Turn the "why" behind your numbers into context your AI can use.
Dashboards show what happened. The reasons live in emails, meeting notes and hallway conversations, invisible to any AI reading your data warehouse. Writeback fixes that by capturing context as structured data, right next to the numbers it explains.
Your data warehouse only knows the number
Revenue dipped 8% in March. The fact table shows the number; the reason, a delayed customer contract, lives in someone's inbox. Ask an AI assistant to explain the variance and it can only guess, or hallucinate a plausible-sounding cause.
The same variance carries a comment, attached to that exact cell, written back to the same database as the fact table. Now any AI tool querying the model retrieves the number and the explanation together, grounded, not guessed.
AI is only as good as the context it can retrieve
Large language models don't know your business, they know what's in front of them. Contextualized comments, tied to a specific fact and written back to the source database, become retrievable metadata: institutional knowledge an AI assistant can cite instead of inventing.
- ✓ Grounds AI answers in real explanations, not inference
- ✓ Reduces hallucination risk on variance and forecast questions
- ✓ Keeps tribal knowledge in the database, not in someone's inbox
Comments, tied to the cell, written back to source
Threaded to the data point
Comments attach to the exact visual, row or cell they explain, not a separate document.
Written back to the database
Comments live in the same SQL Server or Fabric database as the fact table, queryable alongside it.
Structured, not free text buried in chat
Approval flags, roles and timestamps come with every comment, so context is auditable and machine-readable.
Context & AI questions
What is "contextualized data" in this sense?
Data paired with the explanation behind it, who changed a number, why a forecast assumption shifted, what drove a variance, stored as structured metadata next to the fact itself rather than in a separate document or chat thread.
How does accoCOMMENT feed AI tools?
Comments write back to the same database as your semantic model, so any AI assistant or agent querying that database can retrieve the comment alongside the number it explains, instead of only seeing the number.
Can this reduce AI hallucinations in reporting?
It can't eliminate hallucination, but it removes one common cause: an AI assistant guessing at a cause because no real explanation was available. Grounded context gives it something true to retrieve instead.
Do I need accoPLANNING too, or does accoCOMMENT work standalone?
accoCOMMENT works on its own, on top of any Power BI report. Many teams pair it with accoPLANNING so budget and forecast entries carry their own context automatically.