Healthcare AI Development Pricing

How much does custom healthcare AI cost?

The short answer: development starts at $90/hour, and analytical, consulting and data work starts at $50/hour.

The longer answer is that asking for one universal project price is a little like asking a dentist:

“How much will it cost to fix all my teeth?”

Nobody serious knows until they look.

A simple automation may cost a few thousand dollars. A complicated platform with patient-facing AI, several integrations, local infrastructure, clinical logic, permissions, audit, validation and months of development can move into six figures.

There is a very large territory between those two.

That is why we do not start by trying to sell you a development package. We start by figuring out what you actually need.

First meeting is free

The first interview is free.

Usually that conversation is less about AI than people expect. I want to understand how the clinic works now: what people are doing manually, where patients are waiting, what information gets copied between systems, what annoys the doctor, what annoys reception, where leads disappear, which systems are involved, and what business result we are actually trying to get.

“Build an AI agent” is not a business result.

These are examples of the kind of business result we design around: reducing receptionist workload by a meaningful percentage, automatically answering most incoming WhatsApp requests, cutting physician chart-review time, or eliminating manual copying of lab information between systems.

Those are things we can design around.

I have personally launched more than 15 clinics, managed three and worked on development or implementation of healthcare IT for roughly 100 organizations. That background is useful here because I have been the clinic owner receiving the development proposal as well as the person building the system.

And I know how often the thing management thinks the receptionist does and the thing the receptionist actually does are two completely different processes.

Then comes analytics

After the initial interview, if the project needs proper discovery, we move into paid analytical work.

Consulting, workflow analytics and work with data: from $50/hour.

For a very simple project this stage may be short. For a complicated one it can involve several stakeholder interviews, reviewing existing software, examining API documentation, looking at sample data, mapping processes and determining what can realistically be automated.

This part is regularly underestimated because people assume the difficult part is writing the code.

Quite often it isn’t.

The difficult part is answering: what exactly should the code do?

If you come to a developer with “I need a chatbot that answers patients” as the entire work scope, you are preparing yourself for a bad result. Proper design requires understanding the workflow, available technology, integrations, risks, failure modes and what result the clinic expects.

I would rather spend ten hours figuring that out before development than spend 100 hours rebuilding the wrong thing afterward.

Then you get a proposal

After discovery, we prepare a practical proposal.

Not a 70-page digital-transformation strategy nobody will open again.

It normally defines what we are building, what we are not building, major integrations, infrastructure assumptions, expected stages, estimated development volume and the commercial model.

If there are meaningful uncertainties, we say so.

For example, “We can integrate with this EHR if the vendor gives us this API” is different from “We definitely can integrate with everything.”

I prefer the first sentence.

Development starts at $90/hour

Our software-development rate starts at $90/hour.

But we do not think of the project as renting you a developer for 73 hours. You are buying the result.

A normal project can involve architecture, backend, frontend, AI workflows, prompting, RAG, integrations, DevOps, testing, project management and deployment.

Healthcare software also has an annoying habit of needing to work after the demo.

Which means QA matters. Security matters. Logs matter. Error handling matters. The receptionist clicking something we never expected matters. The patient sending a photograph instead of an answer matters. The old EHR timing out matters.

Everything after the happy-path demo is where a large percentage of the real development lives.

We develop iteratively

This is the same practical model I have used building healthcare systems for years.

We do not disappear for six months and return holding “the finished system.” We build in iterations: get the first working flow, show it, try it on real examples, find out what we misunderstood, fix it, then add the next part.

When appropriate, we test in a sandbox or shadow mode before putting anything in front of patients.

This is especially important with AI because something can look brilliant on 20 prepared examples and behave completely differently once 2,000 real patients discover creative new ways to communicate with it.

The goal of iteration is not Agile theatre.

The goal is to discover mistakes while they are still cheap.

Why don’t we just quote a fixed $2,999 package?

Because custom healthcare software isn’t a logo design.

Two clinics can ask for “an AI receptionist” and mean completely different things.

Clinic A wants website chat → answer FAQ → send appointment request to email.

Clinic B wants WhatsApp + phone + website → patient identification → EHR integration → scheduling → insurance logic → multilingual conversations → payment → medical triage boundaries → escalation → call recording → analytics.

Same two words: “AI receptionist.”

Very different project.

We do have existing components, architectural patterns and experience with common workflows, so we are not rebuilding everything from zero. That can reduce implementation time significantly.

Custom does not have to mean “reinvent every wheel.”

One-time project or subscription?

Most custom projects are primarily one-time implementation projects.

That means we design it, build it, integrate it and launch it. Afterward there may be ongoing costs for support, hosting, model/API usage, telephony, infrastructure or continued development.

We can also structure a project as a recurring subscription if that makes more commercial sense or if you would rather spread the implementation cost.

I don’t believe every software project has to become SaaS simply because everybody learned the phrase “monthly recurring revenue.”

