DataHow

AI-powered bioprocess development and manufacturing platform for the biopharmaceutical industry.

Cover Block

Public sources

Field	Value
Name	DataHow
Tagline	AI-powered bioprocess development and manufacturing platform for the biopharmaceutical industry [DataHow, May 2024]
Headquarters	Zurich, Switzerland [startup.ch]
Founded	2017 [Momenta]
Stage	Series A [Greater Zurich Area, May 2024]
Business Model	SaaS
Industry	Deeptech
Technology	AI / Machine Learning
Geography	Western Europe
Growth Profile	Venture Scale
Founding Team	Academic spinout, ETH Zurich spin-off [DataHow, May 2024]
Funding Label	Undisclosed [Greater Zurich Area, May 2024]

Executive Summary

PUBLIC DataHow builds software for biopharmaceutical process development and manufacturing, and it merits investor attention because it sits at the intersection of AI tooling and a regulated industrial workflow where model quality and deployment credibility matter more than broad user growth claims [DataHow, May 2024] [TetraScience, November 2026]. Founded in Zurich in 2017 as an ETH Zurich spin-off, the company has positioned itself around bioprocess modeling rather than general laboratory software, with public materials consistently tying the business to data analytics and process modeling for biopharma and adjacent process industries [DataHow, May 2024] [Chemeurope.com]. Its core platform, DataHowLab, combines mechanistic process knowledge with machine learning, a hybrid approach the company says supports development, monitoring, optimization, control, and scale-up of manufacturing processes; the differentiation case rests on that hybrid modeling layer and on expansion into real-time connectivity, model-driven process control, and downstream processing [DataHow, May 2024] [DataHow, September 2026].

The founding team appears technically credible in the public record: Alessandro Butté is identified as co-founder and CEO across company and third-party profiles, Fabian Feidl is identified as co-founder and CTO, Michael Sokolov as co-founder, and Massimo Morbidelli as co-founder and president, with the broader team described as emerging from ETH Zurich research with industry experience [Crunchbase] [Pharma Almanac] [The Venture Codex] [Bioprocessing Summit Europe] [DataHow, May 2024]. On financing, the clearest public read is a Series A announced in May 2024 led by Momenta, with Rockwell Automation and Zürcher Kantonalbank named as participants, followed by a September 2026 bridge financing framed as an extension of that Series A and including continued support from existing investors plus HINA Bioventures [Greater Zurich Area, May 2024] [DataHow, September 2026].

The most notable traction signal in public sources is the claim, via TetraScience, that customers include 12 of the top 20 global pharmaceutical companies, alongside ecosystem partnerships with Eppendorf, Genedata, and TetraScience that point to an effort to embed more deeply into bioprocess data and manufacturing workflows [TetraScience, November 2026] [DataHow, December 2024] [Genedata, 2026]. Over the next 12 to 18 months, the key watchpoints are whether the bridge financing translates into broader product surface adoption in downstream and real-time control, whether large pharma usage converts into durable platform penetration rather than project-based deployments, and whether hiring against the machine learning stack indicates continued product deepening rather than support-heavy customization [DataHow, September 2026] [DataHow].

Lightly corroborated -- Mix of company announcements and third-party profiles, with core funding and leadership partially corroborated by independent public sources.

Taxonomy Snapshot

Axis	Value
Stage	Series A
Business Model	SaaS
Industry / Vertical	Deeptech
Technology Type	AI / Machine Learning
Geography	Western Europe
Growth Profile	Venture Scale
Founding Team	Academic Spinout
Funding	Undisclosed

How the Company Got Here

PUBLIC DataHow sits in a part of industrial software where the buyer problem is concrete and expensive: biopharmaceutical process development still produces fragmented data, long experimental cycles, and difficult scale-up decisions. Public company materials describe the business as a Zurich-based bioprocess-technology company founded in 2017 and spun out of ETH Zurich, with software and consulting focused on data analytics, process modeling, screening, monitoring, optimization, control, and scale-up for biopharma and adjacent process industries [DataHow, May 2024] [Crunchbase].

