Category: Machine learning

A while ago, a brochure website was a meaningful software project. Somebody needed to lay out the pages, create navigation, make a contact form work, and get it all deployed. Website builders did not make a good website automatic, but they made that particular layer of work cheap enough that it stopped being the main thing most companies paid for.

Something similar is happening to CRUD applications.

A competent engineer with current tools can get surprisingly far, surprisingly quickly: a schema, basic APIs, forms, table views, search, permissions, validation, an admin screen, and a handful of ordinary integrations. LLMs help produce that code faster and make the usual implementation details less expensive to iterate on.

That is real progress. It is also easy to draw the wrong conclusion from it.

The fact that it is getting easier to build a system of record does not mean the business problem is solved. It means the database-shaped part of the problem is less scarce.

The valuable question was rarely just “where do we put the records?” It was “what should we do next, who needs to do it, and how do we know it worked?”

From recording work to improving work

CRUD is still necessary. Organizations need a place to record customers, jobs, invoices, inventory, cases, and the rest of the nouns that make up their work. They need people to be able to find and correct those records.

But a record is not an outcome.

A useful distinction is between a system of record and a system of action. The first stores what happened. The second helps decide what deserves attention, coordinates action across people and systems, and learns from the result.

System of record	System of action
Stores customers, jobs, invoices, and cases	Prioritizes work and moves it forward
Lets people enter, search, and update data	Coordinates people, systems, and exceptions
Reports what happened	Forecasts, recommends, and optimizes what to do next
Uses broadly reusable patterns	Encodes domain-specific constraints and tradeoffs

The CRUD layer is often part of a system of action. It is just not usually the part that makes the system valuable.

The floor is moving to workflow

Take a customer-success tool. The commodity version has accounts, contacts, renewal dates, health-score fields, notes, and tasks. That is useful, and it is also a familiar application shape.

The harder version combines product usage, unresolved support issues, contract terms, champion turnover, and outcomes from similar accounts to answer a more useful question: which accounts need attention this week, and what action is most likely to change the outcome?

Then it needs to make that action practical. Perhaps a support issue needs escalation, a CSM needs a meeting, sales needs to be involved before a renewal date, and the team needs a shared view of what happened next. The value is not a nicer account page. It is reducing the chance that an important customer falls through the cracks.

This pattern appears everywhere. Field-service software can store work orders, technicians, addresses, and status updates. The differentiated work is scheduling and re-scheduling against technician skills, promised windows, parts availability, geography, overtime rules, uncertain job duration, and emergency calls. A credible schedule at 8:00 AM is not enough if the system cannot respond when a job takes twice as long as expected at 10:30.

That is workflow orchestration: the messy part involving handoffs, timing, exceptions, policy, and people. It is not glamorous, but it is where a lot of operational software earns its keep.

Not everything valuable is an LLM

LLMs are part of this shift, but “CRUD to AI” is too narrow a description.

LLMs are particularly useful when a workflow begins with unstructured information: an email, a document, a call transcript, an image, or a request written in normal language. They can help extract information, classify incoming work, summarize context, or give a person a natural-language interface to a system.

Other valuable systems may have no LLM in the critical path at all. They may use a rules engine, a forecast, a statistical process-control chart, a constraint solver, a simulation, or a carefully constructed report. Many systems will combine several of these approaches.

The common thread is not the model. The software does more than preserve a record of work. It helps make a better decision, execute it, and learn from the outcome.

Analytics turns data into a question worth answering

Consider revenue operations. A CRM stores leads, opportunities, stages, activity, and quotas. The useful analysis is often above that layer: pipeline coverage by segment, conversion rates between stages, typical cycle times, and the difference between a healthy-looking pipeline and one that is unlikely to close in time.

Those views support real decisions. Is a territory short on coverage? Is a segment converting differently? Does the organization need more sales capacity, a different territory design, or a different target? The implementation might be straightforward cohort analysis or a forecast based on historical data. It does not need to be generative AI to be valuable.

Product analytics has a similar trap. A dashboard can show that activation or retention moved. An experiment, with a clear metric and a credible comparison group, helps answer whether a product change caused the movement. That difference matters when deciding what to ship to everyone.

The system of record supplies the events. The analytical layer makes them useful for a decision.

Optimization makes tradeoffs explicit

Some of the highest-value software is not about generating text or predicting a label. It is about choosing among competing, constrained options.

