Analysis of AI is Killing B2B SaaS and HN discussion in the context of Pulsify.
AI-assisted "vibe coding" lets non-technical users prototype custom solutions rapidly. B2B SaaS companies that remain rigid feature-sets face replacement risk. The survival strategy: become a platform users build on, not a product they use.
- Build vs. buy economics shifting: A company paying $500k/year for niche software built a replacement in days
- Feature subset problem: Users pay for bloated suites when they need 10% of features
- Counterpoint: Companies still want to "pay you so we don't have to wrestle with this" - maintenance burden and risk transfer have value
- Service over software: Customers care about outcomes, not implementation
Pulsify isn't looking for sellers who want to code. It's offering an LLM sellers control that codes for them.
This reframe is critical. Pulsify embraces vibe coding rather than competing with it. The AI-assisted development happens inside the platform, running on infrastructure users can't casually replicate.
- SP-API + Ads API integration
- EventBridge → SQS event pipelines
- Sandboxed JavaScript execution
- Auth flows, rate limiting, error handling
- LLM that writes automations for users
- Users describe intent in natural language
- Code runs in Pulsify's managed environment
- The "vibe coding" happens inside the platform
Amazon keeps these worlds separate:
- SP-API: orders, inventory, pricing, notifications
- Ads API: campaigns, spend, ACOS, targeting
Pulsify creates derived data by joining them:
- "I increased ad spend on this ASIN → here's what happened to organic rank, inventory velocity, and margin"
- "This pricing change affected my ACOS by X"
- Cross-domain correlations Amazon doesn't surface
Amazon knows what changed (price went to $X). Amazon doesn't know why:
- What signal triggered the change
- What rule or automation fired
- What the competitive context was
- What the outcome was
Over time, this becomes valuable:
- "Show me every time I lowered price in response to competitor X and what happened to sales velocity"
- "What pricing patterns worked during Q4 vs Q1?"
- Training data for the LLM to get smarter about your business
Pattern: Systems of record hold data that accumulates value over time. Leaving means losing history that's hard to reconstruct.
Examples: Salesforce (customer relationships), QuickBooks (financial transactions), Stripe (payment history), GitHub (code history)
For Pulsify: The unified ads + operations dataset, combined with decision history, creates switching costs. Leaving means losing:
- The joined view that doesn't exist elsewhere
- Historical correlations and patterns
- The automation's "memory" of what worked
| Layer | What It Is | Why It Matters |
|---|---|---|
| Infrastructure | SP-API, Ads API, event pipelines, sandbox | Hard to replicate with casual AI tools |
| AI Layer | LLM writes automations for users | Embraces vibe coding as feature |
| Unified Data | Ads + operations joined | Only place this exists |
| Decision History | Why things happened, not just what | Accumulates value, creates lock-in |
The first two layers are defensible. The last two create switching costs.
February 2025
I do sympathize with the build culture, we are moving there, however it will take time and, it will take a lot of electricity for the masses to vibe-code. So, infrastructure yes and Unified Data, definitely yes.
AI layer and Decision History are more customer oriented. The assumption here is, the Amazon Seller is tech savvy, analytics driven and believes in AI. Is this the case?
Thoughts of an Amazon seller:
I am an employee in a small company that has an Amazon channel - I kind of understand this stuff (maybe I should find another job). Can I sell it to the team? Someone told me this is a good system to try. It is said to automate stuff with Amazon, managing all these ASINs is time consuming.
The question is, will the recommendation/actions in Pulsify, move the needle? (what the needle represents?)
Lets give it a go, can I express (what I think is) my need and will the AI LLM address it? Is there a risk? How to measure success?
Lets start with low hanging fruit, on something I can measure directly & it will make an impact fast (and that is?). Then, I can fine-tune. I am already busy to get the product to the DCs.