← back to rankingRVTY · Revvity, Inc.
Medical - Diagnostics & Research · mkt cap $11.2B · calls: Q1 FY2026 vs Q4 FY2025
54.0 conviction · conf-adj 53
conf 5/10 Opus+GPT ✓ agree
enthusiasm:27.0 · trend:8 · quantifies:5 · impact:0 · under_radar:5 · credibility:5 · business_impact:4 · disruption:0 · commitment:0 · confirmation:0
Enthusiasm latest 9 / prev 7 (rising)
Revvity's AI story intensified from Q4 to Q1: external thesis is "AI creates more hypotheses → more wet-lab validation and Signals workflow pull" (Xynthetica/BioDesign/LabGistics, Lilly TuneLab), plus instruments used for AI training data; internal thesis is enterprise LLM rollout with Gartner validation and future cost-out, not near-term margin lift. Management is highly enthusiastic but does not quantify AI-specific revenue, bookings, or savings—2026 guidance explicitly excludes material benefit from AI software launches while citing broader Signals/SaaS growth metrics.
GROUNDED NEXT-FY IMPACT vs CONSENSUS
Grounded on actual base — revenue $2.9B · net income $0.2B · net margin 8.5% · diluted EPS 2.08
These are next-fiscal-year annual uplift estimates, not next-quarter numbers.
Aggregate next-FY est. rev uplift: 0.0% · next-FY EPS uplift: 0.0% · vs analysts: inline · priced in: medium (model's call-read: high; verdict above is the hard-data one used for ranking) · confidence: 5/10
| Claim | Figure | Arithmetic | Next-FY Rev % | Next-FY EPS % |
|---|
Signals SaaS ARR growth ~40% YoY (Q1 FY26)
engagement · soft | 40% ARR growth YoY | ARR growth ≠ recognized revenue; no Signals/SaaS ARR $ base disclosed anywhere in the claims, so 40% cannot be translated to a company-level rev/EPS. Bookings≠revenue. Formula would be rev_uplift_pct = 100 × (ΔARR × FY26 recognition rate) / 2,856,051,000; base missing → unsizeable. | | |
Signals SaaS ARR growth north of 30% (Q1 FY26)
engagement · soft | >30% ARR growth | Lower-bound restatement of the same recurring-bookings metric on an undisclosed Signals base; no company-level $ base → unsizeable. | | |
Signals software organic growth guide: mid-single digits FY26
revenue · soft | +mid-single-digit organic FY2026 (≈5%) | Segment-only organic growth: company rev_uplift_pct = 100 × (0.05 × Signals_rev) / 2,856,051,000. Signals absolute revenue not disclosed in any claim → null. (Illustrative only: if Signals were ~20% of sales, +5% ≈ ~1.0% group rev; @25% incr net margin ≈ 1.25% adj EPS — not used.) | | |
Signals cadence: ~-20% Q2, high-teens H2 FY26
revenue · soft | -20% Q2 / high-teens H2 | Intra-year phasing of the same Signals line; nets to the mid-single-digit full-year guide. Undisclosed dollar base → cannot size; informational only. | | |
Internal AI implementation 'a fraction of the cost'
cost · soft | fraction of peer cost (no $) | No dollar amount, headcount, or opex base. Vague/unanchored cost-out → cannot compute after-tax saving or EPS%. Forward Investor-Day narrative only. | | |
Lilly TuneLab built on >$1B Lilly R&D
other · soft | >$1B (Lilly's spend) | Partner (Lilly) historical R&D, not Revvity revenue or cost. No flow to RVTY rev/EPS; excluded from adopter/supplier aggregates. | | |
Signals to 'at least double' in 4-5 yrs (~15% organic)
revenue · soft | double rev in 4-5 yrs / ~15% organic | Validates mgmt math: (1.15)^5 = 2.01 → ~2× in 5y. Multi-year ambition; next-FY (FY26) is governed by mgmt's explicit mid-single-digit Signals guide and 'no material benefit in 2026', not 15%. Beyond FY26 it is real optionality but Signals $ base is undisclosed → company-level % null. Not additive with FY26 guide. | | |
No material FY26 benefit from new software/AI launches
