What we do
The sexy AI labs runs on demo culture. A polished prototype, a launch tweet and then the accountability conversation never happens. “Move fast and break things” is a defensible philosophy until the thing that breaks turns out to be your compliance posture, or the trust of your customers.
We build the other kind. Eigenvector is a research lab that operates at the frontier of unsexy AI. We research and build new AI models to power enterprise AI that has to be reliable, explainable, governed, and ready for the conditions that actually exist in large organizations with regulatory scrutiny and operational complexity. Predictability is a feature here, and control is a requirement.
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our AI FACTORY APPROACH
Analysis of 177 enterprise agentic AI deployments reveals that ad-hoc, project-based deployment models suffer from high failure rates (69%), extended time-to-value, and unsustainable governance overhead. The Agentification Factory model replaces this with a systematic and repeatable production pipeline — achieving a 65% success rate, 40–60% faster time-to-value, and a verified net ROI of 21% through the compounding ROI Flywheel effect.
We created a structured method for assessing automation potential and setting agentification targets that reflect what AI can actually deliver today. That means working with the real limitations of current AI, not vendor benchmarks.
In this whitepaper, we explore eight agentic governance models, providing a compre‐
hensive comparison across critical dimensions such as scalability, operational cost, auditability, and suitability for regulated environments.
By treating agent deployment as a continuous
production pipeline rather than a series of discrete projects, the factory model dramatically reduces the marginal cost and risk of each new deployment.
our Services
Ambition without structure is just a roadmap to regret. We translate AI vision into an executable operating model that covers governance, platform architecture, and delivery design, so that when scale arrives, it does not bring chaos with it.
Before anything gets automated, we map how work actually moves through your organization, where friction accumulates, and where agentic execution would create genuine leverage rather than cosmetic efficiency.
We design AI stacks with explainability, observability, access controls, data lineage, and auditability treated as structural requirements from the first line of architecture, not as features bolted on after an audit request arrives.
AI without lifecycle discipline is allowing technical debt to build up. We implement a full AI Software Development Lifecycle covering design, validation, deployment, monitoring, retraining, and retirement, because a model that nobody owns eventually becomes a model that nobody trusts.
We build the infrastructure for your process operators that measures real performance, bias, drift, control effectiveness, and business value against your data, your processes, and your definition of what good actually looks like.
We design or implement your control frameworks that embed accountability, traceability, human oversight, and policy enforcement into every stage of the AI lifecycle, covering model approvals, vendor risk, audit readiness, and regulatory alignment.
We secure enterprise AI through identity controls, data protection, continuous monitoring, red teaming, prompt injection defense, and access governance, built into operating models that are designed to hold under pressure.
We help organizations move from confused experimentation to AI that people trust, use, and benefit from, without dismantling everything that made the organization functional in the first place.
our research
We conduct fundamental research into agentic AI at enterprise scale. We analyse success stories, create frameworks that hold up under pressure, and we build AI-models for enterprise AI that holds up under scrutiny. Our research is published in journals and preprint servers for the community to use.
We created a structured method for assessing automation potential and setting agentification targets that reflect what AI can actually deliver today. That means working with the real limitations of current AI, not vendor benchmarks.
The OCG framework connects operating cadence, governance discipline, and delivery execution into a single enterprise model. Built for organizations that need clear AI ownership, decision velocity, and real delivery rhythm rather than accountability structures that exist only in org charts.
Most AI programs measure activity. We measure value. The Roundtrip Value model links investment, adoption, operations, and business outcomes into one closed loop, making visible where value is created, where it leaks, and what is actually worth scaling.
EIGENVECTOR BLOGS
Haven’t you ever wondered how an LLM is actually able to ‘think’? I’m not talking about the pipeline, the whole sequential thing of breaking a sentence into tokens, projecting those tokens into vector embeddings, running them through multi-head attention layers and feed-forward blocks until a probability distribution over the next token falls out the other…
I write about agentic AI so often that my Weiner (dog), Slob, has developed what I can only describe as a professional skepticism toward my keyboard. Every time I sit down to produce another piece on autonomous agents, orchestration layers, or the seventeen-layer governance stack no one is actually going to build, he looks at…
books
ENTERPRISE AI GOVERNANCE
The definitive CMM-style reference for governing non-deterministic, agentic, and autonomous AI systems in the large enterprise. Grounded in NIST AI RMF, ISO 42001, and the EU AI Act.
Henk
Chief Architect
AGENTIC ECONOMICS
The complete guide to autonomous agent deployment, value measurement and economic governance of your AI program. You will learn how to design, build, operate, and measure the economic value of enterprise autonomous agent
systems from first principles to full-scale deployment.
James
Customer Success
AGENTIC IMPLEMENTATION
A full manual for implementing enterprise scale agentic AI for governed deployments, including architecture and evidence factory for agentic governance.
Marco
AI Strategy & Research
An AI Factory is a repeatable operating model for designing, deploying, governing, and scaling AI products, automations, and agents across the enterprise.
Through explainability, telemetry, governance controls, lifecycle management, and evidence-based monitoring.
Yes. We analyze workflows, controls, bottlenecks, handoffs, and waste before introducing automation.
Just sign up and we’ll get back!
Both. We design strategy, architect systems, build platforms, automate workflows, and support scaling.
Yes. We are vendor-neutral and integrate AI into the environments where work already happens.
We use our Roundtrip Value model to connect investment, operations, adoption, and measurable outcomes.
With a discovery session focused on opportunities, risks, readiness, and fastest routes to value.
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Schedule a free consultation focused on opportunities, risks, readiness, and reliable routes to value
EIGENVECTOR 