Agriculture
Field trials, agronomy, seed and crop science.
Suite · the operating surface for research
The operating axis for research and analytics teams.
Design studies, validate data, run statistics, forecast outcomes, and publish the report — all on one operating surface that remembers every record, decision, and reason.
Suite · the dial
VII pillars · one surface
01 · the suite
Seven pillars, one workflow
The suite is opinionated about the order of operations — design before data, validation before analysis, attribution before forecast, structure before report — but each pillar can be entered directly when the project demands it.
I01
Experimental Design Assistant
Structure that holds up under peer review.
Plan treatments, factors, and replicates with a structure that makes downstream analysis defensible.
Open module
II02
Statistical Data Analysis
From dataset to defensible answer.
Ingest a dataset and get correct, citation-grade statistics — with plain-English interpretation alongside every number.
Open module
III03
Predictive Analytics
Forecasts you can show a board.
Train, validate, and explain models for yield, risk, demand, and outcome — without writing infrastructure code.
Open module
IV04
Research Report Generator
The write-up, drafted at the end of the analysis.
Turn the analysis you just ran into a clean, well-cited research artifact — methodology, findings, charts, and recommendations.
Open module
V05
Agriculture & Biotech Intelligence
Built for the trial, the field, the bench.
Sector-specific workflows for crop science, field trials, lab studies, and biotech research — with the vocabulary and metrics those teams actually use.
Open module
VI06
Research Dataset Workspace
Where the dataset becomes the analysis.
Upload a CSV or Excel file and walk through profiling, validation, chart previews, and AI-drafted insights — without leaving the page.
Open module
VII07
Data Quality & Validation
The check before the chart.
Catch missing values, outliers, inconsistencies, and unit errors before they survive into a published number.
Open module
02 · workflow
From question to published report
A research project moves through six stages on the suite. Every step writes back to the same event log — the next stage opens with the prior stage's outputs already loaded.
Step 01
Pose the question
Define the hypothesis, the population, and what you would accept as evidence. Aixys interviews you to fix gaps before the design is locked.
Step 02
Design the study
Treatments, replicates, randomisation, sample size. The protocol is exported the moment the design is approved.
Step 03
Validate the data
Profile the dataset, fix obvious issues, score readiness. Re-run automatically on every refresh.
Step 04
Run the analysis
Pick the test, run it, get assumption checks and post-hoc grouping by default. Save the run; everything is reproducible.
Step 05
Forecast the outcome
Where the analysis answers "what happened", a predictive model answers "what next" — with intervals, attribution, and stress tests.
Step 06
Publish the report
Generate the document at the end — methodology, results, recommendations — in your house style.
03 · sectors
Built for the disciplines that publish their work
The suite ships with templates and vocabulary tuned to research-heavy industries. The underlying engine is general — the templates accelerate the standard motions.
Biotech
Discovery screens, assay analytics, dose-response.
Life sciences
Comparative effectiveness, cohort studies, lab QC.
Healthcare research
Non-interventional studies, registries, outcomes analysis.
Consumer R&D
Sensory panels, formulation factorials, stability.
Operations research
Throughput, defect analysis, process improvement.
04 · why aixys
Why analyst-led teams pick this surface
The suite is built for the kind of work that lives or dies on whether someone can defend the number — months later, in a meeting, under questioning.
Reproducible by default
Every analysis carries the dataset version, the test chosen, the assumptions checked. Replay any result from any prior week, end-to-end, with the same inputs.
Defensible in review
The methodology section writes itself from the actual run metadata — no gap between the experiment that was done and the protocol that was published.
Built around the analyst
Tests, attributions, and post-hoc groupings come out of the workflow, not out of a black box. Every number can be traced back to the rows and the test that produced it.
One surface, one memory
The suite does not live on top of seven tools; it replaces them. The workspace, the design assistant, the report generator share one event log and one access model.
Continue · suite
See the suite against your team's data.
A 30-minute working session with the Aixys studio. Bring a real research question and a dataset; leave with an analysis, a draft report, and a plan.