revenue | no material benefit (FY2026) | Mgmt explicitly embeds ~$0 incremental software/AI launch revenue in the FY26 guide: rev_uplift_pct = 100 × 0 / 2,856,051,000 = 0.0%; incremental NI = 0 → eps_uplift_pct = 100 × 0 / $570,634,227 (adj NI) = 0.0%. This is the only hard, next-FY-sizeable AI claim and it pins near-term AI uplift at zero. | 0.0 | 0.0 |
Signals SaaS ARR growth nearly 40% (Q4 FY25)
engagement · soft | ~40% ARR growth | Backward-looking recurring-bookings growth on an undisclosed base; ARR ≠ next-FY recognized revenue. Unsizeable to company revenue. | | |
High content screening double-digit growth (Q4 FY25)
revenue · soft | double-digit organic (≈10%) | Product-line growth, AI-adjacent at best, not labeled AI revenue: rev_uplift_pct = 100 × (0.10 × HCS_rev) / 2,856,051,000. HCS revenue $ undisclosed → null. | | |
Signals full-year organic growth high teens (FY25)
revenue · soft | high-teens organic | Backward-looking FY25 actual on undisclosed Signals base; not a forward FY26 incremental claim. Unsizeable; excluded from FY26 aggregate. | | |
Assumptions: Next fiscal year = FY2026 (year-end 2026-12-28); company revenue base $2,856,051,000. EPS sized vs consensus/adjusted base (FY2025 adj NI ~$570.6M, adj EPS ~$4.94), NOT GAAP NI $241.7M / GAAP EPS $2.08 (GAAP depressed by one-offs → a GAAP denominator inflates any EPS%). Tax rate 21% for any cost-out (none quantified). Incremental net margin for software scenarios ~25% (higher-margin SaaS/software) — unused since no Signals/HCS dollar base is disclosed. Phasing: per explicit 'no material benefit in 2026', next-FY AI uplift = 0; the 'double in 4-5 yrs' (~15% organic) is beyond the next fiscal year. Disclosed-base rule applied strictly: Signals/SaaS/HCS absolute revenue is NOT given in any claim, so growth-rate claims on those lines are soft (unanchored base), not invented; ARR treated as bookings-like, requiring ARR level + ratable conversion to size next-FY revenue (not available).
Top line: Revvity is a pure AI ADOPTER (Signals software, internal AI cost-out) — no supplier/compute revenue. The only hard, next-FY-sizeable AI statement is mgmt's explicit guidance embedding NO material benefit from software/AI launches in FY26 → quantifiable next-FY AI revenue uplift = 0% of $2,856M. Bullish data points (Signals SaaS ARR +~30-40% YoY, Signals 'at least double in 4-5 years' at ~15% organic, HCS double-digit) are real but unsizeable: Signals/HCS absolute revenue is never disclosed and ARR is recurring bookings, not revenue. Illustrative ceiling if Signals were ~20-25% of sales and grew ~5% organically: ~0.75-1.25% company revenue (~$21-36M) — not in the headline aggregate. Genuine but unquantified multi-year optionality.
Bottom line: No quantifiable next-FY EPS uplift. Internal AI cost-out is unanchored (no $), and hard FY26 software/AI launch EPS uplift = 0.0% vs adj NI $570.6M. Illustrative segment path at ~1.0% rev uplift and 25% incremental margin ≈ $7.1M / $570.6M ≈ 1.3% adj EPS — sensitive to undisclosed Signals mix; not aggregated.
Adopter-side hard FY26 AI launch uplift = 0.0% rev / 0.0% EPS, explicitly embedded in mgmt guidance, so consensus already reflects it — inline, highly priced-in. All other AI/segment data points (Signals ARR +30-40%, mid-single-digit FY26 organic, HCS double-digit, 'double in 4-5 years') lack a disclosed dollar base and cannot be mapped to company-level rev/EPS, so they remain soft optionality rather than quantifiable upside.
MODEL CONSENSUS (impact)
Opus+GPT ✓ agree
Both answers concur: pure adopter, hard FY26 AI uplift 0%/0% vs adj NI $570.6M, all segment/ARR claims soft (undisclosed Signals/HCS base). Adopted Y's explicit arithmetic and completed verdict fields.
