Evidence becomes comparable
Normalize agent, strain, target, crop, formulation, conditions, study design, and outcome across published and internal work.
Agricultural biologicals R&D
Evidence to experiment.
Turn published and internal evidence into condition-specific efficacy comparisons, failure explanations, and review-ready R&D decisions.
The biologicals evidence gap
Biological performance is conditional. Your evidence system should be too.
From papers to the next defensible experiment
The evidence-to-experiment layer
Conditions stay attached
Keep dose, delivery, temperature, moisture, production setting, and measurement context connected to every result.
Failures become informative
Retain negative findings, expose metric mismatches, and distinguish real contradictions from study-design artifacts.
Decisions become reusable
Move reviewed evidence into experiment briefs, candidate stage gates, regulatory gap maps, and monitored project memory.
A biologicals-native evidence model
The context generic research tools leave behind.
Kwáu organizes evidence around the interaction your team must understand—not only the documents it came from.
01
Canonical biological identity
Preserve organism, strain, pest, pathogen, crop, and synonym relationships across sources.
02
Condition-response context
Connect formulation, dose, environment, delivery, and production setting to the measured outcome.
03
Comparable performance
Separate incidence, mortality, reduction, yield, and other metrics before drawing cross-study conclusions.
04
Negative and conflicting evidence
Search for failure deliberately and record how specialists resolve disagreements and uncertainty.
05
Source-linked decisions
Carry evidence locations, assumptions, review status, and residual gaps into the next R&D action.
Scientist-governed evidence
People decide. Kwáu prepares the evidence.
Specialized agents acquire, structure, compare, and challenge the evidence. Scientists inspect the source trail, resolve uncertainty, and approve what supports the decision.
Prove one evidence workflow before scaling it
The Kwáu validation pilot
You choose
One consequential interaction or decision
Begin with a completed or active biologicals question whose accepted outcome and evidence standard can be judged.
We lock
The evidence and review standard
Agree entities, conditions, inclusion rules, expected outputs, review points, and failure thresholds before the run.
We benchmark
Manual, existing AI, and Kwáu
Compare coverage, accuracy, traceability, expert time, rework, and whether the output improves the R&D decision.
You reuse
A reviewed workflow and evidence graph
Retain accepted evidence, expert corrections, and evaluation logic so the next project starts ahead.
One interaction reviewed. Evidence retained. The next decision starts ahead.
Trust is part of the evidence model
Your science stays yours—and stays inspectable.
Kwáu preserves the trail from source to extraction, interpretation, review, and decision while your team retains control of its confidential science.
Your data
Your documents, experimental records, and confidential project materials remain private to your organization.
Source-location provenance
Claims and extracted records retain a reviewable path to the supporting page, table, or source material.
Scientist approval
Specialists resolve uncertainty and approve what becomes reusable evidence or enters a decision.
Versioned conclusions
Files, corrections, evidence states, and workflow versions remain available when a project is updated or audited.
Designed to complement your R&D stack
An evidence layer—not another system to replace everything.
Kwáu works before and between experiments, connecting external evidence and reviewed decisions to the systems your team already uses.
ELN and LIMSMove reviewed evidence and decisions into the experimental system of record.
Literature and regulatory sourcesBring scholarly, patent, label, government, and internal material into one evidence workflow.
Structured exportsDeliver evidence records, matrices, reports, and handoff packages in usable formats.
Private integrationsConnect organization-specific repositories, schemas, review gates, and deployment requirements.
Start with evidence
Bring one biologicals decision that takes too long.
We will define the evidence standard, run Kwáu on a historical or active interaction, and benchmark the output against work your scientists can judge.