How HexisLab and AI Are Putting Newcastle on the Map for Science-Led Skincare and Haircare Innovation

Key Highlights:
Introduction
From Lagos to Newcastle: the scientist behind HexisLab
How HexisLab’s AI platform models ingredient behavior
Bridging digital simulation and the lab: HexisLab’s workflow
Regulatory compliance and safety: navigating international standards
Layla & Kays: turning laboratory science into certified consumer products
How HexisLab’s model reduces development time and waste
Partnerships with universities and talent pipeline in the North East
Newcastle as a credible node for beauty science and consumer brands
Global clients, global challenges: operating across jurisdictions
Sustainability, ethics and consumer trust
Limitations and challenges of relying on AI in cosmetics R&D
How boutiques like HexisLab complement big-tech and corporate R&D
Talent, scaling and the future for HexisLab and regional beauty hubs
Practical takeaways for brands and entrepreneurs
Real-world examples and industry parallels
Challenges ahead: data, diversity and market expectations
What comes next for HexisLab and the sector
FAQ

Key Highlights:

HexisLab combines a proprietary AI-driven research platform with traditional laboratory validation to predict ingredient behavior, reduce development time, and maintain regulatory compliance for global beauty clients.
The Newcastle-based consultancy turned product house demonstrates how digital simulation, rigorous testing, and regional university partnerships can produce certified consumer products and attract international business.

Introduction

A boutique consultancy in Newcastle is demonstrating how machine learning and rigorous bench science can reshape product development in skincare and haircare. HexisLab, founded and led by Dr Olusula Idowu (Sola), pairs an AI-driven predictive platform with targeted laboratory validation to help brands—large and small—bring safer, higher-performing products to market faster. Operating from the Biosphere within the Newcastle Helix innovation district, HexisLab serves clients across multiple continents while also building its own consumer line, demonstrating that advanced ingredient science and commercial success can emerge from regional hubs outside traditional beauty capitals.

HexisLab’s approach confronts two persistent problems in cosmetics R&D: lengthy development cycles driven by trial-and-error experimentation, and growing consumer demand for transparent, scientific proof that products are safe and effective. The consultancy’s model—simulate, shortlist, validate, refine—reduces unnecessary testing and waste while helping brands navigate complex regulatory landscapes. This article traces HexisLab’s methods, situates them within global industry trends, examines product and regional impact, and explores the practical limits and ethical implications of AI in beauty science.

From Lagos to Newcastle: the scientist behind HexisLab

Dr Olusula Idowu’s personal and professional journey frames the consultancy’s identity. Originally from Lagos, he relocated to Newcastle more than 30 years ago to pursue doctoral studies at Newcastle University. He established HexisLab over 14 years ago and has since specialized in ingredient analysis, formulation, testing and validation. The consultancy’s longevity and small-team structure reflect a deliberate emphasis on expert-led, hands-on scientific work.

That background explains two defining features of HexisLab. First, the business is rooted in academic rigor and conventional laboratory practice; its founder’s training and long-term ties to regional research networks shape the company’s methods and partnerships. Second, HexisLab prioritizes iterative improvement: the proprietary AI platform predates the current mainstream hype around artificial intelligence, having been developed by Sola to augment—not replace—bench science. This lineage positions HexisLab as a practitioner of applied translational research, translating fundamental science into consumer-ready formulations.

The founder’s narrative also resonates with regional economic development goals. Newcastle has a history of industrial and scientific innovation; HexisLab’s presence at Newcastle Helix and the Biosphere underscores how the North East is cultivating a cluster of health and beauty technology activity, linking entrepreneurship to university-led research.

How HexisLab’s AI platform models ingredient behavior

HexisLab’s core capability centers on a proprietary AI-driven research platform designed to predict how ingredients and formulations will interact with biological systems before any physical lab testing. That predictive layer performs several functions that materially alter the development pipeline.