Conflicts reconciled
- est_rev_uplift_pct/est_eps_uplift_pct: X=0/0 vs Y=0.0/0.0 -> used 0.0/0.0 (identical; both anchored to mgmt's 'no material FY26 benefit')
- revenue base: X cites $2,779M in example vs Y uses $2,856,051,000 -> used $2,856,051,000 (Y's explicit FY base; moot since uplift=0)
| Field | Opus 4.8 | GPT-5.5 |
|---|
| Rev uplift % | 0 | – |
| EPS uplift % | 0 | – |
| Priced in | medium | – |
| vs analysts | inline | – |
| Confidence | 5 | – |
| Top line | Revvity is a pure AI ADOPTER (Signals software, LabGistics/Synthetica workflows, internal AI cost-out) — no supplier/compute revenue. The only hard, next-FY-sizeable AI statement is management's explicit guidance that it embeds NO material benefit from software/AI launches in 2026 → near-term AI revenue uplift ≈ 0%. The bullish data points (Signals SaaS ARR +~40% YoY, Signals 'at least double in 4-5 years' at ~15% organic) are real but unsizeable from the inputs: Signals' absolute revenue is never disclosed, and ARR is recurring bookings, not revenue. So the quantifiable next-FY topline uplift is 0%, with genuine but unquantified multi-year optionality. | – |
| Bottom line | No quantifiable next-FY EPS uplift. The internal-AI 'remarkable cost-out at a fraction of peer cost' has no dollar/headcount anchor, so no after-tax saving can be computed (would be sized off the ~$570.6M adjusted base, 21% tax, if disclosed). With the 2026 AI benefit explicitly set at zero by management, est_eps_uplift_pct = 0. Note the GAAP base is thin (8.5% margin, $241.7M NI) so any future small saving sized off GAAP would optically exaggerate EPS% — another reason to anchor on the ~$570.6M adjusted base. | – |
| Reasoning | Consensus FY2026 revenue $2,832.1M is essentially flat-to-down vs FY2025 consensus $2,847.7M (-0.5%); FY2027 $2,979.2M implies only ~+2.6% CAGR. Consensus EPS rises $4.937 (FY25) → $5.242 (FY26) = +6.2%, a margin/buyback story, not AI. Management's own 'no material AI/software benefit in 2026' aligns exactly with this flat-revenue consensus, so for the NEXT fiscal year the AI uplift (~0%) is fully consistent with the numbers — no near-term gap to exploit (inline). The unpriced piece is the longer-dated Signals 'double in 4-5 years' (~15% organic), which consensus's ~2.6% revenue trajectory clearly does NOT embed — real optionality, but beyond next-FY and unsizeable here (Signals dollar base undisclosed), so it does not move the next-FY estimate. Hence priced_in medium: near-term nothing to price, long-term optionality genuinely absent from estimates but unquantified. | – |
Rows highlighted where the two models disagreed.
QUANTIFICATIONS
SaaS ARR growth (Signals; discussed in AI/software context, not attributed to AI revenue): 40% year-over-year (Q1 FY2026, topline)
“our SaaS pipeline continues to grow robustly with 40% ARR growth year-over-year leading to the business, again, growing double digits from an APV perspective.”
SaaS ARR growth (Signals): north of 30% (Q1 FY2026, topline)
“we continue to see robust growth from a SaaS and ARR perspective, and that was north of 30% in the quarter.”
Signals software organic growth guide: positive mid-single digits for full year 2026 (FY2026, topline)
“for the full year for this business, we are calling for positive mid-single digits organic growth.”
Signals software organic growth cadence: down approximately 20% in Q2; high teens in second half (Q2 and H2 FY2026, topline)
“In the second quarter, we do have tougher comps. And so we expect that business to be down approximately 20% in the second quarter. However, those comps ease in the second half of the year and for the second half of the year for this business, we expect it to grow in the high teens.”
Internal AI implementation cost vs peers: a fraction of the cost (no dollar amount) (current, bottomline)
“we are doing this at a fraction of a cost that you would see from traditional AI corporate implementation.”
Lilly TuneLab model training investment (partner, not Revvity): over a billion dollars of R&D investment (last decade (Lilly), topline)
“Lilly TuneLab's AI models are built on knowledge and insight from over a billion dollars of R&D investment by the company over the last decade.”
Signals revenue ambition tied to AI-era software launches: at least double revenue over the next four to five years (~15% organic growth rate) (next four to five years, topline)
“we would be really disappointed if this business does not at least double in revenue over the next four to five years, which would imply something closer to a 15% organic growth rate.”
