Your organisation reads itself. Intelligence delivered daily, no dashboards to check.
A passive intelligence layer in development. Today it delivers a governed morning brief in Microsoft Teams; email, calendar and HR sources, and signals such as delivery blockers and commitment drift, are roadmap.
StatusAI CompMate is in active development for its first enterprise client. The 33-agent, 9-layer design is a target, not what runs today; today is a governed morning brief in Microsoft Teams.
The signals are in the data. No one has time to read it all.
People signals sit in message sentiment, in meeting patterns, in engagement gaps. Delivery blockers surfaced in standups too late to prevent. Commitments made on calls and never followed up. Every growing organisation produces this data. Almost none of it reaches a decision-maker in time.
AI CompMate passively reads your organisation's existing data, no new tools, no new processes, no behaviour change required. It builds a live causal model of what is actually happening and delivers targeted intelligence to the right person at the right cadence. The Trust Ladder ensures it earns autonomy incrementally.
Leadership layer costs ₹2Cr/year. Signals discovered late. Commitments tracked in spreadsheets. Delivery blockers surfaced in standups, too late to prevent.
Today: a governed morning brief in Microsoft Teams. Roadmap: commitment tracking and wider stakeholder briefs, built up under Trust Ladder governance.
How we build it.
Every stage has a defined role and a quality gate before the next one runs, the same engineering spine under every Agentix system.
- 01
Data Audit
We catalogue every data source your organisation produces, Microsoft Teams today, with email, calendar, HR system and project tools on the roadmap. This defines what the system can see.
- 02
Schema & Ingestion
L0.5 CPE normalises every source into a unified schema. Normaliser → Segmenter → Extractor → Linker. Conversations become structured intelligence.
- 03
Agent Architecture
Target design: 33 agents across 7 tiers. Each agent has a defined scope. No agent handles everything. Orchestration routes work to the right tier.
- 04
Intelligence Streams
9 Passive Intelligence streams configured to your organisation's specific risk profile. Roadmap signals are tuned to your organisation's patterns before any is enabled.
- 05
Trust Ladder (Week 1-2)
Observer mode: system runs but all output reviewed by a human before delivery. Builds confidence in system judgement before any autonomy is granted.
- 06
Scale & Calibrate
Monthly calibration sessions. Intelligence streams tuned based on false positive rate and actionability scores. System improves with your organisation.
Delivered on
every engagement.
You own all of it, code, infrastructure and data, from day one. No licence fees, no hosted dependency.
- L0.5 Conversation Processing Engine, normalise, segment, extract, link across all message sources
- Target design: 33 agents across 7 tiers, Ingestion → Resolution → Analysis → Deep Analytics → Lifecycle → Delivery → Executor (not the state today)
- Target design: 9 Passive Intelligence streams, decisions, commitments, blockers, client health, and more (roadmap signals)
- 6 scheduled delivery agents, right intelligence to right person at right cadence
- Trust Ladder governance, Observer → Advisor → Assistant → Partner (earns autonomy incrementally)
- Designed to be self-hosted on K3s / Hetzner, for data sovereignty on your infrastructure
In development, not in production.
Open-source.
Self-hosted. Owned.
Built on components you can host, fork and extend. LiteLLM abstraction means you swap models without rewriting integrations.
Is this your
problem shape?
Describe what you're trying to do. We'll confirm whether this is the right system type, scope the build, and tell you what the eval harness looks like.