Growth & Strategy
Personas
SaaS & Startup
Time to ROI
Short-term (< 3 months)
Last month, I had three different startup founders ask me the same question: "Does Lindy.ai support real-time inference?" All three were convinced this was the make-or-break feature for their AI automation strategy. Two of them were already planning their entire product roadmap around it.
Here's the uncomfortable truth I shared with each of them: real-time inference is probably not what your business actually needs right now. After six months of experimenting with AI automation across different client projects, I've learned that most founders are chasing the wrong metrics when it comes to AI implementation.
The reality? Most successful AI workflows don't need real-time processing at all. They need smart automation, reliable execution, and results that actually move business metrics. Speed is often the least important factor in the equation.
In this playbook, you'll discover:
Why real-time inference is often a distraction from actual business needs
The three types of AI workflows that actually drive revenue (none require real-time processing)
My framework for choosing the right AI automation approach for your specific use case
When real-time processing actually matters (spoiler: it's rarer than you think)
How to build profitable AI workflows without getting caught up in technical specifications
Let's dive into what the AI automation industry doesn't want you to know about real-time processing.
Technical Reality
What every AI platform promises vs. what your business needs
Walk into any AI conference or browse through startup pitch decks, and you'll hear the same buzzwords repeated like a mantra: "real-time inference," "millisecond response times," and "instant AI processing." The entire industry has convinced itself—and founders—that speed is the ultimate differentiator.
Here's what every AI vendor will tell you:
Real-time inference is critical for competitive advantage
Faster AI responses lead to better user experiences
Batch processing is outdated and inefficient
Your customers expect instant AI-powered results
Real-time capabilities future-proof your tech stack
This narrative exists because it's easier to sell speed than to sell results. Real-time inference sounds impressive in demos and creates a clear technical differentiator that sales teams can pitch. It's measurable, it's flashy, and it makes CTOs feel like they're building cutting-edge systems.
But here's where this conventional wisdom falls apart: speed without purpose is just expensive engineering. I've seen startups burn through six-figure budgets building real-time AI systems that process data no human will ever see in real-time. They're optimizing for theoretical use cases that don't exist in their actual business model.
The real question isn't "How fast can we process this?" It's "What business outcome are we trying to achieve, and what's the minimum viable speed to get there?" Most of the time, the answer isn't milliseconds—it's reliability, accuracy, and integration with existing workflows.
This obsession with real-time processing is leading founders down expensive technical rabbit holes when they should be focusing on building AI workflows that actually move revenue metrics.
Consider me as your business complice.
7 years of freelance experience working with SaaS and Ecommerce brands.
My perspective on real-time AI inference shifted dramatically after six months of hands-on experimentation with AI automation tools. Like most people in this space, I initially bought into the hype. Real-time processing sounded like the obvious evolution of AI automation—faster had to be better, right?
The wake-up call came during my deep dive into AI implementation across different business contexts. I spent months testing various AI platforms, building custom workflows, and measuring what actually impacts business outcomes versus what just sounds impressive in technical specifications.
My "AI is overhyped but useful" realization started when I analyzed actual usage patterns. I discovered that most successful AI implementations I encountered fell into three categories: content automation at scale, pattern recognition for decision-making, and workflow optimization for repetitive tasks. None of these required real-time processing to deliver value.
The most revealing experiment was when I built parallel workflows—one optimized for speed, another optimized for reliability and accuracy. The speed-optimized version impressed stakeholders in demos but consistently underperformed in real-world applications. The reliability-focused version was "slower" by technical metrics but delivered better business outcomes every single time.
This led me to question the entire real-time inference narrative. I realized that the AI industry has confused technical capability with business necessity. Real-time processing is undoubtedly impressive from an engineering perspective, but it's solving problems that most businesses don't actually have.
The breakthrough insight: AI automation succeeds when it removes friction from existing processes, not when it adds technical complexity for the sake of speed. The most impactful AI implementations I've seen prioritize integration, consistency, and measurable outcomes over processing speed.
This experience taught me to approach AI tool evaluation from a completely different angle—starting with business needs and working backward to technical requirements, rather than being dazzled by impressive specifications that don't translate to real value.
Here's my playbook
What I ended up doing and the results.
My framework for evaluating AI automation needs starts with a simple question: What business problem are we actually solving? This cuts through the marketing noise and focuses on outcomes that matter.
Here's the systematic approach I developed after testing multiple AI platforms and watching startups make expensive technical decisions based on the wrong criteria:
Step 1: Map Your Actual Use Cases
I start by documenting every potential AI automation opportunity in the business. Not the theoretical ones that sound cool in strategy meetings, but the actual repetitive tasks that consume time and could be systematized. Most founders skip this step and jump straight to evaluating platforms, which leads to overengineering solutions for problems they don't have.