Embedded 2026 uplift from new software/AI launches: no material benefit (FY2026, topline)
“We are not embedding any material benefit from those software launches in 2026.”
SaaS ARR growth (Signals): nearly 40% (Q4 FY2025 vs prior year, topline)
“our SaaS pipeline continues to grow with nearly 40% ARR growth as compared to last year”
High content screening organic growth: double-digit growth (Q4 FY2025, topline)
“high content screening for us ... did end up being strong, I would say, double-digit growth in the fourth quarter”
Signals full-year organic growth: high teens (FY2025, topline)
“For the full year, our signals business grew in the high teens organically.”
PAST (realized)
- Q1 FY2026 — Introduced Xynthetica in December as an AI models-as-a-service marketplace connecting computational capabilities to wet lab research.
- Q1 FY2026 — Introduced BioDesign as a cloud-native molecular design platform for biologics development.
- Q1 FY2026 — Gartner published research highlighting Revvity's internal AI deployment; structured approach accelerated software delivery.
- Q1 FY2026 — Rolled out multiple leading LLMs to the global employee base with adoption above corporate averages at a fraction of traditional implementation cost.
- Q4 FY2025 — Introduced Signals Synthetica as an AI/ML models-as-a-service platform within existing Signals workflows.
- Q4 FY2025 — Announced Lilly TuneLab collaboration including Synthetica modeling credits for biotech users.
- Q4 FY2025 — Signals software grew high teens organically for the full year; SaaS ARR grew nearly 40% YoY.
- Q4 FY2025 — High content screening grew double digits organically in Q4, sold into pharma/biotech.
CURRENT (now)
- Q1 FY2026 — Stronger demand for high content screening including data generation for AI model creation and training; new Opera Phenix OptIQ aligned with AI-driven preclinical R&D trends.
- Q1 FY2026 — Customer engagement remains high around Xynthetica, BioDesign, and upcoming LabGistics; Lilly TuneLab partnership is the launch path for Xynthetica.
- Q1 FY2026 — Internal AI adoption is differentiated, focused on software development productivity, with employee feedback on productivity and efficiency.
- Q1 FY2026 — Signals software grew mid-single digits organically in Q1; SaaS ARR grew north of 30% with 40% YoY SaaS pipeline/ARR cited in prepared remarks.
- Q4 FY2025 — Scientists can leverage AI/ML models in Signals via Synthetica for lab-in-the-loop drug development.
- Q4 FY2025 — Software launches (Synthetica, BioDesign, Labgistics) are customer-pull driven upsells into the Signals suite rather than greenfield pushes.
FORWARD (guidance)
- Q1 FY2026 — LabGistics, an AI-first drug discovery-to-development workflow, expected toward end of 2026.
- Q1 FY2026 — Management frames life sciences AI in an infrastructure build-out phase, then value creation (more wet-lab validation demand for Revvity tools), then value capture (faster timelines, higher success rates, virtuous cycle).
- Q1 FY2026 — Expect consumables, instruments, and software demand to increase as customers use Revvity offerings to validate AI-generated discoveries.
- Q1 FY2026 — Mid- to longer-term remarkable cost-out from internal AI; Investor Day (Nov 13) to deepen software/AI growth narrative.
- Q4 FY2025 — Synthetica expected to accelerate drug development timelines via faster refinement loops.
- Q4 FY2025 — 2026 guidance embeds no material benefit from software/AI launches; management would be disappointed if Signals did not at least double revenue in four to five years (~15% organic).
- Q4 FY2025 — High content screening momentum expected to continue in 2026.
TRACK RECORD — PROMISE vs DELIVERY
62/100 track record mixed 6 calls reviewed
Revvity seldom gives hard AI KPIs; they delivered on Signals software growth but did not report against the 50 internal AI-agent target after stating it.
Signals software strong double-digit organic growth for FY2025 — promised Q1 FY2025
delivered Q4 FY2025 reported Signals grew high teens organically for the full year, meeting double-digit guidance.
Expand internal custom AI agents from 30+ to 50+ by end of FY2025 — promised Q3 FY2025
quietly-dropped No agent-count or milestone update in Q4 FY2025 or Q1 FY2026 despite detailed internal-AI discussion.
