Overview
Services
Our Values
Clients

Healthcare AI Consulting Services

An AI roadmap that survives contact with your EHR, your compliance team and your budget committee.

Most health systems do not lack AI ideas. They lack a way to tell which ones are worth building, what they will cost to run inside a HIPAA boundary, and who signs off when a model is wrong. A pilot gets built on a laptop, a demo goes well, and then nothing reaches a real patient encounter because nobody scoped the integration, the governance or the failure mode before the vendor contract was signed.

Cabot provides healthcare AI consulting services that start with an honest audit of your data, your workflows and your existing systems, not a vendor's product catalog. We tell you which use cases justify a build, which are better served by a packaged tool, and which are not ready yet because the data behind them is not. Where we build, a named engineer answers for every model that touches your systems, and nothing trains on your patient data.

Most engagements start with an AI readiness audit scoped to two or three candidate use cases, so you see a realistic cost and timeline before committing further. Tell us what you are trying to solve and what your EHR and data environment look like today, and we will return a written scope and a roadmap you can take to your board.

See how we take AI from pilot to productionSee what stays human in every engagement
Scope your project

Tell us what you need and where it has to run, and we will come back with the right approach and the risks before the estimate.

No obligation. Your details stay private.

Thank you! Your submission has been received!
Oops! Something went wrong while submitting the form.

95%

of enterprise generative AI pilots fail to deliver measurable value, most often from misaligned tasks or poor integration rather than the technology itself.

75%

of US health systems now use at least one AI application, up from 59 percent a year earlier.

$18.8B

projected size of the global healthcare AI consulting services market by 2036, growing 12.3 percent a year.

What does a healthcare AI consulting engagement actually deliver?

Healthcare AI consulting is the work of deciding which AI use cases a health system should build, buy or leave alone, then governing and delivering the ones worth building. It is not a workshop that ends in a slide deck. A consulting engagement earns its cost only if it ends in a scoped, costed, compliant path to production, or an honest answer that a use case is not ready.

The work covers four things in sequence: auditing what your data and systems can actually support, mapping candidate use cases against clinical and financial value, evaluating whether to build custom or adopt a packaged product, and building the governance a health system needs before any model touches a patient record. Skipping straight to a vendor demo is how most AI budgets get spent on pilots that never scale.

Why health system AI initiatives stall before they reach a patient

These are the six failure points we see most often when a health system brings in AI without a consulting partner who has done this inside a compliance boundary before. None of them is fixed by buying a bigger platform.

  • The use case was picked because a vendor pitched it, not because the data supports it. Nobody checked whether the underlying records were structured enough for the model to work.
  • The pilot ran on a laptop with synthetic data and never touched the EHR. The integration work that decides whether it survives contact with a real encounter was never scoped.
  • No one owns the model once it is live. When it drifts or gets something wrong, there is no named person accountable for reviewing or retraining it.
  • Compliance was invited after the build started. A business associate agreement and an access log get bolted on at the end instead of designed in from the first sprint.
  • The build versus buy decision was never made explicitly. The team built something a packaged product already does well, or bought something too rigid for a workflow it needed to fit.
  • Cost was estimated as a subscription fee, not as the integration, review and governance work that actually consumes the budget once the pilot has to become production software.

The healthcare AI consulting services we provide

Three phases, built to take a use case from an idea a clinician raised to a system running inside your compliance boundary. Some engagements need all three. Most start with the audit, because that is where a build versus buy decision gets made honestly.

AI readiness audit and use case prioritization

We inventory your data sources, EHR interfaces and existing tools, then score candidate use cases against clinical value, data readiness and integration cost, so you know which ones are worth funding before any code is written.

Model evaluation, vendor selection and build versus buy

Where a packaged product fits your workflow, we say so and help you select and validate it. Where it does not, we scope a custom build against the same evaluation criteria, model accuracy, bias testing, and total cost of running it inside your environment.