The public record on corporate basics is directionally consistent, if not especially rich. Crunchbase lists DataHow as DataHow AG and identifies Zurich, Switzerland as headquarters, while the company describes itself as an ETH Zurich spin-off led by co-founder and CEO Alessandro Butté [Crunchbase] [DataHow, September 2026].

A short milestone history is visible from company announcements. In May 2024, DataHow announced a Series A led by Momenta to support wider deployment of DataHowLab, its bioprocess software platform [DataHow, May 2024]. In September 2026, the company said it secured bridge financing as an extension of that Series A to expand real-time connectivity, model-driven process control, and downstream capabilities, which suggests a business still broadening product scope within the same operating thesis rather than resetting strategy [DataHow, September 2026].

Lightly corroborated -- Confirmed in part by Crunchbase and company announcements; key corporate history details rely materially on company website disclosures.

Product and Technology

Core platform

MIXED DataHow’s product story is legible from public materials, even if most of the detail still comes from the company itself. DataHowLab is described as a digital bioprocess development and manufacturing platform that combines mechanistic process knowledge with machine learning, a "hybrid modeling" approach the company says helps biopharmaceutical teams develop, monitor, optimize, control, and scale manufacturing processes [DataHow, May 2024]. In later company materials, DataHow expanded that description to include process analytics, modeling, transfer learning, real-time connectivity, model-driven process control, and downstream processing [DataHow, September 2026].

The practical point is that DataHow is not presenting as a general-purpose AI layer for life sciences, but as a workflow product for process engineers working on biopharma development and manufacturing [DataHow, May 2024] [RoundOS]. Public third-party references broadly line up with that framing, describing the company as focused on data analytics, process modeling, digital twins, and bioprocess development for pharmaceutical and chemical-process users [Crunchbase] [RoundOS].

Deployment signals and technical posture

MIXED External validation is still stronger on adoption signals than on underlying architecture. TetraScience said in November 2026 that DataHow’s customers include 12 of the top 20 global pharmaceutical companies, and that the company joined the Tetra Partner Network to support bioprocess data and digital-development workflows [TetraScience, November 2026]. Separate partnership announcements with Eppendorf, focused on bioprocess data management, and Genedata, focused on process development and manufacturing digitalization, suggest the product is being positioned inside established data and software environments rather than sold as a stand-alone point tool [DataHow, December 2024] [Genedata, 2026].

One hiring signal adds a narrow technical clue. DataHow’s careers page lists a Lead Machine Learning Engineer role responsible for the quality, evolution, and technical direction of the machine-learning stack underlying DataHowLab, which supports the company’s claim that ML is part of the production product rather than only a services layer [DataHow]. Beyond that, the public record does not support a detailed stack description, so any deeper read on infrastructure would be speculative.

Company-stated, unverified -- This section relies primarily on company materials, with partial corroboration from TetraScience, Crunchbase, RoundOS, Genedata, and partnership announcements.

Where the Demand Sits

PUBLIC

Biopharma process development software sits in a timely part of the stack because manufacturers are under pressure to shorten development cycles, improve process consistency, and connect fragmented production data without taking on full plant replacement programs [DataHow, May 2024] [DataHow, September 2026] [Genedata, 2026].

The public record here is stronger on demand signals than on formal market sizing. No named third-party TAM, SAM, or SOM study for DataHow's exact category appears in the supplied source set, so sizing has to be handled conservatively through adjacent market evidence rather than asserted as a precise revenue pool. What is visible is that DataHow is positioned at the intersection of bioprocess analytics, process modeling, digital manufacturing, and biopharma data infrastructure, with its product described as supporting process analytics, modeling, transfer learning, real-time connectivity, model-driven process control, and downstream processing [DataHow, September 2026]. That places the company inside a broader shift toward software-assisted biologics development and manufacturing, but the sources do not support a clean standalone market estimate for hybrid bioprocess modeling software.