Launch AI-first LabGistics drug-discovery workflow by end of 2026 — promised Q1 FY2026
too-early Still a forward pipeline item at last transcript; no delivery evidence in this call set.
PRICED-IN (REFINED)
MEDIUMEst. revisions flat · Fwd P/E 20.2 · EV/Sales 4.7x
AI claim maps to Life Sciences, Diagnostics
Analyst ratings softened from early 2026 (fewer strong buys/buys, holds steady at 9) while price targets are only modestly higher recently (lastMonth 114 vs lastQuarter 109.25); forward consensus shows FY26 revenue slightly below FY25 with only mid-single-digit EPS growth to FY27, so AI-driven acceleration is not clearly baked into estimates. At ~20x next-FY EPS and ~4.7x EV/sales, the stock carries a quality/tools premium but not a clear AI-re-rating versus typical mature diagnostics/life-sciences multiples. AI upside would most plausibly flow through Life Sciences (informatics, screening, discovery tools) and Diagnostics (lab workflow/automation), yet segment-level estimates are not being revised up aggressively—mixed signals yield a medium priced-in verdict.
COVERAGE — ENTHUSIASM TRAJECTORY + CATALYSTS
5Q4 FY20246Q1 FY20254Q2 FY20258Q3 FY20258Q4 FY20259Q1 FY2026
AI enthusiasm across 6 calls — trend ↗ rising
AI evolved from brief Phenologic/Signals mentions to a CEO-led Signals AI platform strategy with named products, customer workflows, and revenue-linked software growth.
RECENT AI CATALYSTS & NEWS
BUSINESS IMPACT - QUALITATIVE MATERIALITY
6/10 qualitative impact moderate medium-term · mixed evidence
Where AI matters: Life Sciences Signals software, screening instruments, consumables
Real shipped AI products (Xynthetica, BioDesign) and wet-lab validation demand narrative, but mgmt embeds no material FY26 uplift from AI launches and never sizes AI-specific revenue—Signals ARR growth is engagement, not provable company P&L.
Caveats: FY26 guidance explicitly excludes material benefit from new AI software launches; Signals revenue base undisclosed—ARR/bookings cannot be mapped to group rev/EPS; Q2 FY26 Signals organic comps ~-20% despite AI narrative; Internal AI agent-count target went unreported after Q3 FY25 promise
AI DISRUPTION / CANNIBALIZATION RISK tailwind · 3/10
Customer AI expands hypothesis volume and validation pull for instruments/reagents and regulated diagnostics; in-silico pressure on SaaS pricing is real but secondary to a durable razor-blade and FDA-cleared assay model.
OPTIONS / MARKET STRUCTURE
option liquidity: fair
proxy inputs — dollar-ADV $128M · beta 1.051 · px $99.08
source: proxy (no options chain on FMP)
FMP /stable/ exposes no options-chain endpoint on this key, so ATM IV, bid-ask spread and open interest are unavailable. Liquidity below is a PROXY from dollar-ADV, beta and price level (a stand-in for option depth), not measured option-market data.
CONFIRMATION — INSIDERS · 13F · LANGUAGE
Undercutting — insiders selling, institutions unknown, management language 5/10 measured.
INSIDERS selling 13 open-market sell(s) vs 0 buy(s) — net distribution
INSTITUTIONS (13F) unknown no 13F data returned
MGMT LANGUAGE 5/10 measured Substantial AI narrative; firm on internal rollout and products, but customer upside is belief-led with no quantified targets.
commit “we are seeing employee adoption rates of AI well above corporate averages”
commit “we are dramatically transforming our internal operations through AI adoption”
commit “we introduced Xynthetica in December, our AI models as a service platform”
VERBATIM AI QUOTES
“In our Signals software business, we introduced Xynthetica in December, our AI models as a service platform that serves as a secure marketplace, collecting computational capabilities to wet lab research.”
— Prahlad Singh, Q1 FY2026
“Last month, we introduced BioDesign, our cloud-native molecular design platform for biologics development.”
— Prahlad Singh, Q1 FY2026
“Then towards the end of this year, we'll introduce LabGistics, a novel AI-first drug discovery to drug development workflow offering, rounding out an impressive year of software innovation that demonstrates our ability to rapidly bring new capabilities to market.”