Governed build, integration and scale up

Custom models and integrations are built in reviewable increments, tested against synthetic clinical data, and released with the access logging, audit trail and business associate agreement a health system needs before go live.

What drives the cost of a healthcare AI consulting engagement?

Three things move the number: how many data sources and EHR interfaces the use case touches, whether the path is a packaged product evaluation or a custom build, and how much governance the compliance team requires before go live. Integration and model validation are the costs health systems most often underprice.

Start with a rough range from the calculator, then walk us through the use case and your current systems and we will turn that range into a real estimate.

Estimate your AI consulting engagement costAsk an engineer about your use case

How much of this work AI actually does, and what it never does

Less than a vendor pitch suggests, and more than a skeptical CIO expects. The clearest win is in the audit itself: AI can summarize years of clinical documentation and workflow notes into the patterns that decide which use cases are worth funding, far faster than a team reading them manually. The second win is in the build, drafting integration code, test cases and model evaluation scripts against patterns already proven in a codebase.

What AI does not do in this work is decide. It does not decide which use case is clinically appropriate, what a model's error tolerance should be, or when a system is safe to put in front of a patient. A named engineer reviews every AI assisted change before it merges, and a clinician on your side signs off on every clinical judgment a model influences.

Three commitments are written into every engagement. We stay model neutral, recommending the model or platform that fits the use case rather than steering you toward one vendor. Every AI assisted change is reviewed by a named engineer before it merges, and that engineer answers for it exactly as for code they wrote by hand. And we never train models on your code or your patient data, in any engagement, under any arrangement.

The tooling a Cabot AI consulting engagement runs on

We evaluate and build around the models and platforms that fit your environment and your compliance posture, not around a fixed product list. Where your team has already standardized on a cloud or a model provider, we work in it. The table below shows what AI does at each stage of the engagement, what a person keeps, and which tools are typically involved.

What we work in:

Audit and evaluation

Claude
Azure OpenAI Service
MLflow
Weights and Biases
Python
Jupyter

Integration and data

HL7 v2
FHIR R4
SMART on FHIR
PostgreSQL
Redis
Apache Airflow

Platform, security and quality

AWS
Azure
Terraform
Playwright
SonarQube
OpenTelemetry

What AI does at each stage of a consulting engagement, and what it never does

Every stage produces something you can review, and every stage has a named person who signs it off before it moves forward.

Build stageWhat AI doesWhat stays humanTools used
Readiness auditSummarizes clinical documentation, workflow interviews and system inventories into the patterns that decide priority.Deciding which use case is worth funding and what “good enough” accuracy means for it.ClaudeJupyter
Model evaluationRuns candidate models against test datasets and drafts comparison scorecards.Choosing the evaluation criteria and accepting or rejecting a model for clinical use.MLflowWeights and Biases
Integration buildDrafts interface mappings and integration code following patterns already in your systems.The integration design and every decision about which data crosses which boundary.GitHub CopilotClaude Code
Governance and reviewFlags a first round of bias, drift and security issues in a model or integration.Approving go live. One named engineer and one named clinician sign off.SonarQubeMLflow
Scale and operateDrafts monitoring dashboards and clusters model drift alerts by likely cause.The decision to retrain, retire or expand a model's use.GrafanaOpenTelemetry

How Cabot's healthcare AI consulting differs from a generic AI consultancy

A general AI consultancy can evaluate a model. These four dimensions are where the difference shows when the model has to work inside a health system.

DimensionGeneric AI consultancyCabot
Healthcare domain depthApplies a general AI framework to your use case.Every recommendation is scoped against clinical workflow, HIPAA and EHR realities from the first conversation.
EHR and data integrationRecommends a model and hands off integration to your team or a separate vendor.Integration is scoped, built and tested as part of the same engagement, against what your EHR actually exposes.
Compliance and governanceGovernance is a deliverable added at the end of the engagement.A business associate agreement, access logging and a model review process are designed in from the audit stage.
Accountability after go liveThe consulting relationship ends at the recommendation.A named engineer remains answerable for every model or integration Cabot builds, through scale up.

