PARTNER BRIEF · 8 visual chapters · 30 seconds each
How Avery wins the next launch.
Thermo captures and orchestrates the science.
SCIKIQ operationalises enterprise intelligence on top of it.
11wlost per launch · today
−40%cycle target
$3.2MR&D cost out / programme
$42Msecured · HELIX wedge
14 daysto live integration
01
THE QUESTION
The eleven weeks
…is what Avery loses on every new-product launch — and the largest fixable lever in R&D.
BENEFIT
$3.2M per programme · $19M / year
Hybrid RAG · BM25 + Azure OpenAI text-embedding-3-largeBayesian active learning · BoTorch-styleGaussian Process surrogate model
Read chapter 1 →
02
WHAT'S ALREADY WORKING
You already built the lab
Thermo Fisher Platform for Science captures the science. The investment is sunk · the workflow is regulated · we don't touch it.
BENEFIT
Keep the $14M investment · add the intelligence layer for $0 CapEx
No AI in this layer — pure capture + workflowSCIKIQ AI sits ABOVE this (Ch 4)
Read chapter 2 →
03
THE GAP
Twelve systems away from a decision
The science is captured. The enterprise is not. Every scientist pays the tab-switching tax.
BENEFIT
12 silos → 1 governed graph · $4.3M back
Active metadata ingest · change-data-captureML-based entity resolution (sentence-transformer + rules)Property graph (Neo4j-class)
Read chapter 3 →
04
THE THESIS
Thermo captures. SCIKIQ operationalises.
Two systems · one sentence · four layers · a category nobody else owns.
BENEFIT
AI-Native Scientific Intelligence Platform
Azure OpenAI gpt-4.1 + text-embedding-3-largeBoTorch · Bayesian optimisationKnowledge graph + RDF semantic layer
Read chapter 4 →
05
THE WORKED EXAMPLE
HELIX, in eight beats
Brussels publishes a PFAS restriction Tuesday 09:00. The old way: a quarter. The new way: 7 weeks to A-sample.
BENEFIT
14 weeks → 7 weeks · $42M secured · 0.78 first-try probability
Hybrid RAG (BM25 + Azure OpenAI text-embedding-3-large)BoTorch Bayesian optimisationGaussian Process surrogate · qNEHVI acquisition
Read chapter 5 →
06
WHERE THE MONEY IS
Three pitches. Three audiences. Now.
Each is board-ready · funded this quarter · 90-day proof point.
BENEFIT
3 pitches · 3 owners · 90-day proof each · $12–18M Y1
See per-pitch AI/ML/LLM stacks in the cards below
Read chapter 6 →
07
WHAT IT'S ALREADY WORTH
Six wins from one dataset
SCIKIQ's first pass over the synthetic PfS tenant — six CEO-grade moments most teams would have missed.
BENEFIT
Six CEO moments from one dataset · ~$4–6M / quarter at scale
Sentence-transformer composition embeddingsDBSCAN clustering · similarity ≥0.91Bayesian opt + Gaussian Process surrogate
Read chapter 7 →
08
THE ASK
What to do Monday
Three things · in order · 14-day kickoff · 7-week proof · Q1 2027 ship.
BENEFIT
$1.2M ask · $12–18M Y1 return · $42M HELIX secured · payback <8 months
Azure OpenAI gpt-4.1 · prompt cachingBoTorch Bayesian optimisationHybrid RAG (BM25 + embeddings)
Read chapter 8 →
SOURCE
Pravin Jaiswal · COO, TechnePlus · for the Office of the CEO, Avery Dennison · 22 May 2026