— Prahlad Singh, Q1 FY2026
“we have been seeing stronger demand for our high content screening portfolio, driven by increases in GLP-1 related research, new approach methodologies, including organ on chip development work and data generation for AI model creation and training, amongst other validation related work.”
— Prahlad Singh, Q1 FY2026
“This is another great example of one of our key product lines, which we believe will meaningfully benefit from increasing AI adoption by our customers in their preclinical R&D work.”
— Prahlad Singh, Q1 FY2026
“I think it is important to clearly address the transformational impact of artificial intelligence and life sciences research. AI is dramatically accelerating scientific discovery, enabling researchers to identify and design exponentially more therapeutic compounds and biological targets than ever before.”
— Prahlad Singh, Q1 FY2026
“Today, we are in what would be called the infrastructure build-out phase, similar to the early days of the Internet, when companies were laying fiber optic cables and building foundational systems that would support the digital transformation.”
— Prahlad Singh, Q1 FY2026
“Every AI-generated discovery will still require physical validation through wet lab experimentation. One cannot approve a drug based solely on computational predictions.”
— Prahlad Singh, Q1 FY2026
“As AI generates more promising therapeutic hypothesis at an unprecedented rate, the downstream demand for laboratory tools, reagents, and instruments to validate these discoveries will grow substantially.”
— Prahlad Singh, Q1 FY2026
“Beyond our external AI strategy, we are dramatically transforming our internal operations through AI adoption that I believe is quite differentiated and includes appropriately repositioning our employees and their roles.”
— Prahlad Singh, Q1 FY2026
“The well-known research from Gartner recently published a research paper highlighting our internal AI deployment, which stands out across the industries that they've researched.”
— Prahlad Singh, Q1 FY2026
“With our unique rollout of multiple leading LLMs to the entirety of our global employee base, we are seeing employee adoption rates of AI well above corporate averages, and we are doing so at a fraction of the cost of traditional AI corporate implementations.”
— Prahlad Singh, Q1 FY2026
“Software will be a central theme of that discussion, and we are excited to provide much deeper insights into how our offerings in this space will enable long-term growth.”
— Prahlad Singh, Q1 FY2026
“We announced the Lilly TuneLab partnership, which is a great launch path for Xynthetica, leveraging the ecosystem that Lilly brings to the table.”
— Prahlad Singh, Q1 FY2026
“the question really is not really how AI is going to impact, but how are we going to leverage AI in the development of the software into bringing Xynthetica early on.”
— Prahlad Singh, Q1 FY2026
“From an internal operations perspective, the AI adoption, I would say, is going very well and is quite differentiated.”
— Prahlad Singh, Q1 FY2026
“We are rolling out multiple leading LLMs to our total employee global -- global employee base. And the adoption rate is well above what we are seeing in terms of peers' metrics out there from corporate averages perspective.”
— Prahlad Singh, Q1 FY2026
“we are doing this at a fraction of a cost that you would see from traditional AI corporate implementation.”
— Prahlad Singh, Q1 FY2026
“And in the mid- to longer term, the cost-out impact that it will have on the business will be remarkable.”
— Prahlad Singh, Q1 FY2026
“Another item we are extremely excited about as we move into 2026 is our recent introduction and upcoming launch of our AI models as a service platform, Signals Synthetica.”
— Prahlad Singh, Q4 FY2025
“With the introduction of Synthetica, we are providing a platform where bench scientists will be able to seamlessly leverage industry-leading AI and ML models that are both publicly and privately available directly within their existing workflows.”
— Prahlad Singh, Q4 FY2025
“The insights gained by leveraging these AI models will be used by scientists to more quickly iterate and improve their drug candidates and development, both in the wet lab and virtually, enabling a lab-in-the-loop approach to drug development.”
— Prahlad Singh, Q4 FY2025
“Lilly TuneLab's AI models are built on knowledge and insight from over a billion dollars of R&D investment by the company over the last decade.”
— Prahlad Singh, Q4 FY2025
“they are also co-funding with us access to our Signals platform and providing Synthetica modeling credits to biotech users, exemplifying our shared commitment to driving and engagement of both platforms.”