Chemical-to-biological mapping: The platform uses computational models to infer how molecules are likely to interact with skin and hair tissues. Techniques drawn from cheminformatics—such as chemical fingerprinting and Quantitative Structure-Activity Relationship (QSAR) models—allow the system to estimate properties like permeability, irritation potential, and likely metabolic transformations.
Formulation-level simulation: Beyond single-ingredient predictions, HexisLab’s system evaluates formulation matrices to anticipate ingredient compatibility, stability, and sensorial outcomes. Simulating how emulsifiers, active compounds and preservatives interact can drastically reduce the number of physical formulations that need to be made and tested.
Safety and ADMET-like screening: In silico screening methods provide early warnings about toxicity, sensitisation risk, or systemic absorption profiles, streamlining the safety assessment required by regulators in multiple jurisdictions.
Iterative learning: The platform is not static. When laboratory validation returns results that differ from simulation, those discrepancies feed back into the model as corrective training data, improving future predictive accuracy.

These capabilities mirror broader advances in in silico toxicology and cheminformatics that have matured across pharmaceutical and chemical industries. In cosmetics, such predictive tools accelerate hypothesis-driven experimentation. They also facilitate compliance with legislative frameworks that require robust safety dossiers, because companies can present preclinical evidence grounded in established scientific models alongside laboratory results.

Bridging digital simulation and the lab: HexisLab’s workflow

HexisLab operates on a clear sequence: simulate outcomes digitally, select the most promising formulation candidates, then validate those in the laboratory. This workflow yields benefits at each stage.

Hypothesis generation: The AI platform creates ranked lists of candidate active ingredients and formulations tailored to specific performance goals—moisturisation, frizz control, scalp health, or anti-oxidant protection. That reduces early-stage scatter and focuses chemists on a smaller, higher-probability set.
Resource prioritisation: By narrowing the field of physical experiments, HexisLab cuts reagent consumption, bench time, and sample waste—important for small teams and for brands committed to sustainability.
Pre-emptive compliance: Digital safety screens flag ingredients that could complicate regulatory approval in target markets, enabling formulation alternatives to be selected before costly lab batches are made.
Feedback-driven model refinement: Laboratory assays—ranging from physicochemical stability tests to cell-based irritation assays—either confirm or challenge the model’s predictions. Discrepancies become training data, improving future simulations.

This sort of hybrid model is the pragmatic response to one simple truth: computational prediction reduces uncertainty but cannot entirely replace empirical testing. The laboratory remains the final arbiter of safety and efficacy. HexisLab’s method acknowledges that balance, using AI to structure and accelerate experimentation while retaining rigorous validation to meet international standards.

Regulatory compliance and safety: navigating international standards

Cosmetic regulation varies by jurisdiction, but all major markets require that products be proven safe for human use. HexisLab’s clients span the UK, the United States, Canada, China and India—markets with distinct regulatory expectations—which makes a robust safety strategy essential.

European and UK frameworks: In the EU, Regulation (EC) No 1223/2009 sets strict pre-market safety assessment requirements. The UK’s post-Brexit cosmetics regulation largely mirrors these obligations. Both systems require a safety dossier with toxicological profiles, stability data, and a responsible person for market placement. In silico predictions complement but cannot replace required laboratory and literature-based safety data; they can, however, make assembling those dossiers more efficient by identifying potential red flags earlier.
North America: The U.S. Food and Drug Administration (FDA) regulates cosmetics less prescriptively than pharmaceuticals; responsibility for product safety rests primarily with manufacturers. In Canada, Health Canada reviews product safety with its own requirements. Predictive models help companies demonstrate due diligence wherever regulatory scrutiny may arise.
Emerging markets: China historically had stricter requirements, including animals testing for imported cosmetics; recent regulatory shifts have eased some barriers for ‘non-special use’ cosmetics and allowed more reliance on non-animal methods. India’s regulatory framework is still evolving and places emphasis on ingredient safety and labeling. HexisLab’s early identification of safety liabilities helps brands adapt formulations to comply with or enter these markets.

HexisLab’s AI approach explicitly supports regulatory needs by producing pre-validation evidence: predicted ADMET-like properties, compatibility analyses, and stability projections. When these virtual assessments align with lab results, they strengthen the safety narrative brands must present to regulators and consumers. When they diverge, the iterative loop ensures the model improves and companies avoid costly post-market issues.

Layla & Kays: turning laboratory science into certified consumer products

HexisLab is not only a consultancy. The company develops consumer-facing products, notably Layla & Kays, a textured hair shampoo and conditioner range. The line has received national recognition and carries vegan, cruelty-free and sulphate-free certifications—claims that carry weight with today’s consumers but also require verification.

