AI Integration Services
Has your AI pilot worked in a demo and then stalled? Maybe the model cannot reach clean data, your ERP or CRM has no usable interface, or nobody owns it once it goes live. If that sounds familiar, you are in the right place. Cabot is the AI integration company that closes that gap. Need to connect a language model to your internal knowledge? Want AI inside the workflows your teams already use? We start with a readiness assessment, design the integration, connect the models to your applications and data, and stay accountable for governance, security and monitoring after launch. You get AI your teams can trust and your auditors can review, with no rebuild. Healthcare is where we see this most. Hospitals, health systems and digital health companies need AI to work with EHR, clinical and billing systems, and to do it under HIPAA. We build for that every day, and we bring the same discipline to enterprises in every other sector.
Readiness · Application and API integration · Data pipelines · LLM integration · Monitoring · Governance
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What Are AI Integration Services, and How Do They Differ From Building an AI Product?
AI integration services connect AI models to the applications, data and workflows a business already runs, so existing systems gain AI capability without being replaced.
The work spans readiness assessment, architecture design, API and application integration, data pipelines, model deployment and ongoing monitoring. It differs from building an AI product, which creates a new application around a model. Integration starts from the systems and data you already have, which is why it usually reaches production faster.
An AI integration company is a partner that owns this connection work end to end. It designs the integration layer, chooses the right models, applies governance and stays accountable for the system after launch. For leaders comparing AI integration consulting with a build partner, the test is simple: who is responsible when the model meets your ERP, your CRM and your data. These AI integration solutions also cover LLM integration services, where a language model is connected through a controlled gateway to your internal knowledge and workflows.
Why Do AI Pilots Stall at the System Boundary?
The pilot works in a sandbox and fails in production. The model cannot reach live data, so it never leaves the demo.
Data is siloed and inconsistent. Customer, product and transaction records disagree across systems, and no model can fix that on its own.
Legacy systems have no usable interface. Older platforms expose no clean API, so integration stalls before it starts.
Compliance and security block approval. Nobody can show where data goes, who can see it or how model output is reviewed.
Nobody owns the integration. Data, engineering and the business each assume another team is responsible for monitoring and updates.
There is no path to roll back. A model update changes behavior, and the team cannot tell what broke or how to return to the last good state.
Ready to see where your pilot is stuck?
Walk us through the pilot and the systems around it, and we will show where the integration breaks down.
Which AI Integration Services Can Cabot Deliver for Your Systems?
AI readiness assessment and architecture
We review your systems, data and infrastructure, then design the integration architecture: data flow, model endpoints, security and the order of work.
Application and API integration
We connect AI to your ERP, CRM, service and custom applications through APIs, events and an integration layer, including EHR platforms through FHIR and HL7, and older systems with no clean interface.
Data pipelines, governance and lineage
We build the pipelines that feed models and the controls that keep them trustworthy: ownership, access rules, lineage and quality checks.
Generative AI and LLM integration
We connect language and multimodal models to your internal knowledge and workflows through controlled gateways, with prompts, access and cost managed in one place.
Model deployment, runtime and monitoring
We deploy models into production, then track accuracy, latency, cost and drift, with versioning and a tested rollback for every release.
Custom AI, agents and workflow automation
Where an off-the-shelf model does not fit, we fine-tune or build one, and wire agents and automations into the workflows they serve.
What Does AI Integration Cost, and What Moves the Price?
Five things move the price: how many systems you connect, the condition of your data, the models you choose, your governance requirements and the depth of testing. A focused first integration costs far less than a platform rebuild. Share your scope and we will give you a range you can take to your finance team.
How Does Cabot Wire AI Into Your Stack, and What Stays Human?
Three rules govern how we work. We build on the model you choose, or a custom LLM, and recommend one only when you have no preference. A qualified person reviews AI output before it triggers a consequential action. And we do not train any model on your data.
Which Models, Platforms and Systems Does Cabot Connect AI To?
