CRM hygiene & pipeline reporting
Reporting arguments are usually data arguments. This agent checks your records against the rules your team has agreed — required fields, naming, ownership, stage definitions — lists what is wrong and who owns it, and proposes the merges. Once the data is defensible it builds the recurring pipeline report from the same source, so everyone is reading the same numbers.
Step by step
Write the rules down
Required fields, naming conventions, stage definitions and ownership rules are agreed and recorded as configuration.
Scan the records against them
The agent checks open and recent records against every rule and collects the exceptions.
Group the likely duplicates
Probable duplicates are clustered with the evidence for the match and a proposed surviving record.
List exceptions by owner
Each rep sees their own list, which is shorter and far more likely to be acted on than a global report.
Build the pipeline report
The recurring report is drawn from the cleaned view, with any exceptions still outstanding shown alongside it.
Operations approves every merge
No records are merged and no fields overwritten until your revenue operations owner accepts the proposal.
Exactly what this agent can see, touch and change
The same five controls sit behind every ollo agent. These are this one's settings — visible before you build it, not buried in an admin screen afterwards.
Context
What reaches the modelRevenue operations teams whose forecast gets questioned because of the data behind it, and organisations where several tools write into the same records.
Escalation
When it asks a personRevenue operations approves every merge and field change before it is written
Applications & Rights
Which tools it uses, and what it may do in each
Google DriveRead only
ZapierReadWrites newCreates new records or documents. Never edits, moves or deletes anything that was already there.
SlackReadWrites newCreates new records or documents. Never edits, moves or deletes anything that was already there.Verification
How you know it’s rightEvery claim links to the document it came from. A statement the agent cannot cite does not make it into the output — which is what makes the result reviewable in minutes rather than re-read end to end.
Who it’s for
Revenue operations teams whose forecast gets questioned because of the data behind it, and organisations where several tools write into the same records.
What you’ll need
- CRM reachable through your automation layer
- Written field and stage rules
- A revenue operations owner
- Agreement on what may be written back
What you get
- A duplicate cluster list with proposed merges
- An exception list per record owner
- A recurring pipeline report from clean data
What it doesn’t do
It proposes; a person merges. It will not invent missing data either: an empty required field is reported to its owner rather than filled with the agent's best guess.
We build the first one with you
Not a template you configure alone. We sit with your team, build it on real data, and hand over the controls.
Scope
One session with the people who actually do the work. We agree what the agent reads, what it may write, and who approves.
Co-build
Built on your own data, not a sandbox. You watch it being made, so you know why it behaves the way it does.
Handover
You own the controls. Change the context, tighten the rights, move the approval gate — without coming back to us.
Parts of the work currently spread across the categories below. It does not replace any of those products outright.
Frequently asked
Can it merge records automatically?
Only if you configure it that way for a narrow, clearly safe case, and most teams choose not to. Merging is destructive and hard to reverse, so the default is that operations accepts each proposal.
How does it decide two records are the same?
By comparing the fields you tell it matter, such as registered name, domain and contacts in common, and it shows the comparison. Borderline cases are presented as borderline rather than resolved quietly.
Does the report replace our BI tool?
No. It reports on the pipeline from the records, with the data quality caveats attached. Where you have a warehouse and a BI layer, the agent's job is to make the source data defensible rather than to become another reporting layer.
What happens when the rules change?
You change them in the configuration and the next scan uses them. Rules change most early on, when the first exception lists show you which of your rules nobody was following.