A logistics application might store shipments, vehicles, drivers, stops, service windows, and delivery status. The difficult work is assigning loads and planning routes while respecting vehicle capacity, driver-hours rules, pickup timing, delivery promises, and cost. This is an optimization problem. There may be no chat interface and no LLM involved.

Inventory is another familiar example. A basic app can show that stock is low. A more useful system estimates demand and lead-time uncertainty, accounts for storage limits and the differing cost of stockouts, and recommends what to order, from whom, and when. It makes the tradeoff visible instead of leaving a person to infer it from a table of quantities.

Workforce scheduling has the same shape. Employee records, certifications, availability, and shifts are CRUD. Building a workable schedule means balancing coverage, labor rules, preferences, fairness, qualifications, and overtime. The value is a schedule that an operation can actually run.

Operations research, forecasting, and constraint solving have been doing this work for a long time. Cheaper application development does not replace them. It makes it more feasible to spend effort on the part that changes the outcome.

Reliable execution is part of the product

A recommendation that cannot be acted on is just another dashboard.

Useful systems need to connect to the places where work happens, create or route the next task, explain why an item was prioritized, and handle cases that do not fit the normal path. They need audit trails where the decision matters. They need safe fallbacks and a clear way for a person to take over.

This is especially important when an LLM is involved. A model can help read an invoice, summarize a case, or classify an incoming request. It should not turn uncertainty into an invisible decision. The system needs confidence thresholds, validation, exception queues, permissions, and a way to correct mistakes. Those are not incidental implementation details. They are what make automation usable in a real operation.

The same is true for non-AI logic. A routing optimizer needs to expose the constraints it used. A forecast needs to show when its assumptions no longer resemble reality. An approval workflow needs a path for the unusual case. Dependability is not separate from the product; it is part of the value proposition.

Start with the bottleneck, not the screen

For builders, the practical implication is simple: start with the recurring decision or bottleneck.

Ask what people are repeatedly deciding, what information they have to assemble to decide it, which constraints they are balancing, and what happens after they make the call. Then work backward to the data, integrations, analysis, and interface required.

That approach may still produce a CRUD application. Most useful systems need records. But the record pages become infrastructure for a more specific outcome: fewer missed renewals, better route utilization, more disciplined purchasing, faster resolution, or a decision that used to require several people and a spreadsheet.

LLMs have made the CRUD shell cheaper to produce. That should be good news. It lets teams spend more of their attention on the work that has always been difficult: understanding an operation well enough to remove delays, make tradeoffs explicit, and reliably move work forward.

AI code generation is one of the most useful things to happen to software development in a long time. We use it. It gets people from an idea to a working application much faster than they could have a few years ago.

That is a big deal. It is also not the same thing as getting from an idea to a well-operated application.

This week, one of our clients’ Snowflake cost alerts went off. A new application had spent more than $4,000 in a couple of days. The application had been built with Claude and it was doing what its owner intended it to do. The problem was how it was doing it: repeatedly polling Snowflake with a larger warehouse than the work required.

The code worked. The bill did too.

The alert was the important part

The client had anomaly alerts in place long before this application existed. Over years of normal use, those alerts had established a useful picture of what ordinary compute usage looked like. When the new application’s usage departed from that pattern, it stood out quickly.

That monitoring was not glamorous, and it was not new. It was operational knowledge turned into a guardrail. Without it, the polling loop could have continued until someone happened to notice an unusually large bill.

This is worth emphasizing because it is easy to see AI as the whole story. The model helped create the application. The monitoring, the historical baseline, and the people who responded to the alert are what limited the damage.

Working is not the same as economical

AI is good at getting to a plausible solution. It can write the query, connect the service, add a loop, and return the result. But it does not naturally care whether a process runs every minute instead of every hour, whether data can be cached, whether an existing system already solves part of the problem, or whether a warehouse is sized appropriately for the query.

Sometimes the generated solution will rebuild something that already exists. Sometimes it will choose a direct approach that is perfectly functional but wasteful at production scale. A polling loop is a simple example: it may make a feature feel responsive while quietly paying for repeated work that is unnecessary.

None of that makes the application useless, or AI a bad tool. A few years ago, a person without deep technical experience might not have been able to build and deploy this application at all. Now they can. That is real leverage.

But the leverage changes where the risk sits. Development time may go down while cloud spend, maintenance, security exposure, or reliability risk goes up. Those costs often arrive after the demo is working and the application is in use.