We connect to the platforms you already run. In healthcare, that means EHR platforms such as Epic, Oracle Health and MEDITECH through FHIR and HL7, which we cover in our healthcare integration services. Across other sectors, it means platforms such as Salesforce, ServiceNow, SAP, Oracle, Snowflake, Databricks, MuleSoft and Boomi, and to custom applications through APIs. The groups below show what we build with. The table after them shows where AI sits at each stage of an integration. For Salesforce programs, see our Salesforce integration services.
Models
Data and orchestration
Cloud and monitoring
Where AI Sits at Each Stage of an Integration, and What Stays Human
Every stage produces something you can inspect and has a person who signs it off.
In-House Build, Generic AI Vendor or an AI Integration Company: Which Fits Your Organization?
Comparing partners for an AI integration?
Share your shortlist and your constraints, and we will give you a plain view of where each option fits.
How Does Cabot Secure and Govern AI Integrations?
Security comes first. An AI integration touches live business data, so we apply encryption in transit and at rest, role-based access, secrets management, audit logging and data lineage from the first design session. Models are versioned, monitored for drift and released through a controlled process with a tested rollback.
Standards follow. We align controls to SOC 2, ISO/IEC 27001 and GDPR, and to AI-specific guidance such as the NIST AI Risk Management Framework, ISO/IEC 23894 and the EU AI Act where they apply. For healthcare clients, we add HIPAA safeguards, minimum-necessary access to protected health information and full audit trails on every AI interaction with patient data. For other regulated markets, we add the controls your sector requires. Which standards apply depends on your sector and regions, and we confirm the list with your compliance team before build. Explainability notes and model cards are part of what we hand over. See also our data governance and AI practice.
How Does Cabot Take an AI Integration From Pilot to Production?
Cabot builds in six steps, each ending in a decision you make. You can stop after any of them, and the first is worth having even if nothing follows it.
Not sure where to begin? Start with step 01 and settle the scope before you commit to a build.
1. AI readiness assessment
We review systems, data and infrastructure against the AI capability you want. You decide which use case goes first.
2. Architecture and roadmap
We design the integration layer and sequence the work. You decide scope and the first milestone.
3. Build and connect
We build the pipelines, connectors and model integration. You decide which systems go into the first release.
4. Validate and govern
We test accuracy, security and review steps against your standards. You decide whether controls are strong enough to go live.
5. Deploy with monitoring
We release in stages with monitoring and rollback in place. You decide how fast to widen access.
6. Train teams and hand over
We train your teams, document the integration and hand over runbooks. You decide what Cabot continues to support.
Ready to move your pilot into production?
Start with a readiness assessment and leave with a prioritized plan, whether or not you build with us.
Who Stands Behind the Engineers Integrating AI Into Your Systems?
Every engagement has named owners. A solution architect owns the integration design. Data engineers own the pipelines and their quality. Machine learning engineers own model behavior and monitoring. A quality lead owns the tests that prove the integration behaves as specified, and a delivery lead owns the cadence and keeps your leaders informed. Cabot's depth is greatest in four areas: integrating AI with enterprise and legacy systems, LLM gateways and retrieval, healthcare and regulated-data integration through FHIR, HL7 and HIPAA controls, and production monitoring. We work in your tools, hold regular reviews with your leads and document everything, so the knowledge stays with your organization. Teams also learn the new workflows with us, through training built into the final step.
Why Technology Leaders Choose Cabot as Their AI Integration Company
Integration first
We start from your systems and data, so AI reaches production instead of staying a demo. See our AI development services for the wider build.
Your choice of model
We build on the model you request, or develop a custom LLM when your use case needs one. Without a preference, we recommend the best fit for your risk and cost profile.
Security and governance
Access control, lineage and review steps are part of the first design. See our data governance and AI work.
Fast first integration
A focused first integration reaches production in weeks, so you see value before committing further. See MVP development services.
