One connected research workflow
Connect PMS, ELN, approvals, and ERP so AI understands the full research context.
AI-IRIS / PRIVATE R&D OPERATING SYSTEM
Connect research workflows and AI inside your company.From PMS, ELN, and ERP to task-specific model training.
Connect PMS, ELN, and ERP in one workflow. AI-IRIS, your on-premises research operating system.
- PMS + ELN + ERP
- AMBIENT AI
- ON-PREMISE + HARDWARE
- DATA FLYWHEEL
PRIVATE R&D ENVIRONMENTLOCAL
PROJECT DATA
LAB NOTE
ERP DATA
AI-IRISResearch operations and ambient intelligence
MODEL ROUTER
SPECIALIZED SLM
WORKFLOW ASSIST
As teams use the system, specialized models accumulate and make the same infrastructure more efficient.
WHY AI-IRIS
The difference between an AI PoC and AI used in daily work
AI-IRIS does not add another tool. It connects AI to your existing research workflow and runs inside your company.
Work the way you already do
AI works naturally within existing research tasks, without requiring teams to learn a new way to use it.
Your data stays in your company
Review an on-premises structure for operating critical research data within the enterprise. External connections and data processing terms are checked during adoption.
PRODUCT IN OPERATION
One research workflow, from projects to search
PMS, Ambient AI, Stage-Gate, and Agentic Search work together in one operating environment.
Integrated research project management
Connect schedules, progress, lab notes, issues, and core ERP data for each project in one operating view.
PRIVATE AI FLYWHEEL
AI that adapts to your company with use
Select and refine work data, then train and optimize task-specific models within customer infrastructure. Training by an external AI provider must be checked separately in the connected service's contract and settings.
PRIVATE MODEL TRAINING / CUSTOMER INFRASTRUCTURE
Observe
Analyze work patterns and context
Curate
Refine data into validated training assets
Specialize
Optimize models for specific tasks
Route
Select the best model for each task
Serve
Operate securely inside your company
Run task-specific models on shared GPU infrastructure and review model performance alongside resource usage.
ROADMAP / IN DEVELOPMENT
Private MLOps that extends to Physical AI
Building on our experience operating research AI, we are extending private MLOps to run foundation models and Physical AI inside the enterprise.
CAPABILITY EXPLORER
One structure from research operations to AI infrastructure
Start with the capabilities you need and expand in stages to fit your organization.
Connect research operations in one data structure
Manage projects, records, approvals, and resources as connected operating data rather than isolated documents.
- Research project, schedule, and issue management
- Electronic lab notebooks and history
- Internal ERP data integration
- Stage-Gate reviews and approvals
PMSELNERP IntegrationStage-Gate
DEPLOYMENT PATH
Validate small, then scale with actual usage.
Start with a focused research-team PoC, observe workloads and model usage, and use that evidence to determine the next infrastructure stage.
STEP 01
8 USERSResearch-team PoC
RTX PRO 6000 × 3 class
Validate core workflows and ambient intelligence in the field.
STEP 02
10–25 USERSLaboratory operations
B200 / B300 class
Operationalize PMS, ELN, ERP, and the data flywheel across the laboratory.
STEP 03
100+ USERSEnterprise expansion
Usage-based DGX-class cluster
Design enterprise infrastructure from proven workload and model usage.
The user counts and GPU configurations above illustrate deployment scales. Actual configurations are sized after reviewing models, concurrent usage, and security requirements.
ADOPTION / FAQ
AI-IRIS adoption FAQ
- What is it?
- AI-IRIS is an on-premises research operating system that connects PMS, ELN, and ERP to support research project management, writing, search, and approvals within enterprise infrastructure.
- Who is it for?
- It is for enterprise laboratories and research operations teams that want to connect project records, electronic lab notebooks, and ERP data, and bring AI into existing research workflows.
- What does it address?
- It addresses scattered schedules, records, approvals, and resource data that make research context hard to find and reports hard to prepare. Connected materials and workflows support writing, search, and review.
- How do you start?
- Review the current PMS, ELN, and ERP, team size, and security conditions first. A small research-team PoC checks core workflows and actual usage before integration scope and infrastructure are defined.
Core capabilities
- Integrated research project management
- Research writing assistance
- Stage-Gate reviews and approvals
- Relevant document and evidence search
Where is AI-IRIS installed?
Adoption is reviewed around on-premises deployment using enterprise servers. Existing servers, GPU resources, models, concurrency, network conditions, and access permissions inform the configuration. The 8-user PoC and GPU configurations on this page illustrate deployment scales; they are not universal minimum specifications.
Is research data sent outside the company?
The product description covers operating research data and AI within enterprise infrastructure. An adoption review must identify any external model APIs or support-service connections and examine the data transmitted, processing locations, and retention terms. An on-premises installation alone does not define every connected service's data policy.
How does external provider training differ from internal model training?
Whether an external AI provider uses customer data for model training must be checked in that service's contract and settings. AI-IRIS's Private AI Flywheel describes selecting and refining work data, then training and optimizing task-specific models within customer infrastructure. Data scope, permissions, and approval procedures for internal training are defined during the adoption review.
Can it connect to existing PMS, ELN, and ERP systems?
The product connects PMS, ELN, and ERP data. The actual integration scope depends on the current systems' interfaces, data formats, and access permissions. Share your systems and the projects, records, approvals, and resource data you want to connect.
How is a PoC reviewed?
Define the research workflow and data to evaluate, then review field suitability, usage, and model workloads with a small research team. Duration, cost, success criteria, and production scope are items for prior agreement. This page does not specify a universal duration or success rate.
START WITH A TECHNICAL REVIEW
Design how the laboratory will operate, not simply where to attach AI.
With your PMS, ELN, ERP, security requirements, and team size, we can define the most practical PoC scope.
Include your research-team size, field, current systems, and on-premises constraints when contacting us.
What's Next
HED-G Quant
From research labs to every department: AI transformation that simulates business decisions
Connect AI-IRIS research data and ERP through an ontology. Simulate interactions across purchasing, inventory, and financing to analyze enterprise cost structures and compare decision scenarios.
- An ontology connecting research, procurement, and finance
- Multi-economic entity simulation
- Historical backtests and forecasts