A few features of the Layla & Kays story illuminate HexisLab’s modus operandi.

Science-led formulation: The product line benefits from the consultancy’s ability to select actives and excipients based on predicted performance in textured hair matrices. That kind of targeted formulation tends to yield better consumer outcomes than generic off-the-shelf approaches.
Certification and transparency: Vegan, cruelty-free and sulphate-free labels meet growing demand for ethical and scalp-friendly products. Certification is not merely marketing; it requires documented supply chain declarations, compliance with animal-testing policies, and ingredient transparency.
National recognition from small-scale R&D: A company with fewer than ten staff leveraged scientific credibility to build a recognized brand—illustrating how technical depth can substitute for scale in competitive consumer categories.

Layla & Kays exemplifies a virtuous cycle: the consultancy applies rigorous methods to create a product that showcases those same methods. That dual role—service provider and product owner—also offers real-world validation of the predictive platform’s effectiveness. Resonant product performance helps win new consultancy clients; consultancy insights inform product refinement.

How HexisLab’s model reduces development time and waste

Traditional product development often cycles through dozens of physical prototypes. Each iteration consumes raw materials, lab time, stability chambers, packaging samples and sensory panels. HexisLab shifts much of the early iteration into the digital space.

Fewer physical prototypes: By shortlisting top candidates through simulation, teams can focus resources on validating a smaller set of formulations.
Reduced raw material waste: Ingredients that never make it past the simulation stage are not purchased or used, lowering costs and environmental footprint.
Faster go-to-market: Shorter cycles between idea and validated formula shorten the time-to-market, critical for brands competing on trend responsiveness.
Ethical gains: In silico screening reduces the need for animal testing, aligning product development with ethical standards and regulatory movements that restrict animal testing.

Global beauty conglomerates have invested in similar strategies. L’Oréal’s acquisition of ModiFace and investments in AI-enabled platforms show that digital proof-of-concept combined with lab validation accelerates product iterations and improves personalization. HexisLab offers those benefits at a boutique scale, a model that suits smaller brands and startups.

Partnerships with universities and talent pipeline in the North East

HexisLab operates with fewer than ten staff, but it magnifies capacity through collaborations with regional universities and postgraduate researchers. That network serves multiple purposes.

Access to specialized expertise: University labs provide technical capabilities—advanced imaging, cell culture assays, analytical chemistry—that a small consultancy might not fully maintain in-house.
Talent development: Working with postgraduates fosters a pipeline of skilled researchers who can move into industry roles, supporting local workforce development.
Research collaboration: Joint projects can explore novel actives or delivery systems, producing publishable science or proprietary formulations.
Ecosystem building: University partnerships reinforce Newcastle’s credibility as a cluster for health and beauty innovation, attracting investment and further collaboration.

The Newcastle Helix innovation district, where HexisLab is based, intentionally connects academic institutions, startups and civic infrastructure. This spatial concentration of knowledge resources makes it easier for a small firm to access capabilities that would otherwise require substantial capital investment.

Newcastle as a credible node for beauty science and consumer brands

HexisLab’s presence highlights how innovation no longer concentrates solely in traditional beauty capitals like Paris, New York or Tokyo. Regional ecosystems that couple academic excellence with dedicated innovation spaces can produce globally competitive science-led brands.

Regional identity and global reach: HexisLab, rooted in Newcastle, serves clients in the UK, United States, Canada, China and India. That geographic span demonstrates that headquarters location no longer constrains scientific credibility or market access.
Historical continuity: Newcastle’s legacy industries—brewing, shipbuilding, and later, scientific research—create a cultural alignment with industrial expertise and product development. HexisLab’s reference to local achievements such as Newcastle Brown Ale underscores civic pride in translating lab work into consumer products.
Economic development: Small consultancies that export expertise and products contribute to regional GDP, create high-value jobs, and attract complementary services such as contract manufacturing, testing labs and packaging suppliers.

HexisLab’s trajectory suggests that health and beauty sectors can flourish in second-tier cities when anchored by research talent and innovation infrastructures like the Biosphere at Newcastle Helix. The model is replicable: the right mix of public-private collaboration, academic partnership and entrepreneurship can create new industrial clusters.

