AI and ML Development Services
Cabot builds minimum viable products that test your idea with real usage instead of guesswork. You get a lean release, the data to guide your next decision, and an architecture that holds up when you grow.
TensorFlow · PyTorch · Azure OpenAI · AWS SageMaker · HL7 · FHIR | Built for HIPAA, HL7 & FHIR
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What are AI and ML development services?
AI and ML development services turn a business problem into a model that runs in production: scoping the use case, engineering access to the data, building and validating the model, fitting it to the workflow it has to live in, deploying it, and keeping it accurate afterward. In healthcare the work carries requirements general-purpose AI does not, including PHI handling, audit trails, standards-based integration, and explainability a clinician can actually check.
The two labels get used interchangeably though they describe different scopes. A machine learning engagement usually centers on data pipelines, training, evaluation, and the operations that keep a model performing. An AI engagement tends to include that plus what sits around it: retrieval, agents, orchestration across applications, and governance. Healthcare AI and ML development services add a further layer, because a recommendation engine that misfires loses a sale while a risk model that misfires in a clinical setting is a different category of problem entirely.
Why AI projects stall after the pilot
Ask ten AI vendors whether they handle integration and ten will say yes. The word covers a lot of ground: it can mean a team has read the FHIR specification, or that somebody exported a CSV out of a reporting database once, or that they have kept a live interface into a production EHR running through a version upgrade. The distance between those is where budgets disappear, and it is rarely the modeling that runs over.
Underneath that sits a second problem, and it decides more projects than the first. The data a model needs was never gathered in one place, because until now nobody had a reason to gather it. Pulling it together is not a footnote you clear before the real work starts. It is most of the work. Cabot came at this from the other direction: we built the HL7 and FHIR layer and the integration practice first, then put the AI on top of it. That order is the whole argument for using us.
Can our models reach live data without a manual extract?
Will clinicians act on an output they cannot trace?
Who owns the model once it is running in production?
Our AI and ML development services
Custom AI and ML model development
Models built for your problem and trained on your data, rather than a general-purpose tool bent to fit. We start by asking whether ML is even the right instrument. Sometimes it is not.
Machine learning and predictive modeling
Risk stratification, readmission likelihood, no-show prediction, demand forecasting. Scores an operator can act on, with the reasoning attached so the number can be defended in a review.
Generative AI and LLM solutions
Retrieval-grounded systems for documentation, summarization, and intake. Built with guardrails, because a model that invents a medication is a liability rather than a feature.
Natural language processing
Most of what a health system knows sits in unstructured text. NLP pipelines pull structure out of notes, discharge summaries, and reports so the rest of your stack can use it.
Computer vision and image analysis
Detection and classification models for imaging workflows, built to assist a reader rather than replace one. Confidence surfaced alongside every output.
MLOps and model lifecycle management
Models drift. Data shifts underneath them. We monitor performance after launch, retrain when accuracy slips, and keep the audit trail intact.
Have a use case and no clear path to the data?
That is the conversation we are best at.
The systems our models already reach
Epic
Unified Epic data across departments, built to HL7 and FHIR standards.
Cerner
Clinical data exchange across Cerner environments for model training and write-back.
Meditech
Interfaces into Meditech so operational and clinical records reach downstream tools.
PointClickCare
Long-term and post-acute records shared accurately across coordinated care.
Health Gorilla QHIN
Nationwide exchange through a Qualified Health Information Network.
Corti LLM
Clinical language model integration into live care workflows.
Built for regulated environments
Explainability gets discussed as an ethics question. In practice it is a commercial one. A physician who cannot see why a model produced a score will not act on it, and a model nobody acts on returns nothing on what it cost to build. We design for the reviewer, not for a checklist.
Compliance is an architecture decision here, not a review you pass at the end. PHI handling, access scope, and audit logging get settled before the first pipeline is written, because retrofitting them is how timelines double.
How we build and ship AI and ML solutions
A structured process built for clinical environments, where model error, data integrity, and compliance carry real consequences. Every phase has clear deliverables.
1. AI readiness and use case scoping
We look at what you are trying to change and whether AI is the right instrument. Some of these end with a recommendation not to build, which is a cheaper answer than finding out in month seven.
2. Data access and pipeline engineering
Where the data lives, what shape it is in, what it takes to reach it, and what has to be normalized before a model can learn anything useful from it.
3. Model development and validation
Architecture selection, training, and testing against held-out data. We check performance across patient subgroups, because an accuracy number averaged over a whole population can hide a model that fails the people it matters most for.
4. Clinical and operational workflow fit
The output has to land inside how people already work. No second login. No duplicate entry. If it adds a step, it will not get used.
5. Integration and production deployment
Into the EHR, the analytics stack, or wherever the output has to go. This is the step that stalls most projects, and it is the one we have done most.
6. MLOps, monitoring, and retraining
Accuracy decays. We watch for it, retrain when it happens, and keep a record of what changed and when.
An AI and ML development company healthcare leaders trust
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Our Clients
An engagement usually runs from scoping through production and well past it: an honest assessment of whether the use case suits machine learning at all, the data engineering that makes training possible, model development and validation, integration into whatever system consumes the output, and monitoring once it is live. In healthcare, PHI handling and audit trails are built in rather than reviewed at the end.
Machine learning development is usually the narrower scope: data pipelines, model training, evaluation, and the operations that keep a model accurate. AI development covers that plus the surrounding system, which can include retrieval, agents, orchestration across applications, and governance. Most production systems are a mix of both. The distinction matters mainly because it changes what you are buying.
It depends on data readiness more than model complexity, which is why we do not quote a number before looking. A narrow use case running against clean, accessible data moves quickly. The same model against data spread across three systems with no interface between them is a different project. We scope data access first, then give you a timeline that reflects what we found rather than what we hoped.
Cost tracks the same variable as timeline: how hard your data is to reach, and how much work it needs before a model can use it. We price after a scoping engagement rather than before, because a number quoted without that work is guesswork with a decimal point in it. What we can tell you upfront is where the budget usually goes, and it is rarely the model. Data engineering and integration routinely take the larger share, and the projects that overrun are almost always the ones that assumed otherwise.
Yes, and it is the part of the work we are built around. We have delivered integrations with Epic, Cerner, Meditech, Athenahealth, Allscripts, eClinicalWorks, MatrixCare, and PointClickCare, using HL7 interfaces and FHIR APIs, alongside network-level exchange through Health Gorilla as a Qualified Health Information Network. If the output of a model has to land back in your EHR, that path already exists rather than being something we would build for the first time on your project.
Against whatever you agreed the model was supposed to change, defined before development starts. That might be time per encounter, denial rate, or how many at-risk patients get reached before an event. Setting the measure at scoping means success is not argued about after the fact.
Models degrade as the data around them shifts. We monitor performance against the metrics set at the start, flag when accuracy moves outside an agreed range, retrain on current data, and log what changed. In a regulated environment that log matters nearly as much as the model.
Often it is not capability, it is sequence. An in-house team can usually build the model. What slows them down is everything around it: reaching the data, standing up an environment that survives a compliance review, and integrating into systems whose documentation is a decade old. That is the part we have done repeatedly. In practice most teams bring us in for the integration and the lifecycle work, then keep us on the modeling once they see how much of the timeline actually sits in the plumbing.