Technical review and implementation support
Map one workflow, identify control points, and build the first usable path.
Company thesis
Agents create operational uncertainty.
AI agents are becoming part of how companies analyze, decide, coordinate, and execute work. Production systems need more than logs, evals, dashboards, IAM, and policy documents.
01 Need
What is the agent actually doing, what does it mean, and how do we know it is safe enough to scale?
Existing controls answer parts of the question. They do not fully explain the meaning of agentic work across tools, humans, documents, and systems.
production need recordable
- 01 discover behavior
- 02 map access
- 03 define meaning
- 04 attach evidence
- 05 model coordination
- 06 design controls
- 07 record decisions
02 Why
Agent behavior needs explicit meaning before it can be trusted.
That conviction becomes practical work: behavior reviews, meaning models, evidence models, coordination maps, commitment models, control architecture, and runtime prototypes.
behavior reviews meaning models evidence models coordination maps commitment models control architecture runtime prototypes
03 Founder
Founder Approach
Built by an engineer who has shipped complex systems where reliability, clarity, and trust matter.
Semantiv was founded by Ariel Azoulay, an engineering leader focused on making complex AI systems observable, composable, and reliable enough for production use.
Ariel has led enterprise analytics modernization at Moody's Analytics, delivered production platforms for Spotify and McKinsey through ThoughtWorks, built real-time media and data-visualization systems at Nokia and Platora, and worked on recommendation systems and applied NLP at Outbrain and Carnegie Mellon.
That background shapes Semantiv's current focus: understand what autonomous systems actually do, define what their actions mean operationally, bind important work to evidence, and design controls that help teams scale agentic systems with confidence.
Founder signal Operator-led
- enterprise Analytics platforms, reporting systems, regulatory-facing workflows.
- delivery Spotify and McKinsey production work through ThoughtWorks.
- systems Real-time media, data visualization, recommendations, applied NLP.
- current Agent behavior, evidence, control points, and decision records.
04 Market entry
Custom technical reviews first. Product primitives behind the scenes.
The market is early and agent environments vary widely. Each review creates immediate client value, design-partner learning, reusable runtime primitives, implementation opportunities, and product direction grounded in real workflows.
Runtime infrastructure
Evidence, decision records, coordination primitives, and domain packs for accountable autonomous work.
Next step
The category is agent behavior and control.
Semantiv helps teams make autonomous work understandable, evidenced, coordinated, and controlled before deployment scales.