and eCommerce systems build and shipped in 6 months.
Some other lessons:
- All CRMs become ERPs. We’ve just added accounting to CRM#5 and need to rename it.
- Operations is ridiculously cheaper. Entropy is massively reduced because changes are comprehensive (thanks to the speed and agentic context) Engineers look at the user request for features AFTER it has been build).
- Teams are radically different. Design is radically different. The SDLC is radically different - all upcoming blog posts!
If you're thinking through similar questions for your own team - whether the build-versus-buy calculation has shifted for you, what "purpose-built" might mean in your context - let’s talk and we can compare notes.