Global clients, global challenges: operating across jurisdictions

Serving clients across five major markets brings both opportunity and complexity. HexisLab’s client list spans the UK, the United States, Canada, China and India. Each market imposes distinct technical, regulatory and commercial demands.

Ingredient lists and banned substances: Regulatory agencies maintain different lists of prohibited or restricted ingredients. Predictive modeling helps identify candidates that will pass muster in target markets.
Labeling and claims substantiation: Marketing claims—“clinically proven,” “reduces frizz,” “improves hydration”—require different levels of evidence depending on jurisdiction and the exact wording. HexisLab’s capability to produce coherent proof-of-performance data assists legal and marketing teams in making defensible claims.
Manufacturing and supply chain considerations: Sourcing ingredients that meet certification standards (vegan, cruelty-free) across supply chains that span continents demands diligence. Small consultancies that understand global supply dynamics can guide brands to compliant suppliers.
Market-specific validation: Certain markets insist on specific types of testing or local approvals. Anticipating these needs during formulation prevents last-minute reformulation.

Successful international consulting requires not only scientific skill but also regulatory savvy and supply-chain literacy—competencies HexisLab appears to combine with its predictive platform.

Sustainability, ethics and consumer trust

Consumers increasingly evaluate beauty products by sustainability credentials and ethical claims. HexisLab’s emphasis on reducing waste, issuing cruelty-free and vegan-certified products, and minimizing unnecessary testing aligns with these values.

Environmental footprint: Reducing the number of physical experiments cuts chemical waste and packaging use. That matters both economically and environmentally. It also resonates with corporate sustainability commitments increasingly demanded by retailers and investors.
Animal testing: The cosmetics industry has moved decisively away from animal testing in many jurisdictions. In silico methods and advanced in vitro assays provide viable alternatives. HexisLab’s predictive platform supports this shift by identifying safety concerns before any biological assays are necessary.
Transparency and traceability: Certifications such as “vegan” and “cruelty-free” require traceable supply chains. Consumers and regulators expect documentation. HexisLab’s science-based approach enhances transparency by combining traceable ingredient selection with verifiable test results.
Trust through evidence: Consumers ask whether an ingredient really works and whether it is right for their skin or hair. Brands that can present defensible scientific validation—not just marketing language—build longer-term credibility.

Real-world examples confirm that brands emphasizing transparency perform well in niche and mass markets. The success of smaller, science-first brands suggests that rigorous testing and clear communication are commercial assets as well as ethical obligations.

Limitations and challenges of relying on AI in cosmetics R&D

AI offers clear advantages, but it also has limits. HexisLab acknowledges that simulation is a predictive tool rather than a replacement for empirical science. Specific challenges include:

Data quality and representativeness: Predictive models are only as good as the data used to train them. Historical datasets may under-represent diverse skin types, hair types, or ethnicities, producing biased or incomplete predictions.
Biological complexity: Skin and hair are complex biological systems influenced by genetic, environmental and lifestyle variables. Models can approximate but may not capture the full spectrum of real-world variation.
Regulatory acceptance: While regulators increasingly accept non-animal methods and in silico data as part of safety dossiers, most frameworks still require empirical validation. Models support but cannot substitute legally mandated evidence in many cases.
Generalization across chemistries: Novel actives with limited precedent present a problem for models that rely on historical analogues. For truly novel chemistries, physical testing remains essential.
Interpretability and transparency: Complex machine learning models can behave like black boxes. For regulators and safety assessors who demand clear mechanistic rationale, opaque models require careful contextualisation and supplementary data.

Addressing these limitations requires a mixed-methods strategy: combine diverse, high-quality datasets; use interpretable modeling approaches where possible; maintain robust laboratory pipelines; and continuously validate and refine models as new data become available.

How boutiques like HexisLab complement big-tech and corporate R&D

Large beauty corporations have invested heavily in AI, digital imaging and personalized beauty platforms. Examples include L’Oréal’s acquisition of ModiFace and broader investments in AI-driven personalization, and other major firms integrating data science into product development. HexisLab’s role differs and adds value in several ways.

