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If your apps aren’t talking to each other, your business is paying the tax. 🛑
Most growing companies don’t have a software problem—they have a connection problem.
They invest in multiple SaaS platforms, separate their customer pipelines from their databases and operational systems, and then rely on people to manually move information between tools that should already be connected.
At Zorynt AI, we engineer the infrastructure that brings those systems together.
We are a technology and systems company designing event-driven digital infrastructure around the way a business actually operates. We don’t force-fit generic software onto an operation. We examine how information moves, identify where systems disconnect and manual handoffs create friction, then engineer the architecture required to make those platforms operate as one coordinated environment.
That work includes custom applications and SaaS platforms, AI workflow automation and agents, API and webhook integrations, relational data infrastructure, document and data intelligence, dashboards, portals, governance, and secure technology implementation.
Watch the structural reveal below to see how disconnected systems become connected infrastructure designed to operate reliably and scale.
🔗 Explore our services or send us a DM to start a conversation.
saasdevelopmentsystemsarchitecturedatainfrastructureBusiness OperationsBusiness Workflow AutomationSoftware DevelopmentMakeOracle DatabaseVapi
The post highlights that disconnected apps act like a hidden tax on the business. That idea stood out because it frames integration loss as a real cost.
What often gets missed is the need for ongoing monitoring of those integrations to catch data drift or API failures before they...
Great point, Abdul. While continuous monitoring sounds great in theory, setting up lightweight health checks for every API can quickly turn into its own form of "infrastructure tax." Over-engineering observability for non-critical apps often generates more alert fatigue than...
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Before you add AI to your product, find the screen where your users already copy and paste.
That's usually where the real AI feature lives. Not a chatbot in the corner, but a step people already do by hand: summarising a ticket, drafting a reply, tagging an upload, pulling fields out of a PDF.
Here's the order I follow when a founder asks me to add AI to an existing SaaS:
1. Pick one workflow, not "AI everywhere." Watch where users leave your app to paste something into ChatGPT and come back. That round trip is your feature.
2. Keep the model behind your own API. One service owns prompts, retries, logging and cost limits, so you can switch providers without touching the frontend.
3. Make it tenant-aware from day one. Each customer's data, prompts and usage stay scoped to their account, or the first enterprise security review will stop you.
4. Ship a draft, not a decision. Let the AI suggest and the user approve. You earn trust and get real feedback before you automate anything.
5. Measure accepted output, not tokens. If users rewrite most of what it produces, the prompt or the context is wrong, and that's your real roadmap.
Most AI features that flop didn't fail because of the model. They were bolted onto the wrong step.
What's the one task your users still copy into ChatGPT?
Watching where users paste into ChatGPT and come back is such a practical way to find the real feature. Most teams start with the chatbot instead.
Most inbound bottlenecks aren't caused by low lead volume—they happen during triage.
When inquiries roll into a Google Sheet or CRM, operations teams often waste 10–15 minutes per submission reading through messy paragraphs, deciphering budget mismatch, and manually deciding who gets an immediate call vs. a standard rate sheet.
Instead of adding a bulky CRM add-on, I documented a lightweight 3-step automation pipeline built inside Make:
Intake & Extraction: An active listener triggers immediately upon a new submission, extracting core variables: client scope, target deliverables, timeline, and stated budget.
Context-Aware Evaluation (Gemini 2.5 Flash): The payload passes through an AI evaluation node with strict guardrails:
Evaluates budget against minimum agency criteria.
Assigns a triage score: Hot (immediate priority), Warm (nurture), or Cold (disqualified/template reply).
Generates a strict 15-word operational summary—no long paragraphs.
Determines an automated next step (e.g., routing to an account lead or flagging for asynchronous follow-up).
Data Return & Clean Pipeline: Appends the structured classification directly back to the master operational tracker, keeping the sheet filterable and clean.
The outcome: The sales and operations team can filter the entire board by priority status in two clicks, cutting first-touch response time to minutes without manually reading every single submission.
I’ve documented the full scenario map, execution logic, and data schemas below.
Curious how other agency operators and automation leads here handle their lead enrichment layer—are you doing AI scoring at the form level, or later in the CRM pipeline?:
For the budget gate, I'd keep an unstated budget separate from one explicitly below your minimum. Does the Make route ask for clarification when the amount or currency is missing, so a potentially suitable lead doesn't fall into Cold by default?