Data cannot reach AI safely
Papers, SOPs, notes, images, and analysis results are fragmented without retrieval and evidence design.
AI for Science implementation support
Bring usable generative AI and AI agents into research work.
We help life science, pharmaceutical, and medical research teams design generative AI, RAG, local LLMs, AI agents, and research data foundations from PoC to department operation.
The service is designed around human review, access control, explainability, and reproducibility.
Generative AI/RAGLocal LLMAI agentsResearch data foundationIAS integration
From research data to AI foundation, agents, and deliverables
Research data
RAG / AI foundation
AI agents
Human review
Deliverables
We validate search, summarization, analysis support, report drafts, and team knowledge workflows through PoC.
Why research AI implementations fail
Model selection alone is not enough. Research AI breaks down when data, permissions, evaluation, and operations are disconnected.
Unused AI
Evaluation is unclear
Teams lack criteria for correctness, reproducibility, review, and production readiness.
Security is added late
Confidential data, access control, audit logs, and external LLM boundaries are not defined early enough.
It does not connect to work
Chat remains isolated from analysis, reporting, approval, and education workflows.
LifeAnalytics approach
We design AI objectives, data foundations, agent workflows, PoC evaluation, production operations, and training with research teams.
Research data foundation/RAG
Design retrieval, citations, permissions, and update rules for papers, SOPs, notes, logs, and metadata.
AI agent design
Create human-reviewed workflows for search, analysis support, review requests, and report drafts.
Local LLM and secure AI
Compare external LLMs, private RAG, and local LLMs according to security and network constraints.
Operations and training
Prepare usage rules, review perspectives, prompt standards, evaluation logs, and department training.
Five-layer implementation architecture
We separate data, knowledge, models, agents, and department operations before scaling AI usage.
Layer 1
Research data
Papers, SOPs, notes, images, NGS, mass spectrometry, flow cytometry, and analysis results
Layer 2
Knowledge/RAG
Search, vector database, citations, permissions, update rules, and evaluation sets
Layer 3
Model/LLM
External LLMs, local LLMs, specialist models, prompts, and guardrails
Layer 4
AI agents
Research, summarization, analysis support, reports, review requests, and tool integration
Layer 5
Department operation
Human review, audit logs, training, usage rules, PoC evaluation, and improvement
Support menu
We support assessment, design, PoC, production, and training as separate stages.
MenuTargetOutputReview
AI implementation assessment
Research leaders and research DX teams
Use cases, data and permission inventory, PoC plan
Human decision on priority and feasibility
RAG/research data foundation PoC
Teams using papers, SOPs, and notes
Search evaluation, citation display, permission design, report
Evidence and answer review
AI agent PoC
Teams improving analysis, reports, and review
Task design, prototype, operation logs
Review approval before task execution
Local LLM/secure AI
Organizations with confidential or private environments
Environment proposal, model comparison, security plan, findings
Data boundary and responsibility review
Department training and operations
Organizations expanding to multiple teams
Usage rules, training materials, evaluation criteria, roadmap
Review culture that avoids overreliance
Research use cases
Paper, SOP, and internal document RAG
Support literature and protocol review with evidence-aware answers.
Analysis log and result explanation support
Assist review of imaging, NGS, mass-spec, and flow cytometry results with human oversight.
Report and meeting draft generation
Draft research meeting notes, PoC reviews, and department reports for human editing.
Research DX knowledge base
Organize analysis procedures, FAQs, and review criteria for education and improvement.
Implementation process
- 01
30-minute consultation
Review research themes, data, constraints, and field challenges.
- 02
Use case design
Define success criteria, evaluation data, review flow, and risks.
- 03
PoC build
Validate RAG, LLMs, agents, and access control in a small scope.
- 04
Evaluation and improvement
Review evidence, reproducibility, logs, and user feedback.
- 05
Production and training
Prepare rollout, training materials, rules, and continuous improvement.
Why LifeAnalytics
Close to research data
We build web systems for imaging, genomics, molecular, flow cytometry, and mass spectrometry data.
Designed for operations
We include permissions, review, training, and inquiry workflows beyond PoC.
Human-reviewed AI
We design for explainability and human review instead of claims of automated diagnosis or guaranteed outcomes.
IAS integration
AI implementation support is an independent service, not an IAS add-on. When useful, IAS can connect as a multimodal research data foundation across imaging, genomics, molecular, flow cytometry, and mass spectrometry data.
AI for ScienceIASMultimodal research data
FAQ
Is AI for Science support an IAS add-on?
No. It is an independent service for safely introducing AI into research teams. IAS can be integrated as a multimodal research data foundation when useful.
What do you validate in PoC?
We evaluate target data, retrieval quality, evidence, review flow, permissions, operation logs, and user feedback for production decisions.
Can you support local LLM or private environments?
We review external LLM, private RAG, and local LLM options according to confidentiality, network, and operation requirements.
Do you guarantee medical diagnosis or paper acceptance?
No. AI is positioned as support for research, organization, analysis assistance, and drafting, with final decisions handled by human reviewers.
Start with a small, reviewable AI PoC for your research team.
Even if the use case is not fully defined, we can help identify PoC candidates and a safer implementation path in a 30-minute consultation.