Briefs for every rhythm
Retenna writes them from the record — never from imagination. Every line links back to its source, and each reader only ever sees what they're allowed to.
Monday plan
The week ahead, ranked: what changed, what needs a decision, what to watch.
Daily check-in
Two minutes each morning: what moved overnight, and the one or two calls to make today.
Friday wrap
What shipped, what slipped, what was saved — the honest end-of-week story, written without anyone chasing updates.
On demand
Someone asks "where are we on the launch?" — a sourced brief assembles itself in under a minute, ready to share.
Delivered to Slack, Teams, or email — on a schedule, or only when you say so.
From noticing to done
A "move" is a prepared piece of work: the message already written, the task already filled in, the meeting already scheduled, the instructions for your AI assistant already spelled out.
How every move runs
- Retenna spots something and prepares the move — with the reasoning and sources attached
- You see exactly what will happen: the actual message, the actual change
- You approve, edit, or dismiss — one click
- It runs, and a permanent entry is written: what ran, when, why, approved by whom
- Most moves can be undone — the record includes the way back
Anything sensitive — customer-facing messages, money, legal, security — always asks first, no matter what. Over time, for small routine things you choose, you can let Retenna handle them on its own within strict limits. It earns that, step by step.
How Retenna turns separate agents into a company that learns
AI models can already remember conversations, search connected systems, use tools, and complete multi-step work. Retenna supplies the organisational system around them: a sourced view of company intent and live reality, coordination across workers, consequence checks, outcome verification, and learning that changes future behaviour.
More than memory, search, or agent management
Memory can carry context and connectors can retrieve authorised information. Retenna continuously maintains what should be true — goals, decisions, promises, owners, policies, and expected outcomes — beside evidence of what is actually true. It detects where they diverge, what the gap affects, and whether it deserves action.
The right mission, not just more context
Before work begins, each person or agent gets the smallest sufficient brief: the objective, why it matters, current state, prior decisions, commitments, dependencies, authority, success criteria, and escalation rules. It knows not only the facts, but what outcome it is responsible for creating.
Problems found before anyone prompts
Retenna watches selected signals continuously. When a promise becomes unlikely to be met, work drifts from a decision, evidence invalidates an assumption, or an important dependency has no owner, it can assemble a bounded mission before someone notices and writes the perfect prompt.
One coordinated workforce
Retenna routes each part to the right person or model, sequences dependencies, preserves state between hand-offs, and prevents duplicated or conflicting work. The unit of coordination is the company outcome — not the chat or agent run.
Autonomy that is earned
Each agent develops a job-specific performance record: where it succeeds, fails, needs review, and produces the intended result. Verified performance can widen authority for that kind of work; errors can narrow it, strengthen checks, or route the next mission elsewhere.
Outcomes, not completed tasks
Sending the message or closing the ticket is not the finish line. Retenna watches for the expected state change. If risk did not fall, the customer did not respond, or the workaround failed, it reopens the loop instead of marking activity as success.
Learning that compounds at company level
Corrections, overrides, accepted decisions, failed interventions, and verified outcomes improve future detection, context, routing, checks, and autonomy across every authorised person and agent. A lesson from one job changes how the whole company system behaves next time.
Models remain replaceable. Your decision history, operating judgement, outcome evidence, and learnt patterns remain company-owned.