A short review can be a very good investment

The answer is not to ban AI-generated code or require every idea to go through a long development process. The answer is to put experienced eyes on the parts that determine how software behaves in the real world.

For a data-backed application, that review can be straightforward:

What runs on a schedule, and how often does it actually need to run?
Which queries execute, on what warehouse, and how much data do they scan?
Can the application cache results, react to an event, or reuse an existing data set instead of polling?
What is the expected cost at normal usage and at a failure mode?
Which alerts will tell us when the application behaves differently from expected?

A review like this does not need to take longer than the work it is reviewing. In this case, it could have prevented a four-figure surprise. More importantly, it creates a habit of treating an AI-generated application as software that will be operated, not just code that needs to run once.

Keep the human in the loop

There is a familiar parallel with outsourcing. Lower-cost implementation can be a good trade when the work is understood and the output is reviewed. It becomes expensive when the apparent savings mean nobody owns the architecture, the quality, or the ongoing consequences.

AI assistance is similar. It can make capable builders out of more people, and that is something to embrace. But it can also produce slop, inefficiencies, security risks, and bugs that are easy to miss because the first version looks complete.

The goal should not be to slow people down. It should be to pair the speed of AI with monitoring that catches surprises and with people who understand the systems, costs, and tradeoffs behind the code.

This is the first in a series of examples from the gap between shipping software quickly and operating it well. AI can help you build faster. Make sure someone is also asking what the resulting system will cost to run.

Claude, Codex, and the rest are useful debugging partners. They can suggest hypotheses quickly, explain unfamiliar systems, and keep an investigation moving when you are stuck.

They can also send you on a very convincing rabbit hole.

We ran into that with a client’s ASP.NET application. After it had been up for a while, the first request for a static JavaScript file could be very slow. Requests after that were fast. It was the kind of narrow, intermittent behavior that invites a long list of theories.

Claude’s initial diagnosis was that SSL certificate revocation checking was holding up the first request. That is a real thing worth knowing about, and it sounded plausible in the abstract. But the application was using a self-signed certificate. There was no certificate authority revocation check to perform. A small piece of domain knowledge ruled out a direction that otherwise could have consumed hours.

The problem was not that Claude mentioned certificate revocation. The problem would have been treating a confident, technically detailed answer as evidence.

Start with what the system is doing

Rather than follow the SSL theory, we tested the behavior we could observe. We read the assets directly from the filesystem and fetched the asset through the application. The pattern was consistent:

Fetch an asset and the first request is slow.
Fetch it again immediately and it is fast.
Edit the file, then fetch it again, and the next request is slow again.

That is a much more useful description of the issue than “static JavaScript is slow.” The expensive path was associated with first access to changed file content. It was not ordinary request handling, and it did not fit the TLS explanation.

The experiment strongly pointed to endpoint scanning. SentinelOne was running in that environment and was the most likely cause: changed content was likely being scanned on its first access, while the next read benefited from the result already being available. We did not treat that as a definitive vendor-level attribution, but it fit the observed behavior far better than revocation checking did.

Plausible is not proven

LLMs are especially good at producing plausible explanations. They have seen the vocabulary around a symptom, and they can connect it to a real mechanism. That is useful for generating a list of things to investigate.

But a diagnosis has to survive the details of the actual system:

Does the proposed mechanism exist in this deployment?
Does it explain the timing and repeatability of the symptom?
What inexpensive test could distinguish it from the other hypotheses?
What observation would prove it wrong?

A self-signed certificate was enough to make us stop and question the revocation theory. The cold-read, warm-read, and modified-file test gave us a better hypothesis to pursue. Neither step required an encyclopedic knowledge of every possible cause. They required knowing enough to check the assumptions and to design a small experiment.

Use the model as a partner, not an authority

Claude still helped with the investigation. The right use was not to ask it for the answer and implement the first response. It was to use it as a partner while we compared theories against the environment and the measurements.

A practical debugging loop looks like this:

State the observation precisely, including what changes between a slow request and a fast one.
Ask the model for competing hypotheses and a test that would separate each one.
Check its assumptions against the architecture, configuration, and operational environment.
Run the smallest useful experiment.
Feed the result back in and repeat.

This is also why domain expertise still matters when using LLMs. If you cannot tell whether an answer fits the system you are operating, confidence and detail are easy to mistake for correctness. Bring in someone who knows the domain, or slow down enough to validate the model’s premises before chasing its conclusion.