The health systems we provide AI consulting for

Three kinds of organization bring us this work. For each, here is what we have learned about how their AI initiatives actually stall.

Multi hospital health systems

AI initiatives often start in one department and need to prove out before a system wide rollout. The hard part is not the model. It is agreeing one governance standard and one integration pattern across facilities that run different EHR configurations.

Specialty and ambulatory clinic networks

Use cases here tend to be narrower, such as documentation support or referral triage, but the clinics rarely have in house data science capacity, so the audit has to be thorough enough to catch a data readiness problem before a build starts.

Digital health and health tech companies

Product teams need a model evaluated and shipped fast, but a healthcare product carries compliance obligations a typical software consultancy underestimates. The work has to move at product speed without skipping a business associate agreement.

Industries we already understand

volunteer_activism

Healthcare

shopping_cart

Ecommerce

attach_money

Fintech

houseboat

Travel and Tourism

fingerprint

Security

directions_car

Automobile

bar_chart

Stocks and Insurance

flatware

Restaurant

How patient data stays protected across every AI engagement

We treat governance as a condition of shipping a model, not a review scheduled after it works. Every model evaluation runs against synthetic or de identified data until a business associate agreement is in place, access to production data is logged and role restricted, and credentials live in a managed vault rather than in configuration files.

Compliance obligations are scoped to how your organization operates rather than applied wholesale. For AI work, that means HIPAA safeguards, a documented model review process, and a trail linking each governance requirement to the tests that prove it, kept release by release so it is ready when an auditor or a board asks. Our compliance practice covers the wider program.

Role-based access control
SSO and MFA
Encryption in transit and at rest
Audit logging of PHI access
HIPAA safeguards
Business associate agreement

What happens, stage by stage, in a Cabot AI consulting engagement

The work runs in three stages, and each one closes with a decision that is yours to make. Many health systems commission the readiness audit on its own first.

Readiness audit and use case prioritization

Stage one turns a shortlist of AI ideas into a fundable roadmap, complete with a cost estimate for whichever use case you choose to pursue first. The decision at the end is simple: fund one, fund several, or pause and revisit later.

Evaluate, build or integrate in reviewable increments

Whether the path is vendor evaluation or a custom build, each increment is something your team can review, tested against synthetic data, with a named engineer approving every change before merge.

Govern, go live and scale

Rollout goes use case by use case with a review checkpoint at each one. After go live, the roadmap is driven by what the model actually does in production, not by the original pitch.

Who runs your AI consulting engagement, and what each person answers for

Every role has a clear owner from day one. The AI lead is responsible for use case evaluation, model selection and the recommendation you receive. An integration engineer owns the connections to your EHR and data systems. A compliance lead owns the governance trail and the business associate agreement. The delivery lead runs the schedule and keeps your stakeholders updated between reviews. For any decision, you know the person responsible and how to reach them.

Our strength is concentrated in four areas. The first is telling you honestly when a use case is not ready, because the data behind it is not. The second is the integration layer between a model and your EHR, where AI projects most often lose time. The third is building the governance evidence a HIPAA review and a hospital board both expect. The fourth is staying model neutral, so the recommendation fits the use case rather than a vendor relationship.

Your team and ours work inside an overlap window agreed in writing at the start. Design and evaluation decisions live in your repository as short written records, so a change of engineer does not cost you weeks. If the scope grows, you can extend the existing team with more engineers rather than standing up a second one.

Why health system leaders choose Cabot for vendor neutral AI consulting

An AI consulting engagement only pays for itself if the recommendation is honest and the build actually reaches production. These are the reasons health system leaders give for bringing this work to Cabot.

An honest build versus buy answer

We recommend a packaged product when one fits, and say so even when it costs us a build engagement.

Integration scoped from day one

What your EHR exposes and who approves access are established during the audit, not discovered halfway through a build. Where the integration runs deep, it is handed to our healthcare interoperability team.

