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Weak Signal: spotting opportunities before competitors, with L-Acoustics
A site for humans and for agents: what we learned building PHASEONE10841
MCP: the protocol, its limits, and what it needs around it
The shared foundation that lets every team build its agents
Agents that work inside your processes, under control
Giving every employee agents, without losing control
We don’t stop at chatbots. We build the systems AI runs on.
Multi-model
Mistral, OpenAI, Anthropic, open models — the right one for each constraint.
Multi-cloud
AWS, Azure, GCP, IBM or private: we build on what you already run.
Production-first
Identity, permissions, observability and governance from day one.
Agentic AI
AI governance plays out across three verticals.
An assistant that summarises a document and an agent that acts in an ERP don’t call for the same rules. We organise agentic AI in two verticals — end users and business teams — built on a common IT platform that governs every agent.
Public cloud | Sovereign cloud | On-premise | Air-gapped
01End users
User Augmentation
Agents for every employee, with their own rights.
What we put in place
- Choosing and integrating the AI front end
- Declarative agents
- Governed MCP connections
- Identity propagation
- Shared skills and prompts
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Light
The agent acts with the user’s own identity and rights; the person approves.
02Business teams
Business Applications
Agents that work inside your processes, under control.
What we put in place
- Main agent and subagents
- Talk to my data
- Decision support
- Control middleware
- Isolated runtimes
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Reinforced
Rights limited per agent, isolated runtimes, human approval before any write.
03IT & platform
AI Platform
The shared foundation that connects, secures and observes every agent.
What we put in place
- LLM, MCP and agent gateways
- Catalogue of agents, tools and skills
- Observability, evaluation and cost
- Standards and centre of excellence
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Strict
One control point for every team: identities, access, cost, traces and versions.