The model can make a good investigator faster. It cannot replace the judgment needed to decide whether a theory belongs in the investigation at all.

There has been a lot of discussion recently about companies using AI to build internal tools that replace SaaS licenses. That is interesting, but it misses a big category of software: the web apps you cannot replace.

Sometimes the constraint is technical. More often, it is not. An insurance company may require you to use its verification portal. A specialty vendor may only accept orders through a clunky ecommerce site. Or a marketplace may be where all of the demand for your product or service lives.

You can build a better internal tool, but you still have to use those sites.

The browser is the integration point

Chrome extensions have always been a way to change the experience of a site you do not control. An extension can read information from a page, add controls to it, and help guide a user through a workflow.

Historically, that was possible but often not practical. You needed to write and maintain custom code for every awkward workflow, and the payoff had to be large enough to justify it.

The latest AI models change that calculation. It is now much easier to build an extension that augments a legacy site, whether that means changing a workflow, extracting information from a page, or adding LLM capabilities directly where people are already working.

Instead of asking someone to copy information from one system into another, you can put the assistance in the browser tab where the work already happens.

A bike search on Facebook Marketplace

I recently had a good excuse to try this out. I was looking for a bike on Facebook Marketplace with a specific set of requirements. The hard part was not finding listings. It was reviewing the photos for each listing to determine whether a bike was actually a fit.

Doing that manually meant opening and reviewing dozens of listings every day. That is exactly the sort of repetitive visual task that an AI model can help with.

So I built a Chrome extension that uses OpenAI to review listing photos and flag the listings that match what I was looking for. Rather than replacing Facebook Marketplace, the extension improves the part of the Marketplace workflow that was taking the most time.

The result is not a fully autonomous bike buyer. It is a faster way to narrow down a large list of listings so I can spend my time looking at the promising ones.

Where this approach works

The Marketplace example is personal, but the pattern applies to business workflows too. Look for web-based processes where a person repeatedly has to review, classify, summarize, or move information before they can make a decision.

A Chrome extension can be a practical place to add help to:

an insurer’s required portal
a vendor ordering site
a marketplace your team depends on
an internal legacy application that is difficult to change

The goal is not necessarily to replace the site. It is to remove the tedious steps around it while keeping people in the workflow they already need to use.

See it in action

Check out the demo below to see the bike finder at work:

Watch the video on YouTube

Interested in building something similar for a workflow your team cannot avoid? Get in touch with Setfive.

I’ll preface this by saying that I know just enough about machine learning to be dangerous and get myself into trouble. That said, if anything is inaccurate or misleading let me know in the comments and I’ll update it. Last April Amazon announced Amazon Machine Learning, a new AWS service aimed at developers to help them build and deploy machine learning solutions. We’ve been excited to experiment with AWS ML since it launched but haven’t had a chance until just now.

A bit of background

So what is “machine learning”? Looking at Wikipedia’s definition machine learning is ‘is a subfield of computer science that evolved from the study of pattern recognition and computational learning theory in artificial intelligence. In 1959, Arthur Samuel defined machine learning as a “Field of study that gives computers the ability to learn without being explicitly programmed”.’ That definition in turn translates to using a computer to solve problems like regression or classification. Machine learning powers dozens of the products that internet users interact with everyday from spam filtering to product recommendations to Siri and Google Now.

Looking at the Wikipedia article, ML as a field has existed since the late 1980s so what’s been driving its recent growth in popularity? I’d argue key driving factors have been compute resources getting cheaper, especially storage, which has allowed companies to store orders of magnitude more data than they were 5 or 10 years ago. This data along with elastic public cloud resources and the increasing maturity of open source packages has made ML accessible and worthwhile for an increasingly large number of companies. Additionally, there’s been an explosion of venture capital funding into ML focussed startups which has certainly also helped boost its popularity.

Kicking the tires

The first thing we need to do before testing out Amazon ML was to pick a good machine learning problem to tackle. Unfortunately, we didn’t have any internal data to test with so I headed over to Kaggle to find a good problem to tackle. After some exploring I settled on Digit Recognizer since its a “known problem”, the Kaggle challenge had benchmark solutions, and no additional data transformations would be neccessary. The goal of the Digit Recognizer problem is to accept bitmap representations of handwritten numerals and then correctly output what number was written.

