Artificial Intelligence (AI) is the simulation of human intelligence processes by machines, especially computer systems. These processes include learning (the acquisition of information and rules for using the information), reasoning (using rules to reach approximate or definite conclusions) and self-correction.
Hi,
We're working on new capabilities to help you build Agents and AI applications faster with InterSystems IRIS. In order to better understand which entry points and development methodologies would help you most, we've created this brief survey: Building AI solutions with InterSystems IRIS.
Filling it in should not take much more than 5 minutes, and your feedback on this exciting topic will help us fine tune our designs and prioritize the right features.
Thanks in advance!
benjamin
Hey Community!
We're happy to share the next video in the "Code to Care" series on our InterSystems Developers YouTube:
In this article I will be discussing the use of an alternative LLM for generative IA. OpenIA is commonly used, in this article I will show you how to use it and the advantages of using Ollama
In the generative AI usage model that we are used to, we have the following flow:
Hey Developers,
Enjoy the new video on InterSystems Developers YouTube
⏯ Integrating AI Agents into InterSystems IRIS - Patterns and Techniques @ READY 2025
Hey Developers,
Enjoy the new video on InterSystems Developers YouTube
Hey Community,
Enjoy the new video on InterSystems Developers YouTube:
⏯ Analytics and AI with InterSystems IRIS - From Zero to Hero @ Ready 2025
Yes, yes! Welcome! You haven't made a mistake, you are in your beloved InterSystems Developer Community in Spanish.
You may be wondering what the title of this article is about, well it's very simple, today we are gathered here to honor the Inquisitor and praise the great work he performed.
Perfect, now that I have your attention, it's time to explain what the Inquisitor is. The Inquisitor is a solution developed with InterSystems technology to subject public contracts published daily on the platform https://contrataciondelestado.es/ to scrutiny.
Hey Community,
Enjoy the new video on InterSystems Developers YouTube:
This anthropic article made me think of several InterSystems presentations and articles on the topic of data quality for AI applications. InterSystems is right that data quality is crucial for AI, but I imagined there would be room for small errors, but this study suggests otherwise. That small errors can lead to big hallucinations. What do you think of this? And how can InterSystems technology help?
In my previous article, I introduced the FHIR Data Explorer, a proof-of-concept application that connects InterSystems IRIS, Python, and Ollama to enable semantic search and visualization over healthcare data in FHIR format, a project currently participating in the InterSystems External Language Contest.
In this follow-up, we’ll see how I integrated Ollama for generating patient history summaries directly from structured FHIR data stored in IRIS, using lightweight local language models (LLMs) such as Llama 3.2:1B or Gemma 2:2B.
The goal was to build a completely local AI pipeline that can extract, format, and narrate patient histories while keeping data private and under full control.
All patient data used in this demo comes from FHIR bundles, which were parsed and loaded into IRIS via the IRIStool module. This approach makes it straightforward to query, transform, and vectorize healthcare data using familiar pandas operations in Python. If you’re curious about how I built this integration, check out my previous article Building a FHIR Vector Repository with InterSystems IRIS and Python through the IRIStool module.
Both IRIStool and FHIR Data Explorer are available on the InterSystems Open Exchange — and part of my contest submissions. If you find them useful, please consider voting for them!
With the rapid adoption of telemedicine, remote consultations, and digital dictation, healthcare professionals are communicating more through voice than ever before. Patients engaging in virtual conversations generate vast amounts of unstructured audio data, so how can clinicians or administrators search and extract information from hours of voice recordings?
Enter IRIS Audio Query - a full-stack application that transforms audio into a searchable knowledge base.
Hey Community,
The InterSystems team put on our monthly Developer Meetup with a triumphant return to CIC's Venture Café, the crowd including both new and familiar faces. Despite the shakeup in both location and topic, we had a full house of folks ready to listen, learn, and have discussions about health tech innovation!
IRIS Audio Query is a full-stack application that transforms audio into a searchable knowledge base.
community/ ├── app/ # FastAPI backend application ├── baml_client/ # Generated BAML client code ├── baml_src/ # BAML configuration files ├── interop/ # IRIS interoperability components ├── iris/ # IRIS class definitions ├── models/ # Data models and schemas ├── twelvelabs_client/ # TwelveLabs API client ├── ui/ # React frontend application ├── main.py # FastAPI application entry point └── settings.py # IRIS interoperability entry point
Hey Community,
The InterSystems team recently held another monthly Developer Meetup in the AWS Boston office location in the Seaport, breaking our all-time attendance record with over 80 attendees! This meetup was our second time being hosted by our friends at AWS, and the venue was packed with folks excited to learn from our awesome speakers.
The topic of the August meetup was Agentic Orchestration & Multi-LLM Systems, and our speakers brought some amazing demos: First, @Nicholai.
Hi Community,
We're excited to share the new video in the "Rarified Air" series on our InterSystems Developers YouTube:
⏯ Leading with Empathy: The Human Side of Customer Centricity
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Along this OMOP Journey, from the OHDSI book to Achilles, you can begin to understand the power of the OMOP Common Data Model when you see the mix of well written R and SQL deriving results for large scale analytics that are shareable across organizations. I however do not have a third normal form brain and about a month ago on the Journey to generate sql for us utilizing InterSystems OMOP and Python interoperability.
