What does Capiva do?
It pairs business strategy with technical execution to ship AI that produces measurable results. Both live in the same team: there is no handoff from a firm that designs to a shop that builds.
Boutique is a choice, not a stage. The founder leads every engagement directly, and that is what makes the shorter cycle possible: fewer layers between the decision and the person doing the work. Every project runs under contract and under a non-disclosure agreement.
How did Capiva start?
Capiva started in 2025 with a simple thesis: the risk in an AI project is no longer in building, it is in taking too long. The traditional model (committee, RFP, three months of discovery and three more of implementation) was designed for a time when technology was expensive and slow. With generative AI, that math flipped.
The answer was an operating model of its own, the AI-first methodology: a dedicated resource running short cycles of discovery, prototype, validation and deploy, measured in weeks.
In parallel, Capiva started working on optimization for AI engines (GEO). More than a year of that work became an open tool, the GEO Audit. And the harness Capiva uses every day became capivaOS, published as open source under the MIT license.
How does Capiva work?
In four phases, each ending in an explicit decision gate, so no phase gets funded by inertia. You enter where it makes sense; the detail is in how we work.
- Strategic Diagnosis (3 to 8 weeks)
- Maps where AI creates measurable value in the operation: stakeholder interviews, data analysis, and a roadmap prioritised by return.
- Technical Validation (4 to 16 weeks)
- A proof of concept on the client’s real data, in the real infrastructure, plus the architecture needed to reach production.
- Innovation Sprint (3 to 7 days)
- A working prototype from a specific problem, with a go/no-go decision grounded in data. Details in the Innovation Sprint.
- Evolution (ongoing)
- Ongoing strategic reference: new opportunities, team education, and AI investment advisory.
Who is on the team?
Bruno Américo is Capiva’s founder and Chief AI Architect. He works at the intersection of business strategy and hands-on AI execution, focused on AI strategy, data science, data engineering and process automation, and leads every client engagement directly.
Capiva's method comes from running AI harnesses in production every day. Capiva's own operation runs on one, with agents, memory, skills and automations. The article AI harness in practice shows how that harness works inside.
What proof does Capiva publish?
Projects are covered by non-disclosure agreements, so the cases describe Capiva’s own work or the pattern applied, without identifying clients. What is published:
- AI-first methodology
- An operating model with a dedicated resource and short cycles that takes AI projects live in weeks, not months. Read the operating model.
- GEO Audit
- A tool that scores a site on 6 dimensions of AI visibility. Applied to capiva.tech itself, it took Schema to 77/100, Technical Foundations to 97/100 and Platforms to 89/100. Read the case.
- capivaOS
- An open-source (MIT) harness that enforces spec, plan, implementation and verification phases on Claude Code. Used by Capiva itself. See capivaOS.
How do you reach Capiva?
The conversation starts wherever you already have a question. If the problem does not have a name yet, the diagnosis is the entry point. If you want to see the reasoning first, the blog has practical guides on harnesses, agents and AI visibility.