The commercial structure should fit the project.

What does ongoing support include?

It depends on how badly you need us at 3 a.m.

A small internal tool may be perfectly fine with ordinary ticket-based support. A system responsible for patient communications, laboratory workflows or other operationally critical processes may need a much tighter SLA.

Support can cover technical incidents, bug fixes, infrastructure problems, integration failures, staff questions, model or workflow issues, and help with planned changes. At the higher end, we can agree defined severity levels, response targets and 24/7 coverage.

One thing I would not recommend is putting an AI into the middle of your clinic operations and assuming nobody will ever have to maintain it.

Software has never respected that assumption.

AI certainly doesn’t.

What happens after implementation?

Go-live is not the end of the project. It is the moment when the software finally meets reality.

Once staff starts using the system every day, you discover things that no workshop or test environment can show properly. Patients phrase requests differently. Doctors want another field. Reception invents a workaround. Your EHR vendor changes an API. A model provider releases a better model. A workflow that looked perfectly logical on paper turns out to be annoying after the 300th repetition.

That is normal.

After implementation we can continue working with you on:

• bug fixes and operational support when something does not work as intended;
• workflow tuning after we see how the system behaves in production;
• prompt, RAG and AI-logic updates when the knowledge base, rules or expected behavior changes;
• model upgrades or replacements when a better model becomes available or a provider changes pricing, terms or capabilities;
• new integrations with EHRs, labs, telephony, payment systems, CRM tools, devices or other software;
• new functions and modules as the original solution grows;
• UI and usability changes based on staff feedback;
• automation expansion once the first workflow proves itself;
• security and infrastructure updates when your policies or technical environment change;
• new locations, departments or specialties if the system needs to be adapted for a broader rollout.

Some customers need very little after launch. The system does its job and we mostly leave it alone.

Others keep developing for years.

Both are normal.

AI systems need a little more attention than ordinary software

Traditional software can also age badly, but AI adds another layer.

Models change. Providers change APIs. Prices change. A model that was the obvious choice six months ago may no longer be the best one. Your own knowledge base changes. Clinical or administrative rules change. Staff finds new use cases. And sometimes an update from an external vendor changes behavior nobody on our side touched.

That does not mean you need to rebuild everything every six months.

It means we try to design the system so individual components can be changed without throwing away the whole product. Model, knowledge layer, integrations and business logic should not be one giant knot.

If OpenAI, Anthropic or another provider suddenly gives us something materially better, I would rather swap a component than tell you that you need “AI transformation v2.”

Further development is usually incremental

Once the first useful workflow is running, the next opportunities become much easier to see.

Maybe we started with an AI receptionist and then discover that the same infrastructure can automate post-visit follow-ups.

Maybe the original project summarized medical documents, and now we want it to prepare structured data for the EHR.

Maybe one clinic location works well and you want to roll the system out to five more.

This is usually how good healthcare software grows: not through one giant “digital transformation” project, but by adding useful pieces around workflows that already proved their value.

We can continue development under the same hourly model, agree a monthly development allocation, or scope larger additions as separate projects.

Customization does not stop at launch

Every clinic is different before implementation.

It remains different afterward.

Templates change. Staff roles change. Pricing changes. New doctors arrive. New services are launched. The clinic buys another system. Someone decides WhatsApp should now behave differently. An insurance flow gets added. A regulator changes something. A manager discovers a report they suddenly cannot live without.

We expect that.

The whole point of building a custom system rather than forcing you into an off-the-shelf product is that it can keep adapting when the business changes.

That does not mean every request requires development. Some things should be configurable by your own administrators. Some can be handled through settings or content changes. Some genuinely require code.

Part of good architecture is knowing the difference.

Who owns the IP?

This depends on the agreement.

Our normal model is that we keep ownership of reusable code, general components, libraries and technology that are not unique to your business, while you keep your data, methods, proprietary workflows and customer-specific know-how.

That is not the only model.

We also do full white-label development and IP assignment. For example, if you are a healthcare startup building your own product, you may need the project-specific intellectual property assigned to your company so investors can see a clean chain of title.

That is completely normal and we can structure the project that way.

IP is not a religious question for me. It is a commercial question.

What I do care about is agreeing it before development.

Discovering after 12 months that the customer thought they bought the source code and the developer thought they sold a license is a remarkably stupid way to spend money on lawyers.

What a normal project looks like

First conversation: free.

Then, when needed, paid discovery and analysis at $50/hour.

Then we prepare the architecture, scope and proposal.

Development starts from $90/hour.

We deliver iteratively, test on real workflows, pilot before serious rollout, and continue until the agreed scope works in production.

After launch, we support it at the level you actually need, fix what real-life use uncovers, and keep developing or customizing it when there is a business reason to do so.

Pretty boring.

Which is good.

Healthcare has enough exciting surprises without adding creative software contracts to the list.