Governance you can hand to an auditor

Access logging, a business associate agreement and a model review trail exist before a model touches patient data, backed by our wider compliance practice.

AI that reads and drafts, with a person who confirms

Every model output that reaches a clinical workflow is reviewed by a named person before it is used.

Model neutral by design

We are not paid by any AI vendor, so the recommendation is scoped to your use case, not a partnership.

Accountability that outlasts the recommendation

A named engineer remains answerable for what Cabot builds through scale up, not just through the pitch.

Where to go next, depending on your situation

Healthcare AI consulting covers the audit, the build versus buy decision and the governance. If one of these describes you better, start there instead.

You already know you need a voice AI agent

Start at voice AI agents for healthcare.

Your referrals are the specific workflow breaking down

Start at referral management software for clinics.

You need EHR or systems integration, not a new AI build

Start at healthcare interoperability.

Our Clients

Client Success Stories

A Digital Platform Connecting Lives and Transforming Care

A HIPAA-compliant patient engagement platform scoped from the client's vision through requirements, integration and rollout, lifting user engagement 40% and virtual consultations 15% within six months, the same requirements-to-governed-build path this page's consulting engagements follow.

Read the case study

AI-Accelerated Discovery to MVP for a HIPAA-Compliant Patient and Physician Marketplace

A discovery-to-build engagement scoping AI-accelerated delivery inside a HIPAA boundary, the same audit-first discipline this page argues for.

Read the case study

AI Insight Report Generator App

An AI reporting build showing the same evaluate-then-build discipline behind this page's model evaluation service.

Read the case study

AI DevOps Assistant with Locally Hosted LLM

A locally hosted AI tool built and governed inside the client's own environment, the same no training on your data commitment on this page.

Read the case study

See all Cabot case studies

Questions health system leaders ask before hiring a healthcare AI consulting partner

What does a healthcare AI consulting company actually do?

A healthcare AI consulting company audits your data and workflows, decides which AI use cases are worth building or buying, and governs and delivers the ones worth building. You leave with a written scope, a cost range and a governance plan your compliance team can sign off on, or a direct no when the data behind a use case is not there yet.

How much does healthcare AI consulting cost?

Cost tracks the scope of the engagement rather than a flat rate: a standalone readiness audit costs far less than a full custom build with EHR integration. Run the cost calculator for a starting range, then walk us through your current systems and we will turn it into a firm estimate.

How is Cabot different from a generic AI consultancy?

A generic AI consultancy evaluates a model in isolation. Cabot scopes healthcare domain fit, EHR integration, compliance and accountability into the same engagement, so the recommendation survives contact with your actual systems rather than stopping at a slide.

Do you recommend packaged AI products, or only custom builds?

Both, depending on what the audit finds. When an existing product covers your workflow, we help you run a real evaluation against your own data rather than take a vendor demo at face value. When nothing on the market fits, a custom build gets scoped against the same criteria, and we say so either way rather than defaulting to a sale.

How do you keep patient data safe during model evaluation?

PHI never reaches a model until a business associate agreement is signed; until then, evaluation runs on synthetic or de identified records only. After that, every production access is tied to a named person and logged, and the trail is available to your compliance or security team on request.

Who is accountable if an AI model gets something wrong?

A named engineer and a named clinician sign off before any model reaches a clinical workflow, and that engineer remains answerable for it through scale up. No model output reaches a patient record without a person confirming it first.

Can you work with the EHR and systems we already have?

Yes. Integration is scoped during the readiness audit against what your EHR actually exposes, using standard interfaces such as HL7 v2, FHIR R4 and SMART on FHIR, rather than asking your workflow to bend to a new platform.

How long does an AI readiness audit take?

Most audits run two to four weeks. The timeline scales with how many data sources, EHR interfaces and stakeholder groups need review, and it ends with a small set of scored use cases and a realistic budget range for whichever one you choose to pursue first.