Category: Machine learning

A big thing happened the other week: Anthropic helped launch a $1.5 billion AI services company. OpenAI is also deploying enterprise agents with its own team and selected systems integrators.

$1.5 billion is not chump change. These companies are thinking about where the market is going, and they have realized that a capable model is not the only thing that matters. The implementation around the model is equally important.

Ode with Anthropic was announced in July as a standalone AI services firm. A week later, OpenAI introduced Presence, an enterprise deployment model that starts with a specific workflow and includes system access, policies, approvals, testing, and ongoing support.

While massive companies may be able to work directly with OpenAI or Anthropic, or at least afford to, most of us do not have that luxury. What is clear is that getting an impressive demo or proof of concept is not the hard part. The hard part is taking it to the next step, where the complex edge cases, data, users, and exceptions show up.

Small choices can turn into bigger problems

We were brought into an AI project where a fairly simple decision became a bigger problem later: the application was not locked to a specific model and release version.

At first, the AI application looked fine. It produced structured output and the rest of the automation could use it. As the provider changed the model’s behavior over time, the format changed and it began classifying some inputs differently than it had when the application was originally built. The output still looked reasonable to a person, but the rest of the automation relied on those expected formats and classifications, so it became unreliable.

This is easy to miss because the AI still appears to work. The problem only shows up when another system expects the output to be consistent, and there is no test in place to catch the change before it becomes an issue.

A model should be treated like any other production dependency. Pin the model and version where possible, keep a set of real test cases, test changes before they go live, and have a plan for upgrades. It is not exciting work, but it keeps a useful application from becoming unreliable over time.

A demo is not a workflow

We have seen the same thing with agents. An agent can look great in a demo because it can answer questions, summarize documents, or draft an action. Once it is used in a real business process, though, it needs to know where the right data lives, what to do when data is missing, when it should ask a person for help, who can approve an action, and what needs to be recorded later.

If that work is not planned ahead of time, the agent usually does one of two things. It takes actions it should not take, or it hands so much back to people that it does not save much time. In both cases, the model may be fine. The larger workflow was not thought through.

Building an AI agent is not the same as building a working process. The agent needs clear boundaries, the right access, a way to handle exceptions, and someone responsible for it when the process changes.

What good implementation looks like

A good implementation partner does more than connect a model to an API and call it innovation. They should help a company decide which workflow is worth changing, where the right data is, what the system is allowed to do, when people need to step in, and how to tell if the work is actually improving the process.

They should also be asking what happens when the model changes, the company changes a policy, or the data and workflow change. Those are normal parts of operating a business, and the AI application needs to keep up with them.

Sometimes an LLM is the right tool. Other times, ordinary software, better data integration, or a rules-based process makes more sense. Someone who has worked on these projects should know the difference.

AI makes it much faster to go from an idea to something that works, and that is valuable. But the problems with a weak workflow, bad permissions, unreliable output, or missing monitoring often do not show up until after the demo works. That is why implementation experience matters.

If an AI initiative is stuck between an impressive demo and something useful in production, do not assume it needs a bigger model or another tool. It may need a clearer workflow and people who know where these systems tend to fail.

HIPAA-compliant LLM access really is not that hard.

I came to this conclusion after going fairly far down the wrong path.

It started with Kimi K3 and GLM-5.2. These open-weight models are getting close enough to proprietary frontier models that they need to be taken seriously.

My first thought was that healthcare companies could run models like these themselves and keep patient data inside their own environment.

That is an appealing idea. No protected health information sent to a model API. No dependence on a third-party inference service. A company controls the hardware, the model weights, the network, and the logs.

Then I looked at the hardware.

These are not small models. Kimi K3 has 2.8 trillion parameters, and the vLLM project describes an eight-NVIDIA-B300 deployment as its easiest way to run the model. GLM-5.2 has 753 billion parameters; NVIDIA’s current quantized release targets Blackwell hardware and supports runtimes such as vLLM and SGLang.