— Prahlad Singh, Q4 FY2025
“we introduced several very exciting new offerings and collaborations during the quarter, particularly in the areas of software and AI”
— Maxwell Krakowiak, Q4 FY2025
“We are not embedding any material benefit from those software launches in 2026.”
— Maxwell Krakowiak, Q4 FY2025
“even before all these new product launches, you know, the advent of AI or how we might participate there, the business is already performing better than we expect from it over the coming years.”
— Prahlad Singh, Q4 FY2025
“we would be really disappointed if this business does not at least double in revenue over the next four to five years, which would imply something closer to a 15% organic growth rate.”
— Prahlad Singh, Q4 FY2025
“Synthetica for me is not an AI. It's even more potentially important in the near term as it is in the longer term because what it does is it brings to action how drug discovery happens.”
— Prahlad Singh, Q4 FY2025
“It provides a federated model where you are able to curate put AI models on one platform that are validated and be able to use them and enable drug discovery to happen in an efficient form.”
— Prahlad Singh, Q4 FY2025
“I think in the longer term, the benefit of what you will see from that is not just on productivity and efficiency but also acceleration of drug discovery.”
— Prahlad Singh, Q4 FY2025
“high content screening for us, we had mentioned, was looking at a strong fourth quarter. It did end up being strong, I would say, double-digit growth in the fourth quarter as we continue to see momentum there, which, again, really is sold into the pharma biotech environment.”
— Maxwell Krakowiak, Q4 FY2025
ANALYST QUESTIONS ON AI
Q (Q1 FY2026, Patrick Donnelly (Citi)): Can you just talk about the recent conversations with customers? ... on the software SaaS piece ... recent trends.
A: We announced the Lilly TuneLab partnership, which is a great launch path for Xynthetica ... the question really is not really how AI is going to impact, but how are we going to leverage AI in the development of the software into bringing Xynthetica early on. ... excitement level around Xynthetica, BioDesign, LabGistics ... engagement level and excitement level continues to remain very high.
Q (Q1 FY2026, Puneet Souda (Leerink)): Wanted to understand a bit more about the AI corporate implementation. What are some of the steps there that you're taking that could yield sort of an immediate or near-term result? And how are you thinking about margin uplift from that this year?
A: From an internal operations perspective, the AI adoption ... is going very well and is quite differentiated. ... Gartner research paper ... benefits ... primarily around the software development component. ... rolling out multiple leading LLMs ... adoption rate is well above ... peers' metrics ... at a fraction of a cost ... traditional AI corporate implementation. ... in the mid- to longer term, the cost-out impact ... will be remarkable.
Q (Q1 FY2026, Tycho Peterson (Jefferies)): There's also been a view that spending on upstream is going to go up to train the model. So how do you think about that kind of layering in over the next couple of years?
A: On the instrument side and on the reagent side, we already started seeing modest improvements ... Our Life Sciences Solutions were up low single digit from pharma/biotech in Q1 ... We would like to obviously continue to see even greater pickup in the reagents before we can say things are on a clear path to improvement.
Q (Q4 FY2025, Daniel Louis Leonard (UBS)): I could just, you know, use some help better understanding how you're framing the economic opportunity for that AI drug discovery offering and software.
A: Synthetica for me is not an AI. It's even more potentially important ... brings to action how drug discovery happens ... move from only being a wet lab to doing in silico modeling ... link it back to what happens in the wet lab ... federated model where you are able to curate put AI models on one platform that are validated ... in the longer term ... not just on productivity and efficiency but also acceleration of drug discovery.
Q (Q4 FY2025, Dan Brennan (TD Cowen)): On the life science side, is it really just preclinical spending recovering that's gonna drive the strength in instruments and reagents?
A: (Max) One path to upside is software launches at '25, early '26; we are not embedding any material benefit from those software launches in 2026. (Prahlad on separate software question) advent of AI or how we might participate there ... potential for our growth rates in this business to accelerate even further.
Q (Q4 FY2025, Daniel Anthony Arias (Stifel)): Can you just sort of refresh us on the timing of coming to market [software launches] and then what your uptake trajectory might be? ... how quickly do you think gets you back to the nine to, I believe, 11% range ... for software?
A: introduction of Synthetica, and the launch later this year of Labgistics ... advent of AI or how we might participate there ... we would be really disappointed if this business does not at least double in revenue over the next four to five years, which would imply something closer to a 15% organic growth rate.