Niche specialization: Small consultancies can offer bespoke attention, deeper technical partnership, and greater agility than big corporate labs configured for scale.
Integration with local ecosystems: HexisLab’s university partnerships and regional embeddedness provide access to specialized research while supporting local economic development.
Complementary capabilities: Large firms often commandeer massive datasets and in-house manufacturing. HexisLab’s strength is targeted scientific expertise and the ability to work across a wide range of client sizes and geographies.
Proof-of-concept incubation: Boutique labs can act as incubators for innovations that scale: a validated formulation developed by a consultancy can be licensed or acquired by larger players.

This complementary role enables a healthy innovation ecosystem: corporate R&D can scale technologies that boutique firms prove, and boutique firms can commercialize science that large firms might overlook.

Talent, scaling and the future for HexisLab and regional beauty hubs

HexisLab’s current team is small, yet the company manages cross-continental work and consumer product development. Scaling a specialized operation like this presents clear strategic choices.

Recruiting specialized talent: Attracting chemists with formulation experience, data scientists with experience in cheminformatics, and regulatory experts is essential for growth. Partnerships with universities provide an early pipeline.
Infrastructure investment: Scaling may require in-house analytical equipment, stability chambers and larger lab space, or longer-term contracts with contract research organizations.
Protecting IP: As the business grows, safeguarding proprietary models, formulation IP and client confidentiality becomes more important.
Expanding service offerings: HexisLab could broaden into adjacent services—clinical testing coordination, personalized formulation platforms, or manufacturing partnerships—to increase revenue streams.
International presence: With clients on multiple continents, strategic alliances or local representatives in key markets could streamline regulatory navigation and client management.

Scaling without diluting technical excellence is a common challenge for specialized consultancies. Judicious investments in talent and infrastructure, balanced with strategic partnerships, will determine whether HexisLab remains a boutique innovator or becomes a larger industry player.

Practical takeaways for brands and entrepreneurs

For founders and brands evaluating AI-guided R&D or seeking a consultancy, HexisLab’s model suggests several practical guidelines.

Start with a hypothesis: Use predictive tools to generate ranked candidates that reflect clear, measurable performance targets.
Combine models and lab assays: Treat in silico predictions as a means to reduce experimental space, not as a replacement for validation.
Plan for regulatory markets early: Identify target geographies and regulatory constraints at the concept stage to avoid last-minute reformulation.
Vet data sources: Ensure predictive models are trained on diverse, high-quality datasets. If diversity is absent, plan additional testing for under-represented populations.
Make ethics and sustainability visible: Consumers reward brands that demonstrate reduced waste, ethical sourcing, and transparent certification.
Consider small consultancies: Boutique firms can provide specialized expertise and flexible engagement models that larger R&D groups may not offer.

These steps help brands use advanced methods prudently, improving speed to market while reducing risk.

Real-world examples and industry parallels

HexisLab’s approach aligns with broader industry trends where AI augments, but does not replace, empirical testing.

L’Oréal and ModiFace: L’Oréal’s integration of ModiFace for augmented-reality and AI-driven personalization demonstrates how large corporations use digital tools for consumer engagement and product ideation.
In silico toxicology adoption: Regulatory acceptance of QSAR and other in silico methods in safety dossiers has grown, allowing companies to reduce animal testing and accelerate assessments.
Startups leveraging AI: Several startups use AI to design peptides, select natural actives, or predict skin responses. Success often depends on rigorous laboratory validation and clear pathways to certification and manufacturing.

The industry shows a pattern: rapid digitalisation at the front end of R&D, followed by established laboratory pipelines that verify and refine computational leads. HexisLab fits this model, offering evidence that smaller players can operate effectively within it.

Challenges ahead: data, diversity and market expectations

As predictive platforms become more common, three interrelated challenges will define the sector’s ability to deliver equitable and credible outcomes.

Data diversity: Models trained primarily on data from limited populations risk producing less accurate predictions for under-represented groups. Addressing this requires deliberate collection of diverse datasets and inclusion of biological variability in model development.
Interpretability and trust: As regulators and consumers demand transparency, firms must make models explainable and link predictions to mechanistic evidence whenever possible.
Managing consumer expectations: Marketing claims grounded in AI outputs must be tempered with validated performance data. Overstating digital predictions risks reputational damage if real-world results diverge.

These challenges are surmountable but require investment, ethical commitment, and cross-sector collaboration between companies, academia and regulators.