Demand drivers are easier to document. DataHow's own financing announcements consistently frame the commercial need around reducing experimental burden and improving process understanding in biopharmaceutical development and manufacturing [DataHow, May 2024]. The 2026 bridge financing announcement is also useful because it narrows where customer demand appears to be moving, namely toward real-time connectivity, model-driven process control, and downstream process support rather than offline modeling alone [DataHow, September 2026]. Separately, partner announcements from Eppendorf, Genedata, and TetraScience all point to the same operational problem set: bioprocess teams need better data management, tighter software interoperability, and more continuous links between development and manufacturing environments [DataHow, December 2024] [Genedata, 2026] [TetraScience, November 2026].

A practical way to read the market is through adjacent and substitute categories. The nearest adjacent markets in the source base are bioprocess data management, scientific data cloud infrastructure, process development digitalization, and manufacturing control software, each of which can absorb some of the budget that might otherwise go to a specialized hybrid-modeling platform [DataHow, December 2024] [Genedata, 2026] [TetraScience, November 2026]. The substitute risk is not only another vendor. It is also the status quo of internal process science teams using spreadsheets, bespoke statistical models, and local data pipelines, especially inside large pharma groups that already have entrenched software validation workflows. That matters because a category can be strategically important and still sell slowly if it asks customers to change validated operating procedures.

The regulatory and macro backdrop is supportive in a measured way. Biopharmaceutical manufacturing remains a compliance-heavy environment, which tends to reward tools that improve traceability, process understanding, and reproducibility, but it also lengthens deployment cycles and raises validation demands for new software [Genedata, 2026] [TetraScience, November 2026]. The iNDUCARE consortium announcement adds another signal: advanced therapies and patient-specific manufacturing are creating more complex production settings where digital process support may become more valuable over time [DataHow, August 2026]. Still, the public sources support a tailwind thesis more than a timing thesis. They show why this category should exist, but not how quickly budgets convert into standardized platform adoption.

Cited market signal	What it suggests
Customers include 12 of the top 20 global pharmaceutical companies [TetraScience, November 2026]	Large pharma appears willing to evaluate or deploy this type of software, though revenue depth per account is not disclosed.
Bridge financing aimed at real-time connectivity, model-driven process control, and downstream expansion [DataHow, September 2026]	Customer demand may be shifting from point modeling toward broader workflow coverage across manufacturing operations.
Eppendorf collaboration on bioprocess data management [DataHow, December 2024]	Data integration and instrument-adjacent workflows are part of the budget pool around this category.
Genedata partnership on process development and manufacturing digitalization [Genedata, 2026]	The category overlaps with established digital bioprocess and manufacturing informatics systems rather than standing alone.

The table points to a market that is strategically relevant but not cleanly bounded. The clearest public evidence is not a top-down market size figure, it is the pattern of partnerships and product expansion priorities around connected bioprocess data and model-driven manufacturing.

Lightly corroborated -- This section relies on named public sources, but the core market-sizing question is only partially supported by adjacent-category evidence rather than an independently cited TAM study.

Competitive Landscape

Competitive map

MIXED DataHow appears to sit in the bioprocess software layer between instrument vendors, manufacturing informatics platforms, and specialist modeling tools, with its position defined less by breadth than by the claim that hybrid modeling can be used across development, monitoring, control, and scale-up workflows [DataHow, May 2024] [DataHow, September 2026]. The evidence base here is narrower than in a typical software market map because no direct competitors are named in the structured source set, so the public record supports a category read more readily than a ranked peer set [Crunchbase] [CB Insights].

The incumbent flank is easiest to identify from DataHow's own partnership choices. Eppendorf sits in lab and bioprocess equipment, while Genedata and TetraScience sit closer to data infrastructure and scientific software, which suggests that DataHow is not trying to replace every layer of the stack so much as attach a modeling and decision layer to existing workflows [DataHow, December 2024] [Genedata, 2026] [TetraScience, November 2026]. That matters because buyers in biopharma manufacturing tend to prefer software that can coexist with validated systems rather than force a full platform switch, especially where process knowledge, data lineage, and operating continuity are sensitive.

The challenger set is harder to name from the available public evidence, but the substitute categories are still visible. A biopharma manufacturer evaluating DataHow can continue to rely on internal process scientists, conventional mechanistic modeling, point solutions for analytics, or broader data platforms paired with in-house machine learning teams, all of which compete for the same budget line even when they are not product-for-product substitutes [DataHow, May 2024] [Crunchbase]. In practice, DataHow's competitive question is whether a specialist platform can become systemically useful enough to survive alongside larger enterprise vendors that already control data capture, instrumentation relationships, or manufacturing software budgets.