The exact footprint depends on the precision, context length, concurrency, and serving stack. That is the point. An organization considering this path needs to budget for more than a GPU server: high-speed GPU interconnects, storage for weights and logs, redundant infrastructure, monitoring, patching, model serving, and someone who can operate all of it. For serious production traffic, that can become a large infrastructure project quickly.

That does not make local inference impossible. For the right workload, volume, and organization, it may be a reasonable choice.

But it makes “we should run our own frontier model because HIPAA” a much more expensive sentence.

The local-model path has more than one vendor

The next option is a GPU cloud. Some providers will sign a business associate agreement (BAA), which is a necessary part of handling protected health information with a service provider.

But the details matter.

A BAA may cover specific compute, storage, and logging services rather than everything the vendor offers. The organization still needs to understand where prompts, responses, embeddings, backups, traces, and support data go. “The GPU is covered” is not the same as “the whole system is covered.”

That is real diligence, but it is also not unique to open models.

Then there is the more obvious question: what about the clouds most healthcare companies already use?

AWS lists Amazon Bedrock as HIPAA eligible. Microsoft offers a BAA for in-scope Azure services, and its Azure AI Foundry documentation describes its HIPAA compliance offering. Google supports HIPAA workloads through Vertex AI.

So a healthcare company can already get managed access to capable models from AWS, Microsoft, or Google without buying a GPU rack or operating an open-weight model.

The model is available. The work is deciding what to do with it.

HIPAA is a system property

A BAA does not make an application safe by itself. Neither does a local GPU server.

The company still needs appropriate access controls, encryption, audit logging, retention rules, and an architecture that keeps PHI inside covered services. It needs to know which users can ask which questions, which records the system can retrieve for them, and whether sensitive data is leaking into an observability tool or a debugging log.

Those are not optional details around the LLM. They are the system.

A useful healthcare assistant also needs to be more than a chat box pointed at a model. An end user should see an authenticated application that retrieves only the records they are allowed to access, shows where an answer came from, and makes it easy to correct or escalate an uncertain result.

Take prior authorization. An LLM could help assemble information from a patient record into a draft packet. That may save a person time. But somebody still needs to decide what information is relevant, validate that the draft is correct, submit it through the appropriate channel, and handle the exception when the case does not fit the usual pattern.

The same is true for chart summarization, intake, coding support, patient-message triage, and internal policy search. The question is not just whether a model can produce a plausible answer. It is whether the workflow makes a person faster without making a mistake harder to catch.

Start with the workflow

I suspect plenty of healthcare companies are treating HIPAA as the blocker when the larger issue is that they have not identified a narrow enough problem worth solving.

“Give our staff an LLM” is not a workflow.

“Help the prior-authorization team find the relevant clinical history, draft a packet, and flag missing information for review” is much closer. It gives the team something to evaluate: time saved, completeness, error rate, review burden, and the cases where the system should stop and ask for help.

Once that workflow is clear, the infrastructure decision becomes more practical.

A managed model service may be the right answer if it fits the company’s covered environment and the team wants to focus on the application. A locally hosted open model may be the right answer when the organization has unusually strict control requirements, enough sustained volume to justify the infrastructure, or a reason to operate the model as a core capability.

Neither option removes the need for careful design. Both can be part of a HIPAA-compliant system. Both can also be used carelessly.

The LLM is not really the hard part.

The hard part is picking a useful workflow, connecting the right data, evaluating the output, and deciding where a human needs to remain involved.

What healthcare workflow would you be comfortable giving an LLM access to today?

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:

1. Fetch an asset and the first request is slow.
2. Fetch it again immediately and it is fast.
3. 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:

1. State the observation precisely, including what changes between a slow request and a fast one.
2. Ask the model for competing hypotheses and a test that would separate each one.
3. Check its assumptions against the architecture, configuration, and operational environment.
4. Run the smallest useful experiment.
5. 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.