Stop wasting time and AI tokens.
The right context
for every task.
Not too little, not too much.
Missing context creates mistakes. Too much creates noise and cost, and can contribute to drift. Stale or wrong context causes rework.
AuzzurA DM — shared context layer for human + AI teams. It helps people and connected agents work with relevant, current, task-specific knowledge instead of repeatedly searching and re-explaining.
Workspace- Context InRight, current, permitted context
- Human + AI WorkPeople and agents do the task
- Agent ReportFindings come back as a proposal
- Review & ReusePeople approve; the next task reuses it
The next task starts with better context. Agents propose; people approve what becomes trusted. Fully automatic report ingestion is not claimed.
Your information may already exist. The challenge is knowing what matters now.
Good documentation does not tell a person or an agent which part applies to this task, whether it is still current, or whether they may use it. So the same things get searched for and re-explained, again.
- Too little
Missing context creates mistakes.
The work starts without the constraint, decision or exception the team already knew.
- Too much
Excess context creates noise and cost.
Long histories and irrelevant files bury what matters, burn tokens and can contribute to drift.
- Stale or wrong
Outdated context causes rework.
Superseded decisions and expired assumptions still look current, until someone has to redo the work.
The right context is relevant to the task, current, permitted for the person or agent using it, and already there, with no re-explaining.
Industry research on the problem space.
These are independent survey findings about searching for information and almost-right AI output. They are not results measured for DM, and they do not show cause.
- 25%
of their time is wasted by leaders and teams just searching for answers, Atlassian reports.
Survey of 12,000 knowledge workers and 200 executives.
Atlassian · State of Teams 2025 (opens in a new tab) - 66%
report frustration with AI solutions that are almost right, but not quite.
Multi-select question on AI-tool frustrations; share of respondents to that question.
Stack Overflow · Developer Survey 2025, AI (opens in a new tab) - 45%
report that debugging AI-generated code is more time-consuming.
Same multi-select question; reported as 45.2% of respondents.
Stack Overflow · Developer Survey 2025, AI (opens in a new tab)
Storing knowledge is one thing. Delivering the right context for the task is another.
A coding agent gets the same ticket twice. Switch the context on and watch the output change.
Already established by the team
Illustrative agent output
Nothing here is a coding mistake. The code compiles and the tests pass — and every line breaks something the team had already settled.Same agent, same task. The team’s approved decisions, rules and skills reached the work, so the output follows them.
Illustrative scenario, not a benchmark or a measured result. It does not claim that every AI mistake is a context problem.
The right context in. Valuable learning back.
The next task builds on what the last one learned. Approved context goes in; connected agents return findings as a structured proposal; people review what becomes reusable.
Reading approved context that applies needs no new approval. New knowledge does before it is trusted. Connecting an agent is always explicit.
Available in DM today
- Rules
- Skills
- Decisions
- Repository intelligence
- Architecture context
- Prepare Context
- Agent access over MCP
In progress
- Memory, on the same product path
Tenant file upload
Outcome from a connected agent’s task
- Delivered
- Upload path implemented
- Open question
- 1
Schema change needed: the upload path needs a new metadata column, so it was raised for review under the team’s existing review rule.
Proposed as a Decision recording the chosen approach, with the task evidence attached. Nothing is trusted until a person approves it.See the loop in the product.
Context is prepared for the task, learning returns as a proposal, and a person approves what the next task can rely on.
Screens are real DM UI with a seeded synthetic workspace. No customer data.
A 44-second introduction.
Why context gets lost between people, tools and AI agents — and how shared context keeps useful learning moving.
Descriptive transcript
Read the full transcript, including what happens on screen
Demo environment · synthetic workspace and data · backend responses are simulated. This short introduction uses illustrative motion graphics, not product UI. It shows no customer data, real AI agent, revenue or measured outcome.
This is a short homepage introduction to DM. The opening shows why useful team context can disappear between people, tools and AI agents; the closing states the shared-context promise for human and AI teams.
00:00–00:09 · Hook
On screen: An illustrative motion graphic labelled “Illustrative graphic · not product UI”. A task card changes from “Your AI agent can write code in minutes.” to “But does it know what your team already knows?” and then “The reason is stale, buried, or in someone's head.” Supporting labels show an agent working, an agent finished, and context scattered across a wiki page, chat thread, ticket and someone's head.
Narrator: Your AI agent can write code in minutes.
Narrator: But does it know what your team already knows?
Narrator: The reason is stale, buried, or in someone's head.
00:09–00:18 · Buried
On screen: An illustrative motion graphic labelled “Illustrative graphic · not product UI”. An agent report becomes a long report, then the session closes and the useful learning is left behind.
Narrator: When it finishes, what it learned lands in one more long report.
Narrator: Then the session ends, and the useful part is lost.
00:18–00:26 · Both ways
On screen: An illustrative motion graphic labelled “Illustrative graphic · not product UI”. People, tools and AI agents are shown as separate sources of decisions, documents, chat, repositories, prompts and sessions before DM connects the context.
Narrator: Context breaks in both directions.
Narrator: DM, from AuzzurA, keeps it moving between people, tools and AI agents.
00:26–00:38 · The loop
On screen: An illustrative motion graphic labelled “Illustrative graphic · not product UI”. Arrows show context going into work and learning coming back: team knowledge, Prepare Context, work outside DM, proposals, human review, trusted context and the next task.
Narrator: DM prepares what applies to the task.
Narrator: A person or an agent does the work.
Narrator: What it learns comes back as a proposal.
Narrator: People decide what becomes trusted.
Narrator: The next task starts with it.
00:38–00:44 · Close
On screen: The DM mark, “Shared context for human and AI teams.”, “DM by AuzzurA”, and the call to action “Explore the product · auzzura.com/product”.
Narrator: Shared context for human and AI teams.
Narrator: DM, by AuzzurA.
Where to go next
- 01 / For builders
Stop re-explaining the project.
Carry decisions and constraints across coding agents and tasks.
For builders - 02 / For engineering teams
Keep team guidance current.
See where shared context breaks first, in real team workflows.
Explore use cases - 03 / The product
See what DM does today.
Rules, Skills, Decisions, repository intelligence, and how each is labelled.
View the product
Give the next task a better starting point.
Use what your team already knows. Bring useful learning back. Keep context moving between people, tools and AI agents.
Only the context a person or connected agent is allowed to use. Trust & security