A pipeline that does not match the sale
Generic stages hide what is actually needed to move forward and make the forecast impossible to read.
We configure or rescue your CRM so every opportunity has an owner, a next action and context—with no double entry and no reporting that depends on a parallel spreadsheet.
Training cannot fix adoption when the system requests data nobody uses, duplicates tasks or fails to reflect the real sales process.
Generic stages hide what is actually needed to move forward and make the forecast impossible to read.
Email, messages and notes live outside the contact. The seller knows the history; the rest of the company does not.
If the team completes fields but gets no alerts, context or priorities, CRM feels like surveillance and is abandoned.
The platform is a later decision. First we define what each role needs to see and which event moves an opportunity.
Stages, required fields, loss reasons and next actions defined with the team that uses them.
Normalisation, deduplication and mapping so the new database does not inherit the old disorder.
Assignment, tasks, follow-up, alerts and activity logging without relying on individual memory.
Each role sees and changes what it needs; leaders get metrics built on data people actually enter.
A CRM becomes adoptable when configuration removes work. The project is structured to prove that early and adjust against real usage.
Lead sources, stages, owners, exceptions, reporting and every point of double entry.
Objects, fields, pipeline, permissions and automation before migrating a single record.
Test mapping on a sample, remove duplicates and validate totals with business owners.
Training on real cases, launch support and adjustments based on what the team actually uses.
The deliverable combines configuration, data and integration. A tidy pipeline without connected channels depends on manual entry again within days.
It depends on process, volume, integrations and the team that will administer it. We evaluate those factors before recommending HubSpot, Zoho or another platform.
Yes. We audit configuration, data and real use, preserve what works and correct the parts causing double entry or unreliable information.
Yes. Migration includes mapping, normalisation, deduplication, test runs and total validation before the final cutover.
A bounded first scope generally takes weeks. Timing varies with data quality, integration count and permission complexity.
Yes, once records and permissions are in order. AI can summarise, classify and prepare actions, but it cannot fix a duplicate database.
We review pipeline, data and integrations, then tell you what to keep, what to fix and what a realistic first scope looks like.