Your organisation reads itself. Intelligence delivered daily, no dashboards to check.
Passive intelligence layer that reads Teams messages, email, meetings, and HR data, and surfaces the signals leadership would otherwise miss: attrition risk, delivery blockers, commitment drift.
Status AI CompMate is in active development for its first enterprise client. The figures below are architecture facts and design targets, not a benchmarked production score.
The signals are in the data. No one has time to read it all.
Attrition visible three months before resignation, 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. Attrition discovered when someone resigns. Commitments tracked in spreadsheets. Delivery blockers surfaced in standups, too late to prevent.
~$300/month replaces the manual intelligence layer. ~60% of attrition caught early. Commitments tracked automatically. Daily brief to every stakeholder. CEO attention model running 24/7.
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, Teams, Slack, email, calendar, HR system, ElevateHQ, project tools. 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
33 agents deployed 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. Flight-risk weights tuned to your retention data. Delivery risk calibrated to your project patterns.
- 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
- 33 agents across 7 tiers, Ingestion → Resolution → Analysis → Deep Analytics → Lifecycle → Delivery → Executor
- 9 Passive Intelligence streams, decisions, commitments, blockers, flight-risk, client health, and more
- 6 scheduled delivery agents, right intelligence to right person at right cadence
- Trust Ladder governance, Observer → Advisor → Assistant → Partner (earns autonomy incrementally)
- Multi-tenant, self-hosted on K3s / Hetzner, full data sovereignty, your infrastructure
Not a demo. In active development.
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.