Legacy ready
We add an integration layer around older systems, including clinical and billing platforms, instead of forcing a replacement.
In-house delivery
Architects, data engineers, ML engineers and QA work together from kickoff to launch. See our QA and testing practice.
Client Success Stories
AI-Accelerated Discovery to MVP for a HIPAA-Compliant Patient and Physician Marketplace
See how Cabot took a HIPAA-compliant, four-portal telehealth marketplace from discovery to a working MVP with licensure-aware booking, video, and payments.
Read the case study
Text2SQL with Streamlit
Learn how Cabot used Python and Azure OpenAI to build a Streamlit app that turns plain-English questions into SQL and delivers real-time answers for faster analysis.
Read the case study
FHIR Server & FHIR Auth Configuration for Secure API Access
See how Cabot built a FHIR-compliant server with Firely Auth (OAuth2/OpenID Connect) and MSSQL for secure, standards-based access to healthcare data.
Read the case study
Automated Patient Summary Generation and Eligibility Assessment
Cabot built an AI system to generate patient summaries, run eligibility checks, prioritize referrals, and deliver dashboards, cutting intake time and errors.
Read the case studyWhere Should You Start If AI Integration Is Only Part of the Problem?
You want AI that acts on its own
Autonomous workflows sit with AI agent development services.
Your CRM is the system to connect
See Salesforce integration services for the connection layer.
Your legacy system must move first
Staged moves with both systems live are covered by software migration services.
Your system is aging but still works
Upgrades and re-architecture sit with application modernization services.
Your core systems are EHR or clinical platforms
See our healthcare integration services for the connection layer.
You need a budget range before a conversation
Try the Cost Calculator before you talk to us.
Our Clients
AI integration services connect AI models, such as large language models and machine learning systems, to the applications, data and workflows a business already runs. The work covers assessing readiness, designing the architecture, building the connections, governing the models and monitoring them in production. It differs from building an AI product because the goal is to make your existing systems smarter, not to replace them.
Look for five things: proof that integrations reached production, fluency in your existing systems, a clear approach to data governance and security, freedom to use the model you prefer, and a delivery plan with decision points. Ask who owns monitoring after launch. An AI integration company that cannot answer that question has not run integrations at scale.
An AI product is a new application with AI at its core. AI integration adds AI capability to systems you already use, such as your ERP, CRM, data platform or support tools. Integration is usually faster and cheaper to start, because your data and users are already in place.
Cost follows the number of systems to connect, the condition of your data, the models you choose, your governance requirements and the level of testing you need. A focused first integration costs far less than a platform rebuild. For a starting range, try our Cost Calculator.
A focused first integration, such as an LLM connected to one workflow and one data source, can reach production in weeks. Larger programs that span several systems run in stages, so the first stage delivers value while later stages are built. We agree the sequence and the first milestone during the readiness assessment.
We monitor accuracy, latency and cost, review drift and handle controlled updates. We also train your teams and document the integration, so your organization owns the knowledge. Many clients then add the next integration using the same foundation.
ERP, CRM, EHR and clinical systems, billing platforms, data warehouses, ticketing and service platforms, document stores, internal APIs and custom applications. We confirm the exact connectors for your vendors during the assessment, since each platform exposes data differently.
We apply encryption, role-based access, audit logging and data lineage, and we keep a human review step before AI output triggers a business action. Models are monitored for drift, and every change is versioned so it can be rolled back. We align controls to the standards your compliance team requires, including HIPAA for protected health information.
Yes. We work with the model you choose, including GPT, Claude, Gemini and Llama, hosted or self-managed. Where you have no preference, we recommend the model that fits your data sensitivity, latency, cost and risk profile. If your use case calls for it, we fine-tune or build a custom LLM.Explore our LLM development services
Yes. We connect through APIs, event streams and data pipelines, and add an integration layer where a legacy system has no usable interface. If the system itself needs restructuring, we scope that separately through our application modernization services.