Hey Community,
Enjoy the new video on InterSystems Developers YouTube:
Hey Community,
Enjoy the new video on InterSystems Developers YouTube:
Hey Community,
We're excited to invite you to the next InterSystems UKI Tech Talk webinar:
👉AI Vector Search Technology in InterSystems IRIS
⏱ Date & Time: Thursday, September 25, 2025 10:30-11:30 UK
Speakers:
👨🏫 @Saurav Gupta, Data Platform Team Leader, InterSystems
👨🏫 @Ruby Howard, Sales Engineer, InterSystems
Hey Community!
We're happy to share the next video in the "Code to Care" series on our InterSystems Developers YouTube:
In the previous article, we saw how to build a customer service AI agent with smolagents and InterSystems IRIS, combining SQL, RAG with vector search, and interoperability.
In that case, we used cloud models (OpenAI) for the LLM and embeddings.
This time, we’ll take it one step further: running the same agent, but with local models thanks to Ollama.
Article to announce pre-built pattern expressions are available from demo application.
AI deducing patterns require ten and more sample values to get warmed up.
The entry of a single value for a pattern has therefore been repurposed for retrieving pre-built patterns.
Paste an sample value for example an email address in description and press "Pattern from Description".
The sample is tested against available built-in patterns and any matching patterns and descriptions are displayed.
Patterns can also be retrieved by Keyword.
#InterSystems Demo Games entry
A text-to-sql demo on mqtt data analytics with RAG.
🗣Presenter: @Jeff Liu, Sales Engineer, InterSystems
#InterSystems Demo Games entry
This demo introduces an AI-powered clinical decision support tool built on InterSystems IRIS. The use case addresses clinician burnout by unlocking the wisdom trapped in 30,000 unstructured clinical notes. We showcase IRIS's powerful vector search to perform hybrid (semantic + keyword) queries for complex diagnostic challenges. The highlight is our multi-modal Retrieval-Augmented Generation (RAG) assistant, which analyzes clinical video content in real-time to find similar past cases and synthesize evidence-based recommendations, transforming how clinicians access and utilize institutional knowledge.
🗣 Presenter: @Vishal Pallerla, Sales Architect, InterSystems
Hey Community!
We're happy to share the next video in the "Code to Care" series on our InterSystems Developers YouTube:
#InterSystems Demo Games entry
The Co-Pilot enables you to leverage InterSystems BI without deep knowledge in InterSystems BI. You can create new cube, modify existing cubes or leverage existing cubes to plot charts and pivots just by speaking to the copilot.
Presenters:
🗣 @Michael Braam, Sales Engineer Manager, InterSystems
🗣 @Andreas Schuetz, Sales Engineer, InterSystems
🗣 @Shubham Sumalya, Sales Engineer, InterSystems
#InterSystems Demo Games entry
Shows how IRIS for Health can supercharge AI development with a Smart Data Fabric to train and feed their AI Models.
Presenters:
🗣 @Kevin Kindschuh, Senior Sales Engineer, InterSystems
🗣 @Jeff Semmens, Sales Engineer, InterSystems
#InterSystems Demo Games entry
Our Autonomous Business Intelligent Clerk, or ABiC for short, is a prototype revolutionizing how companies process data and make decisions. Normally, to get insights from data, you’d need IT knowledge or expertise in statistics. But with ABiC, that’s no longer necessary. All you have to do is ask your question in plain language. ABiC understands your interests and intentions, then shows a clear dashboard to guide your decisions. With ABiC, complex data is autonomously analyzed and turned into answers that support users, helping to accelerate business processes. This demo sends the metadata of InterSystems BI cubes to LLM. How does it work? Check out the video for more details!
Presenters:
🗣 @Tomo Okuyama, Sales Engineer, InterSystems
🗣 @Nobuyuki Hata, Sales Engineer, InterSystems
🗣 @Tomoko Furuzono, Sales Engineer, InterSystems
🗣 @Mihoko Iijima, Training Sales Engineer, InterSystems
#InterSystems Demo Games entry
For venture capitalists (VCs), evaluating research can be challenging. While researchers typically possess years of training and deep expertise in their field, the VCs tasked with assessing their work often lack domain-specific knowledge. This can lead to incomplete understanding of scientific data and an inability to direct organizational initiatives. To solve this problem, we have designed a solution that empowers VCs with AI-driven due diligence: ResearchExplorer. ResearchExplorer is powered by InterSystems IRIS and GPT-4o to help analyze private biomedical research alongside public sources like PubMed using Retrieval-Augmented Generation (RAG). Users submit natural language queries, and the system returns structured insights, head-to-head research comparisons, and AI-generated summaries. This allows users to bridge expertise gaps while securely protecting proprietary data.
Presenters:
🗣 @Jesse Reffsin, Senior Sales Engineer, InterSystems
🗣 @Lynn Wu, Sales Engineer, InterSystems