Edge and exposure

MIXED The clearest edge in the public record is domain specificity. DataHow describes DataHowLab as combining mechanistic bioprocess knowledge with machine learning, and that hybrid approach is consistent across the company's financing announcements, website language, and third-party descriptions, which gives the positioning more credibility than a one-off product claim would [DataHow, May 2024] [DataHow, September 2026] [RoundOS]. If the reported customer footprint is directionally right, with customers including 12 of the top 20 global pharmaceutical companies, that would also imply referenceability in a market where vendor selection is conservative and deployment risk is high [TetraScience, November 2026].

That edge looks durable to the extent it is rooted in workflow fit, accumulated implementation know-how, and models refined through repeated use cases. DataHow says it has solved hundreds of use cases, but that claim is company-sourced and does not establish retention, expansion, or comparative win rates, so the durability case rests more on inference than on disclosed metrics [DataHow Technology]. The more perishable part of the moat is distribution: Momenta and Rockwell Automation provide useful industrial credibility around the cap table, yet there is no public evidence here that either investor has already turned that relationship into a scaled commercial channel for DataHowLab [Greater Zurich Area, May 2024] [DataHow, September 2026].

The main exposure is platform adjacency. Partners such as TetraScience and Genedata can be read as complements today, but both also occupy strategic territory near the data and workflow layer that often becomes the control point in enterprise software buying [TetraScience, November 2026] [Genedata, 2026]. If a customer decides that data unification and workflow orchestration matter more than specialist modeling depth, the advantage can shift toward the platform that already owns the broader scientific or manufacturing environment, leaving DataHow to defend a narrower analytical wedge.

Most plausible 18-month scenario

MIXED The most plausible near-term scenario is not category domination but deeper embedment within existing bioprocess software stacks. DataHow's 2026 bridge financing was explicitly tied to real-time connectivity, model-driven process control, and downstream processing expansion, which reads like a push from point capability toward a more operational role inside customer environments [DataHow, September 2026]. If that roadmap lands, the likely winner in this setup is DataHow itself, because better connectivity would strengthen the case that its models are not just advisory tools but part of day-to-day development and manufacturing decisions.

The more vulnerable party, under that same scenario, is the in-house status quo, meaning teams that still piece together process analytics from internal spreadsheets, bespoke models, and disconnected data systems. That is a real competitor for budget even if it is not a named vendor, and it loses ground if DataHow can show that hybrid models reduce experimental burden and transfer more reliably across process stages [DataHow, May 2024]. The inverse case also matters: if buyers continue to consolidate around broader data and workflow platforms, then specialist layers without clear ownership of the customer interface are the ones most at risk, and DataHow would need partnerships such as TetraScience, Genedata, and Eppendorf to convert from ecosystem signals into durable distribution [TetraScience, November 2026] [Genedata, 2026] [DataHow, December 2024].

Single unverified source -- This section relies on corroborated public facts for DataHow's positioning, funding context, and partnerships, but the direct competitor set is not named in the structured sources and several differentiation claims remain company-sourced [DataHow, May 2024] [DataHow, September 2026] [TetraScience, November 2026].

Opportunity

PUBLIC The prize here is not a better point tool for process engineers, it is a shot at becoming part of the operating layer that large biopharma companies use to develop, transfer, and control complex manufacturing processes across the lifecycle.[DataHow, May 2024] [DataHow, September 2026]

The headline opportunity is that DataHow could become a category-level software platform for digital bioprocess development and manufacturing, with hybrid modeling as the wedge rather than generic analytics alone.[DataHow, May 2024] The reason that outcome looks reachable, not merely aspirational, is the combination of three public signals: the company says DataHowLab already spans process analytics, modeling, transfer learning, real-time connectivity, model-driven process control, and downstream processing, which implies a product surface broad enough to expand within existing accounts over time.[DataHow, September 2026] Public partner and ecosystem announcements also show it plugging into adjacent infrastructure, including Eppendorf, Genedata, and the Tetra Partner Network.[DataHow, December 2024] [Genedata, 2026] [TetraScience, November 2026] The most important traction claim, while still only singly sourced, is that customers include 12 of the top 20 global pharmaceutical companies, which suggests the company is already selling into the exact accounts that matter if this category consolidates around a few trusted vendors.[TetraScience, November 2026]