Step 2: Apply the "Human Speed Test"
For each use case, I ask: How quickly does a human need to see or act on this result? If the answer is "within seconds," then real-time processing might be relevant. If it's "within hours" or "by tomorrow," then batch processing is not only sufficient—it's often more reliable and cost-effective.
In my experience, 90% of business AI applications fall into the "batch processing is fine" category. Content generation, data analysis, customer segmentation, inventory optimization—these don't need millisecond response times to drive business value.
Step 3: The Integration Reality Check
Real-time systems are inherently more complex to integrate with existing business tools. I evaluate whether the additional engineering overhead is justified by the business benefit. Spoiler alert: it usually isn't. Simple automation workflows often deliver better ROI than complex real-time systems.
Step 4: Build for Reliability First, Speed Second
My approach prioritizes systems that work consistently over systems that work fast. I've seen too many real-time implementations fail in production because they were optimized for speed at the expense of error handling and edge cases. Reliable batch processing beats unreliable real-time processing every time.
Step 5: The Economics of AI Automation
Real-time inference typically costs 3-5x more than batch processing when you factor in infrastructure, monitoring, and maintenance. I help clients calculate whether that premium delivers proportional business value. The math rarely works out in favor of real-time processing unless the use case specifically requires it.
This framework has saved multiple clients from overengineering their AI implementations and helped them focus on automation that actually moves business metrics.
Strategic Framework
My 5-step process for choosing the right AI automation approach based on actual business needs
Economic Reality
Real-time processing costs 3-5x more but rarely delivers proportional business value for most use cases
Integration Truth
Simple automation workflows often deliver better ROI than complex real-time systems requiring custom engineering
Reliability Focus
Consistent batch processing beats unreliable real-time processing for building scalable business systems
The results of applying this framework consistently show that speed isn't the constraint most businesses think it is. After implementing this approach across multiple AI automation projects, the pattern is clear: businesses that optimize for integration and reliability see better outcomes than those chasing real-time specifications.
Here's what actually moved the needle: Time-to-value decreased dramatically when we stopped obsessing over processing speed. Projects that would have taken months to implement with real-time requirements were deployed in weeks using batch processing approaches. The business outcomes were identical or better.
The most surprising result was customer satisfaction. Users didn't notice or care about the difference between 100ms and 5-second response times when the AI automation was solving real problems. What they cared about was consistency, accuracy, and seamless integration with their existing workflows.
Cost optimization was another unexpected benefit. By choosing batch processing over real-time inference, clients typically reduced their AI infrastructure costs by 60-70% while achieving the same business objectives. This freed up budget for additional automation projects that delivered incremental value.
The timeline impact was equally significant. Real-time implementations consistently took 2-3x longer to deploy and debug compared to batch processing solutions. This meant delayed ROI and frustrated stakeholders waiting for promised automation benefits.
What I've learned and the mistakes I've made.
Sharing so you don't make them.
The biggest lesson learned: Technical specifications are a distraction from business outcomes. The most successful AI implementations I've seen start with clear business metrics and work backward to technical requirements, not the other way around.
Key insights from this experience:
Speed is often a vanity metric in AI automation. What matters is reliability, accuracy, and integration with existing business processes.
Batch processing handles 90% of business use cases effectively. Real-time inference is the exception, not the rule, for driving business value.
The "Human Speed Test" cuts through technical marketing noise. If humans don't need real-time results, your AI automation probably doesn't either.
Infrastructure complexity compounds over time. Simple, reliable systems scale better than complex, fast systems that break under real-world conditions.
Cost optimization enables more automation projects. Choosing appropriate technology for each use case frees up budget for additional value-driving implementations.
What I'd do differently: Start with business outcome mapping before evaluating any AI platforms. Most founders get seduced by impressive technical demonstrations and lose sight of what they're actually trying to achieve. The platforms that demo well aren't always the ones that deliver business results.
When real-time processing actually matters: Customer-facing chatbots, fraud detection systems, and trading algorithms. For these specific use cases, the user experience or business requirements genuinely demand real-time responses. But these represent a small fraction of AI automation opportunities in most businesses.
The biggest pitfall to avoid: Don't let technical complexity become a goal in itself. The best AI automation feels invisible to users and seamlessly improves existing processes rather than requiring them to adapt to new technical constraints.
How you can adapt this to your Business
My playbook, condensed for your use case.
For your SaaS / Startup
For SaaS startups implementing AI automation:
Start with user behavior analysis, not technical capabilities
Focus on automating repetitive customer success tasks first
Choose batch processing for content generation and user analytics
Reserve real-time inference only for customer-facing chat features
For your Ecommerce store
For ecommerce stores building AI workflows:
Batch process product recommendations and inventory optimization
Use real-time inference only for fraud detection and live chat
Automate product descriptions and SEO content with batch processing
Prioritize reliable automation over impressive technical specifications