What comes next for HexisLab and the sector

HexisLab signals that advanced, science-led beauty R&D no longer requires a global corporate footprint. The firm’s AI-driven simulation, coupled with empirical validation and a consumer product line, exemplifies a flexible model workable by other regional players.

Potential next steps for HexisLab and similar consultancies include:

Deepening data partnerships to enrich predictive models and tackle under-representation in training sets.
Expanding the product portfolio under the Layla & Kays brand to demonstrate broader application of the platform across hair and scalp conditions.
Scaling validation capacity through partnerships with contract labs or in-house investment to handle more concurrent projects.
Increasing regulatory consultancy services to help clients navigate cross-border compliance.
Exploring B2B licensing of predictive models or formulation libraries to larger brands seeking rapid ideation.

For the broader industry, expect incremental integration of AI with laboratory science, stronger ethical standards for data and testing, and continued decentralisation of R&D to regional innovation hubs. Newcastle’s example shows that credibility and impact depend on scientific rigor, not geographic prestige.

FAQ

Q: What exactly does HexisLab do? A: HexisLab is a boutique beauty research consultancy that uses a proprietary AI-driven platform to predict how ingredients and formulations will behave in biological systems. The consultancy then validates promising candidates through laboratory testing, helping brands develop safe and high-performance skincare and haircare products that meet international regulatory standards.

Q: Who founded HexisLab and where is it based? A: HexisLab was founded by Dr Olusula Idowu (Sola) and operates from the Biosphere at the Newcastle Helix innovation district in the North East of England.

Q: How does HexisLab’s AI platform help product development? A: The platform simulates ingredient interactions, predicts properties such as stability and irritation potential, and ranks promising formulations. This reduces the number of physical prototypes needed, cuts development time, minimizes material waste and provides early safety and performance indicators that aid regulatory compliance.

Q: Can AI replace laboratory testing in cosmetics? A: No. AI is a predictive tool that reduces uncertainty and focuses lab work on the highest-probability candidates. Regulatory frameworks and biological complexity mean laboratory validation remains essential. HexisLab uses AI to shortlist candidates and then performs laboratory assays to confirm safety and performance.

Q: What markets does HexisLab serve? A: HexisLab works with clients across the UK, the United States, Canada, China and India, advising on formulation, safety, and validation for products intended for these jurisdictions.

Q: Does HexisLab sell consumer products? A: Yes. Alongside consultancy services, HexisLab develops consumer-facing products. One example is Layla & Kays, a textured hair shampoo and conditioner range that holds vegan, cruelty-free and sulphate-free certification.

Q: How does HexisLab address sustainability and ethical concerns? A: HexisLab reduces experimental waste by using simulations to limit unnecessary physical testing, supports non-animal predictive methods, and designs products that meet certifications such as vegan and cruelty-free. These practices help reduce environmental footprint and meet ethical consumer expectations.

Q: What are the limitations of AI in cosmetic R&D? A: Limitations include dependence on high-quality, representative training data; incomplete capture of biological complexity; variable regulatory acceptance; and potential interpretability issues in complex models. These limitations make lab validation and continuous model refinement necessary.

Q: How does HexisLab collaborate with academic institutions? A: The firm works closely with regional universities and postgraduate researchers on advanced ingredient science, leveraging academic capabilities for specialized assays, talent development and research collaborations.

Q: How can a small brand benefit from HexisLab’s services? A: Small brands gain targeted scientific expertise, faster development cycles, reduced material waste, assistance with regulatory compliance, and access to rigorous validation that supports credible product claims.

Q: Is Newcastle a growing hub for beauty and health innovation? A: Yes. Newcastle Helix and the Biosphere are part of a regional innovation ecosystem that connects university research with business incubation. HexisLab’s presence contributes to the North East’s reputation for science-led consumer product development.

Q: How does HexisLab keep improving its predictive models? A: Discrepancies between predicted and observed lab results are used as training data to refine the models. This iterative loop—predict, test, refine—enhances accuracy over time.

Q: Where can I learn more about the founder’s journey and HexisLab? A: The founder’s interview and more in-depth profiles are available in regional publications, including an interview with Colin Young in the sister publication mentioned by HexisLab. For direct enquiries, contacting HexisLab through their official channels will provide the most current information and engagement options.