GlassVera develops applied AI systems for regulated, knowledge-intensive industries — where precision, transparency, and domain depth are structural requirements, not product features.
Capability Intelligence Platform — structured scoring for executive roles in regulated industries
AI-driven risk and compliance intelligence, in active development as GlassVera's next vertical
Clinical and regulatory capability mapping, in development for life sciences organisations
Generic AI tools apply statistical pattern recognition across undifferentiated data. GlassVera builds systems that operate from structured knowledge — ontologies, capability maps, and domain-calibrated inference — designed for environments where decisions carry regulatory, financial, or human consequence.
Understanding what individuals and organisations can actually do — not what their documents say. GlassVera maps demonstrated capability through structured ontological analysis, not keyword extraction.
GlassVera's systems are built on structured knowledge representations of professional domains — named, calibrated, and machine-readable — enabling AI to reason across complex role taxonomies rather than match surface text.
Systems designed for environments where explainability, bounded adaptation, and auditability are not optional. GlassVera's architecture reflects the operational requirements of financial services, compliance, and life sciences.
VeraVanta is a capability intelligence platform for senior professionals in regulated industries. It extracts a structured capability profile from a candidate's career history, scores it against a curated ontology of executive dimensions, and surfaces ranked, explained matches against live job markets — with adaptive refinement over time.
Purpose-built for financial services, compliance, risk, and adjacent regulated sectors. Not a job board. Not an ATS. A decision-support system for the most consequential career and hiring decisions in complex organisations.
VeraVanta is GlassVera's first product. Alongside it, the company is developing applied AI work in financial technology and life sciences. These initiatives draw on the same foundational methods — structured knowledge, domain-calibrated inference, explainable reasoning — applied to adjacent problem spaces.
Applying GlassVera's structured-inference methods to financial data interpretation, market intelligence, and risk signal analysis. Early-stage. Selective disclosure available to qualified partners.
Extending GlassVera's ontological and capability-intelligence methods into life sciences and biomedical domains. Research-stage engagement with regulated healthcare and pharmaceutical contexts.
GlassVera systems are built around structured knowledge of specific professional domains. The intelligence begins in the ontology, not in pattern matching across undifferentiated training data.
Every output carries a dimensional breakdown. Users and organisations can see why a score is what it is — which matters in regulated environments where unexplained AI decisions carry legal and compliance risk.
GlassVera's systems learn from feedback within administrator-defined limits. Adaptation is real; so is control. The model improves without drifting into behaviour that cannot be audited or reproduced.
Multi-tenant deployment, per-organisation branding, role-based governance, versioned AI prompts, and full cost and audit logging. Designed for institutional procurement, not retrofitted after the fact.
We work selectively with enterprise buyers, investors, and strategic partners who operate in regulated, knowledge-intensive industries.
Capability intelligence for senior professionals and the organisations that hire them — built for regulated industries where the cost of a misaligned placement extends well beyond a vacancy.
A Chief Compliance Officer candidate and a Chief Risk Officer candidate may have careers that look similar on paper. A keyword-matching ATS cannot distinguish them. A job board algorithm does not know what "regulatory remediation" means at an institutional scale.
The result is a market where senior placements depend on personal networks, expensive search firms, and educated guesses — in organisations where a wrong senior hire can cost multiples of the annual salary and trigger regulatory attention.
Four integrated capabilities, built to work together from day one.
Upload a resume in any common format. VeraVanta parses the career history and extracts a structured capability profile — not a keyword list, but a mapped representation of domain expertise, leadership scope, industry exposure, and executive function — scored against a calibrated ontology of professional dimensions.
VeraVanta scores every job against the candidate's capability profile across a proprietary set of calibrated executive dimensions. Each match carries a composite score and a full dimensional breakdown. The reasoning is visible; the score is not a black box.
As users engage with matches — accepting, saving, dismissing — the system incorporates those signals into the scoring model. Calibration is bounded and auditable; the model improves over time without introducing uncontrolled drift. Enterprise administrators control the parameters of adaptation.
Beyond scoring, VeraVanta synthesises a candidate's career signals into structured narrative dimensions — analytically derived, not manually written — forming the foundation of how a candidate's story is communicated to hiring organisations.
Upload a resume in any major format. The system parses structure — identity, work history, education, skills — and prepares it for ontological analysis.
AI inference maps the career history against GlassVera's executive ontology, producing scored capability dimensions specific to regulated professional environments.
Live job descriptions are scored against the capability profile. Each match shows dimension-level scores, not just a composite — providing the transparency to act with confidence.
User feedback continuously refines the model within defined bounds. The system learns which signals matter most for this candidate in this market.
Scores derive from a structured knowledge model of executive professional domains — not statistical inference from undifferentiated resume text. The ontology encodes what expertise actually means in regulated industries.
Every composite score is broken into calibrated capability dimensions with individual scores. A match at 88 is not an opinion — it is a statement about which capabilities align and by how much. This is auditable and defensible.
Blockers and preferences are expressed in plain English and interpreted semantically. "Non-regulated financial services" as a blocker is applied across thousands of job descriptions without manual tagging.
The matching model learns from user decisions within administrator-defined parameters. Learning is real; so is control. Adaptation is bounded, configurable, and auditable by design.
VeraVanta routes inference across leading AI infrastructure providers, with model selection configurable per deployment. Enterprise organisations can align the AI stack to their data residency and governance requirements.
Multi-tenant, per-organisation branding, role-based access for recruiters and hiring managers, and full cost and audit logging. Built for enterprise procurement — not retrofitted for it.
We are working with a select group of senior professionals and enterprise organisations ahead of general availability.
