AI agents & readiness
AI agents working from records that agree.
Agents read the same records your people do. When those records disagree, an agent repeats the disagreement at scale. We fix the identity layer and permissions first, then deploy AI with its cost and accuracy measured.
Check the AI’s new leads before your reps do
Most bad AI output starts with records the AI cannot match. Run the check.
- Oakline FloorsMarked newExisting customer
- Marrow & GrainMarked newNew prospect
- Tidewater PlankMarked newExisting customer
- Quill & ThreadMarked newAlready in the CRM
- Harbor & PineMarked newExisting customer
- Ostrander GlassMarked newNew prospect
- Wexford StoneMarked newAlready in the CRM
- Larkspur WallcoveringsMarked newNew prospect
Example companies, invented for this page.
Do these sound familiar?
What we deliver
- Data readiness assessmentIdentity coverage, duplicate rates and definition conflicts measured before any model runs.
- Agent access designPer-user, least-privilege access for Claude through MCP, for Agentforce or for your own agents.
- AI-assisted operationsDocument reading, classification and research, run with reviewer and challenger checks.
- Prospecting & scoringNet-new discovery deduplicated against your CRM, graded with hard spend caps.
- Cost & accuracy reportingReal cost against the quote and hand-checked error rates, reported before rollout.
- Integration with the CRMAgent output written back to Salesforce through governed, auditable paths.
This practice in our case studies
- AI prospecting a sales team can trustAn AI prospecting tool kept returning customers the CRM already had. The identity layer it checks against now covers every known prospect.
Questions before we start
Which AI platforms do you work with?
Claude through MCP, Agentforce and custom agents on AWS. The platform matters less than the data and permissions it reads.
How do you control AI cost?
Spend caps on every batch, reuse of results that have not changed, and real cost reported against the original quote before anything scales.
How do you know the output is right?
We hand-check samples against the source before rollout, report the error rate to the business, and keep a reviewer step on anything that writes to a customer record.
Integrations and guides
- Salesforce and Apollo.ioProspects and enrichment from Apollo.io without blanked fields, wrong owners or “new” leads that are already customers.
- Salesforce and Claude (MCP)Claude reads Salesforce as the person using it, through Salesforce’s hosted MCP servers, and takes only the actions you approve.
- Connecting Claude to Salesforce through MCPSalesforce hosts MCP servers that let Claude query your org as the person using it. Setup takes an afternoon. Most questions afterwards come from four misunderstandings, all fixable.
- Agentforce readiness: what to fix before your first agentAn agent answers from your records. If the records disagree, it gives a confident wrong answer faster than a person would. These are the checks we run before any agent goes live, whether it is Agentforce inside Salesforce or Claude connected through MCP.
Related practices
- Salesforce architecture & engineeringApex, Flow and Lightning simplified and secured, and documented so your own team can run the org.
- Systems integrationSalesforce connected to your commerce, warehouse, contract, billing and support systems, with one owner for every field and an alert when a sync goes quiet.
- Data unification & migrationOne record per customer across regions and platforms, merged and migrated with a rollback for every change.
- Revenue & contract operationsThe deal, the signed contract and the bill kept in agreement, from the first quote to the renewal.
Talk to us about your systems.
Tell us which systems are involved and what is going wrong. We reply within one business day, and new engagements begin in January 2027.
hello@feldtgroup.com