Vector1 Announces Kamaji: Research as a Service(RaaS) for the AI Era

What happens when agentic AI handles the mechanical work of research — and humans focus on what matters?

The Research Problem Every Organization Faces

There’s a strange bottleneck in modern business: research doesn’t scale.

Marketing scales. Sales scales. Engineering scales. But research — the work of understanding markets, validating ideas, synthesizing knowledge, and turning insight into action — remains stubbornly artisanal.

Hiring a researcher takes six months. Building a team takes years. And even well-funded R&D departments can only pursue a fraction of the questions that would create value for their organization.

The result? Most research never happens. Not because the questions aren’t worth asking, but because there’s no capacity to answer them.

Vector1 built Kamaji to change this.

What Is Kamaji?

How It Works

Kamaji operates as a pipeline of specialized AI agents, each optimized for a distinct phase of the research process. Discovery agents surface and evaluate sources. Analysis agents synthesize evidence and identify patterns. Composition agents transform findings into clear, rigorous prose.

Each phase includes quality gates before work advances — the same checkpoint discipline you’d find in a well-run research team, automated and consistent.

A human Chief Scientist provides strategic direction, makes judgment calls on ambiguous findings, and ensures the final output meets publication standards.

Here’s what Kamaji does:

Discovery — Systematic literature review, trend monitoring, and source aggregation across academic databases, industry publications, and real-time information.

Analysis — Data synthesis, pattern recognition, and validation. When quantitative work is needed, the system generates visualizations and stress-tests conclusions.

Composition — Clear, rigorous writing optimized for the target audience. Reports, briefs, white papers — whatever the project requires.

Application — Translation of findings into actionable outputs: strategic recommendations, implementation guides, decision frameworks.

A human Chief Scientist provides inspiration, direction, makes judgment calls, and ensures quality. The system handles execution.

Why This Matters: The Case for Research as a Service

The economics of traditional research are punishing.

A senior researcher costs $150,000–300,000 annually. A functional research team requires multiple specialists and 12–18 months before reaching full productivity. Most organizations simply can’t justify this investment for the questions they need answered.

Kamaji operates at a fraction of traditional cost, with turnaround measured in days rather than months.

This is the premise behind Research as a Service (RaaS): on-demand research capacity that scales with your needs, not your headcount.

This isn’t about replacing researchers. It’s about enabling research that would otherwise never happen.

The mid-sized company that couldn’t justify a competitive intelligence function? Now feasible.

The startup that needed a technical feasibility study before committing engineering resources? Done in a week.

The research institution where skilled researchers spend months on literature review? Dramatically accelerated.

The executive team that needs synthesized analysis before a strategic decision? Available on demand.

How to Work With Kamaji

Vector1 is offering Kamaji through three models:

Project-Based Research — Submit a research brief, receive rigorous deliverables. Engagements scoped and priced based on complexity and timeline.

Platform Access — For organizations building internal AI research capacity. Includes the Kamaji framework, integration support, and ongoing development.

Research Partnership — Embedded engagement for organizations with significant, ongoing research needs. Kamaji operates as a dedicated research function integrated with your team.

Proof of Concept

Before launching Kamaji publicly, we used it to investigate a question relevant to our own domain:

Why hasn’t AI investment translated to productivity gains — and when will it?

The system produced a comprehensive analysis synthesizing academic research, economic data, and historical parallels. The full report drops later this week.

Spoiler: The answer is more nuanced than either the hype or the skepticism suggests. And it has significant implications for how organizations should think about AI investment.

What’s Next

Kamaji represents Vector1’s thesis on the future of knowledge work: small human teams amplified by purpose-built AI systems, operating at scales previously impossible.

We’re starting with research because it’s high-value, well-defined, and dramatically underserved. The model extends anywhere that skilled knowledge work follows repeatable patterns.

We’re selectively taking on pilot engagements as we scale.

If you have a research challenge that’s been sitting on the backburner — competitive intelligence, technical feasibility, market analysis, literature synthesis — we’d like to hear about it.

Contact: brian at vector1.ai

Kamaji is a Vector1 initiative. Vector1 builds AI systems that amplify human capability in knowledge-intensive domains.

About Vector1

Vector1 is an applied research studio at the intersection of AI, econometrics, and complex systems. We build intelligence systems that surface the structural forces driving performance — across markets, content ecosystems, and real-world economies.

Brian Curry is Vector1’s founder and Kamaji’s Chief Scientist. Based in Kansas City, he specializes in causal inference, simulation-driven AI, and economic systems modeling. He is the creator of Papilon (open-source complex system simulation) and Daedalus (economic causal analysis platform).

Kamaji is Vector1’s first productized research system — applying the same rigor we bring to economic modeling to the challenge of scalable, AI-native research.

Contact: brian at vector1.ai | vector1.ai