GlassVera's work extends beyond VeraVanta. The company is developing applied AI initiatives in financial technology and life sciences — drawing on the same foundational methods and regulated-industry orientation that define its flagship product.
GlassVera is applying its structured-inference methods to problems in financial data analysis, market intelligence, and risk signal interpretation — areas where the same challenge that exists in talent identification also exists in information processing: too much signal, too little structure, and too much at stake to rely on generic models.
This initiative is in active early development. The specific problem framing, data architecture, and product scope are being developed in parallel with VeraVanta's continued build-out. GlassVera is not making specific product claims at this stage.
Qualified partners in financial services, data infrastructure, or regulatory technology who wish to explore early collaboration are welcome to engage through the contact page.
Financial institutions operate with large volumes of structured and unstructured data — regulatory filings, market signals, risk reports, counterparty information — that are difficult to reason across at institutional scale with current tooling.
GlassVera's ontological methods and structured-inference architecture are directly applicable to financial domain data interpretation. This initiative explores where those methods create the highest-value outcomes in regulated financial contexts.
Early research and architecture phase. No public product. Selective partner discussions available by invitation.
Structured interpretation of regulatory filings, guidance documents, and compliance frameworks across jurisdictions.
Applying domain-calibrated inference to financial risk signals — counterparty, market, and operational — in ways that commodity models do not support.
Understanding how specific market structures, instruments, and participant behaviours relate to each other — at a level of precision beyond surface pattern recognition.
GlassVera is exploring how its core methods — structured knowledge representation, ontological reasoning, capability mapping, and explainable AI inference — translate to problems in life sciences and biomedical domains.
Biological systems and clinical environments share a structural characteristic with regulated financial environments: complexity is deep, the cost of error is high, and the decisions that matter most require domain-specific reasoning that general-purpose AI does not provide. GlassVera's methods are well-suited to these conditions.
This initiative is at an early exploratory stage. GlassVera is in active conversation with researchers and organisations operating at the intersection of AI and life sciences. No specific product has been defined.
Drug discovery, clinical trial design, genomic analysis, and regulatory submission all involve reasoning across highly structured domain knowledge — and the consequences of imprecision are measurable in human terms. This is precisely where GlassVera's methods apply.
Ontological methods developed for complex professional domains share structural properties with biological knowledge bases. GlassVera is evaluating where its architecture produces unique value in biomedical and regulatory contexts.
Early research and partner identification phase. Engaging with institutions and researchers working at the intersection of AI and regulated life sciences.
Applying ontological methods to clinical data, trial results, and medical literature to support AI reasoning across complex biomedical information.
Structured analysis of FDA, EMA, and other regulatory guidance to support drug development and medical device organisations navigating complex approval environments.
Extending VeraVanta's executive capability model into pharmaceutical, biotech, and medical device organisational contexts where regulated domain expertise is equally consequential.
GlassVera engages selectively with partners who bring domain depth, institutional credibility, and a genuine problem to solve.
GlassVera was founded by executives with deep experience in compliance technology, regulatory affairs, and AI systems in regulated financial services — people who spent years watching consequential decisions in complex organisations rely on instruments not designed for the problem.
The insight behind GlassVera did not come from observing regulated industries from the outside. It came from operating within them — from directing technology strategy and compliance execution in global financial institutions under active federal regulatory oversight, where the people making decisions at the senior level need to understand both the regulatory architecture and the technical systems beneath it.
The specific observation: the tools available for identifying, evaluating, and matching senior talent in these environments were built for a different kind of problem. Job boards optimise for clicks. ATS systems optimise for keyword coverage. Neither was designed to reason across the domain-specific expertise that makes the difference between a senior hire who understands the regulatory environment and one who only appears to.
GlassVera was founded to build the category of tool that should exist: applied AI systems that reason with domain structure, produce explainable outputs, and operate within the governance requirements of regulated enterprises.
The structure of the problem comes first. GlassVera builds knowledge representations of specific domains before applying AI — because a model that does not understand what it is reasoning about cannot produce results that practitioners trust.
Every output GlassVera produces can be explained at the dimensional level. In regulated industries, unexplained AI decisions carry legal and operational risk. We treat explainability as a design requirement, not a post-hoc addition.
It is more valuable to be provably right about the things that matter than to have an opinion about everything. GlassVera systems optimise for accuracy in their defined domains, not breadth across all possible inputs.
GlassVera builds as if it is already selling to a regulated enterprise with a procurement function, a legal team, and a compliance committee — because it is. Architecture, data handling, and governance are designed accordingly from day one.
Systems that learn are more useful than systems that do not. Systems that learn without limits are systems that cannot be trusted. GlassVera's adaptive mechanisms are bounded, configurable, and auditable by design.
GlassVera does not make claims it cannot support with documented evidence. The website, the product, and the company's public communications reflect what the technology actually does — no more, and with appropriate precision.
GlassVera's founders bring direct executive experience in compliance technology, risk systems, and regulatory affairs at scale — including leadership of global engineering organisations operating under active federal regulatory oversight, and deep expertise in financial crime, regulatory frameworks, and enterprise technology transformation.
This is not a company founded by technologists who decided to enter regulated industries. It is a company founded by regulated-industry executives who built the AI system that should have existed.
Early partnerships, advisor relationships, and enterprise conversations are welcome. We work with serious organisations and individuals.
GlassVera is accepting inquiries from enterprise buyers, investors, strategic partners, and advisors. We are not a mass-market product. Tell us who you are and what you are trying to accomplish.
GlassVera engages with organisations and individuals who operate in or near regulated, knowledge-intensive industries — and who have a genuine problem that applied AI could address with precision.
We review every inquiry carefully and will do our best to respond within 2–3 business days.