From Data to Breakthroughs: How Our AI-Powered Analysis Platform Works
Turning complex data into insights in under 30 minutes.
From raw data to reproducible results in minutes — without coding or complex setup. Our platform turns everyday research questions into clear, structured analyses.
The Problem We Set Out to Solve
If you’ve ever run an RNA-seq project, you know the feeling: the data arrives fast — the analysis doesn’t.
You spend days aligning reads, normalizing counts, tweaking models, and still end up waiting for results to finish running.
We’ve been there, and that’s exactly the problem the aimed platform was built to fix — turning complex, repetitive bioinformatics into automated, transparent workflows that let teams focus on discovery, not debugging.
Why Traditional Analysis Falls Short
Before automation, most research teams hit the same three walls again and again:
Ask a Question. Get a Workflow.
At the core of the platform is a natural-language interface. Instead of scripting, users simply type scientific questions such as:
Which genes are expressed differently between my experimental conditions?
The platform automatically interprets the request, identifies the relevant analysis modules, and builds a reproducible workflow — including quality control, normalization, differential-expression analysis, and visualization.
Each step is documented and version-controlled, ensuring full transparency.
Example analysis modules selected automatically:
- Quality control: read-count distribution and sample clustering
- Normalization: variance-stabilizing transformation
- Model design: inclusion of biological covariates like batch or sex
- Statistical testing: DESeq2 differential expression
- Visualization: volcano plot, bar charts, and heatmap
Agentic AI and Automation in Practice
Unlike static pipeline builders, the aimed platform uses agentic AI — an adaptive system that reasons through every analysis step.
It selects optimal tools and parameters based on data structure and metadata, reducing manual setup and human error.
In an internal test run using bulk RNA-seq data from CD19⁺ thymic B cells (young vs. old), the platform:
- Automatically detected experimental groups and covariates.
- Generated a DESeq2 model design.
- Processed and normalized all samples.
- Produced interactive visualizations — all within 25 minutes.
Re-running the same workflow later yields identical results, eliminating one of the biggest pain points in bioinformatics.
From Data to Breakthroughs
After automating the workflows, something remarkable happens: the analysis finally keeps pace with the data.
What used to take days of manual setup, scripts, and back-and-forth now fits into a simple, repeatable process.
The impact is simple but profound: faster science, lower costs, and more reliable discoveries.
👉 See how it works: Try the platform.
It may sound ambitious, but it’s ready for you to explore.
Visual Outputs: Straight from the Platform
Within minutes, it produces structured, publication-ready outputs — interactive charts, data tables, and statistical summaries — all reproducible and transparent by design.
Differential expression analysis of RNA-seq data (DESeq2). Generated on the aimed platform.
Heatmap of top 50 differentially expressed genes (normalized log expression). Automatically clustered by the aimed platform.
Reproducible and Scalable by Design
Every analysis produced by the aimed platform is fully reproducible — parameters, versions, and dependencies are logged automatically.
Running the same workflow later yields identical results, removing a long-standing pain point in bioinformatics.
Because execution happens in the cloud, analyses scale from one dataset to hundreds without new infrastructure.
All data are encrypted during upload, processing, and storage, meeting standard biotech and pharma compliance frameworks.
What This Means for Research Teams
- For biotech startups, it reduces reliance on scarce bioinformatics capacity.
- For CROs and academic groups, it enables transparent collaboration and faster iteration.
- And for pharma R&D teams, it ensures that analysis keeps pace with growing data pipelines.
Resetting How Analysis Is Done
AI-driven automation is no longer a future concept — it’s already reshaping how biomedical data are analyzed.
With the aimed platform, complex omics analysis becomes fast, auditable, and accessible — freeing scientists to focus on biology, not pipelines.