A conservative reading is that DataHow is still early, especially because public funding amounts and customer names remain undisclosed.[Greater Zurich Area, May 2024] But the shape of the upside is clear enough: if the platform becomes embedded in high-value development and manufacturing workflows, replacement risk falls and expansion paths widen from modeling into control, data management, and downstream applications.[DataHow, September 2026] [DataHow, December 2024]

Scenario	What happens	Catalyst	Why it's plausible
Standard layer for top pharma process teams	DataHow becomes the common modeling and decision layer used across process development, tech transfer, and manufacturing in major biopharma accounts	The company extends DataHowLab from development into real-time connectivity, model-driven control, and downstream processing, turning a narrow use case into a broader operating platform [DataHow, September 2026]	The platform is already described as spanning those functions, and the company says customers include 12 of the top 20 global pharmaceutical companies [DataHow, September 2026] [TetraScience, November 2026]
Embedded partner in the bioprocess software stack	DataHow wins distribution by integrating with established data and equipment ecosystems rather than selling as a standalone application only	Partnerships with Eppendorf, Genedata, and TetraScience create routes into existing enterprise workflows and procurement paths [DataHow, December 2024] [Genedata, 2026] [TetraScience, November 2026]	Those partnerships indicate third parties see enough value to formalize interoperability and joint workflow positioning, which is often how infrastructure software enters regulated environments [DataHow, December 2024] [Genedata, 2026]
Digital backbone for advanced therapy manufacturing	DataHow extends from mainstream bioprocess optimization into newer manufacturing settings, including patient-specific cell therapy production workflows	Participation in the EU-funded iNDUCARE consortium gives the company a public foothold in manufacturing problems tied to autologous stem-cell therapies [DataHow, August 2026]	The consortium work does not prove commercial scale, but it does show relevance in harder manufacturing contexts where process modeling and control can matter disproportionately [DataHow, August 2026]

The compounding mechanism is straightforward. If DataHow first enters through modeling and process understanding, then expands into connectivity, control, and downstream workflows, each deployment should increase the cost of switching because more process knowledge, more historical data, and more operating decisions sit inside the same environment.[DataHow, May 2024] [DataHow, September 2026] In biopharma, that matters more than in many software markets because process changes are consequential, documentation-heavy, and tied to manufacturing performance.

There is also a softer but still material distribution flywheel visible in the public record. Each new integration or partner announcement makes the platform easier to slot into the existing lab and manufacturing stack, which should shorten proof-of-value cycles in similar accounts.[DataHow, December 2024] [Genedata, 2026] [TetraScience, November 2026] If the company has indeed reached 12 of the top 20 global pharmaceutical companies, even at limited initial scope, that installed base could become a reference network inside a concentrated buyer universe where peer validation carries unusual weight.[TetraScience, November 2026]

The size of the win is hard to quantify cleanly from the public record because there is no cited market-size dataset or public revenue base in the source set. Even so, one credible way to frame the upside is strategic value rather than near-term revenue arithmetic: if DataHow becomes a trusted system of record and decision support layer for bioprocess development and manufacturing inside large pharma, it starts to resemble an infrastructure asset that major life sciences software or automation platforms may want to own, especially given the visible alignment with Rockwell Automation as an investor and the public partnerships around data and manufacturing workflows.[Greater Zurich Area, May 2024] [DataHow, December 2024] [Genedata, 2026] That is a scenario, not a forecast. The public evidence supports a large strategic outcome as plausible; it does not yet support precision on valuation.

Lightly corroborated -- Relies on company announcements and partner announcements, with partial corroboration from Greater Zurich Area and TetraScience. Material traction and product breadth claims are not broadly independently verified.

Sources