Beyond call recording: getting real ROI from Gong
Most teams treat Gong as expensive call storage. The return is in coaching, deal execution, and forecast accuracy. Here is how to move from recording to results.
Read the full post → Why It Matters
AI does not fix bad data. It amplifies it.
A Claude agent is only ever as good as the data underneath it. Point one at a CRM full of duplicate accounts, half-empty fields, and records no one has touched in two years, and it will not fail quietly. It will answer confidently, and it will be wrong, at the speed and scale of automation. Most AI pilots that stall are not model failures. They are data failures wearing an AI costume.
What governance actually means here
Governance is not a binder of policies no one reads. For an AI deployment it is the operational answer to four questions: who owns each piece of data the agent can see, what correct means for every field it reads, how bad data gets caught before it reaches the model, and who is allowed to access what. Answer those four well and Claude becomes dependable. Skip them and no amount of prompt engineering will save the project.
How we approach it
We start narrow, with the data an agent will actually touch, not a boil-the-ocean cleanup of the whole org. We score that data against the decisions it will drive, find the gaps that would produce a wrong or non-compliant answer, and fix the highest-risk ones first. Then we put the controls in place that keep it clean: validation at the point of entry, monitoring that flags drift before it spreads, and the access controls and audit trails that hold up to a security review or a regulator.
Why Abstrakt Solutions
Data governance was core to good Salesforce delivery long before AI made it urgent. As a Salesforce Consulting Partner since 2017 with more than 100 active certifications, Abstrakt Solutions has spent years cleaning up the exact CRM data problems that now decide whether AI works. We are applying a discipline we already had, not inventing one for a trend, and we build the governance so your own team can keep it running after we are gone.
What We Deliver
How we help with Data Quality and Governance.
The engagements we run most often on Data Quality and Governance, from first implementation through optimization.
Governance framework
We define data ownership, standards, and policies for every domain an agent depends on, and assign real owners so each decision has a name attached. The result is a framework your team runs, not a document that gets filed and forgotten.
Data quality standards
Field-level definitions of what good looks like, with validation rules and monitoring that catch problems at entry instead of in a report three months later. Quality becomes a property of the system, not a quarterly cleanup.
Security and audit
Role-based access so agents and their users only see what they should, plus audit trails that record what was accessed and why. Built to satisfy a security review or a regulator without a fire drill.
Data hygiene and remediation
Deduplication, enrichment, and structured cleanup to pay down the data debt you already carry, sequenced by what affects AI answers first. We fix the records that change outcomes, not just the ones that are easy.
How We Work
The engagement model.
Predictable phases. Clear deliverables. No surprises.
01
Discovery
One to two working sessions to map your current state, business goals, and gaps. We come out with a written scope and recommendation.
02
Design
Documented architecture, realistic timeline, and transparent commercial proposal. No surprises and no hidden scope.
03
Build
Configuration, development, integrations, data migration, and QA, with weekly demos and on-the-fly adjustments.
04
Launch & Optimize
Training, change management, hypercare, and ongoing optimization. We do not disappear at go-live.
Featured Insight
Insights on Data Quality and Governance.
Practitioner-level analysis from the consultants delivering the work.
Revenue Operations