The dataset is a modified version of the Mixed National Institute of Standards and Technology which is a well known dataset often used for training image processing systems. Unlike the original MNIST images, the Kaggle dataset has already been converted to a grayscale bitmap array so individual pixels are represented by an integer from 0-255. In ML parlance, the “Digit Recognizer” challenge would fall under the umbrella of a classification problem since the goal would be to correctly “classify” unknown inputs with a label, in this case a 0-9 digit. Another interesting feature of the MNIST dataset is that the Wikipedia provides benchmark performance for a variety of approaches so we can have a sense of how AWS ML stacks up.

At a high level, the big steps we’re going to take are to train our model using “train.csv”, evaluate it against a subset of known data, and then predict labels for the rows in “test.csv”. Amazon ML makes this whole process pretty easy using the AWS Console UI so there’s not really any magic. One thing worth noting is that Amazon doesn’t let you select which algorithm will be used in the model you build, it selects it automatically based on the type of ML problem. After around 30 minutes your model should be built and you’ll be able to explore the model’s performance. This is actually a really interesting feature of Amazon ML since you wouldn’t get these insights with visualizations “out of the box” from most open source packages.

Performance

With the model built the last step is to use it to predict unknown values from the “test.csv” dataset. Similar to generating the model, running a “batch prediction” is pretty straightforward on the AWS ML UI. After the prediction finishes you’ll end up with a results file in your specified S3 bucket that looks similar to:

1,6,0,4,7,3,5,8,9,2
1.544274E-10,3.736493E-6,1.298402E-4,3.529298E-8,2.738585E-7,1.814797E-5,3.520103E-6,7.861468E-6,1.17829E-6,9.998354E-1
2.196675E-11,7.322348E-6,9.969799E-1,3.165914E-10,1.485307E-5,4.171782E-6,2.970602E-3,6.487699E-6,1.930486E-7,1.643579E-5
1.345541E-3,1.968907E-4,1.209908E-5,7.132479E-2,1.572009E-3,7.926991E-4,3.921966E-2,2.677833E-2,8.559376E-1,2.820424E-3
8.210548E-6,2.666948E-3,6.711699E-3,5.835555E-2,2.407767E-1,3.312858E-5,1.107812E-3,5.861267E-4,6.673768E-1,2.237697E-2
1.205649E-3,1.128586E-2,4.179959E-4,4.942253E-5,2.8488E-4,7.751592E-1,6.43877E-3,8.812359E-3,1.996288E-4,1.961462E-1

Because there are several possible classifications of a digit the ML model generates a probability per classification with the largest number being the most likely. Individual probabilities are great but what we really want is a single digit per input sample. Running the input through the following PHP will produce that along with a header for Kaggle:

<?php

$lines = explode("\n", file_get_contents("results_test.csv"));
$header = str_getcsv($lines[0]);

unset($lines[0]);

echo "ImageId,Label\n";
$imageId = 1;

foreach($lines as $ln){
    $ln = str_getcsv($ln);
    for($i = 0; $i < count($ln); $i++){
        $ln[$i] = (float) $ln[$i];        
    }
        
    $maxIndex = array_search(max($ln), $ln);
    
    echo $imageId . "," . $header[$maxIndex];
    
    if($imageId < count($lines)){
        echo "\n";
    }
    
    $imageId += 1;    
}

And finally the last step of the evaluation is uploading our results file to Kaggle to see how our model stacks up. Uploading my results produced a score of 0.91671 so right around 92% accuracy. Interestingly, looking at the Wikipedia entry for MNIST a 8% error rate is right around what was academically achieved using a linear classifier. So overall, not a bad showing!

Takeaways

Comparing the model’s performance to the Kaggle leaderboard and Wikipedia benchmarks, AWS ML performanced decently well especially considering we took the defaults and didn’t pre-process the data. One of the downside of AWS ML is the lack of visibility into what algorithms are being used and additionally not being able to select specific algorithms. In my experience, solutions that mask complexity like this work great for “typical” use cases but then quickly breakdown for more complicated tasks. Another downside of AWS ML is that it can currently only process text data that’s formatted into CSVs with one record per row. The result of this is that you’ll have to do any data transformations with your own code running on your own compute infrastructure or AWS EC2.

Anyway, all in all I think Amazon’s Machine Learning product is definitely an interesting addition to the AWS suite. At the very least, I can see it being a powerful tool to be able to quickly test out ML hypothesis which can then be implemented and refined using an open source package like skit-learn or Apache Mahout.

CRUD Is Getting Cheap. The Work Is Not.