Enterprise seller working with an AI assistant interface on a laptop

AX3 Transformation Playbook

AI Sales Concierge

An agentic selling assistant grounded in catalog, pricing and entitlement data.

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Discuss This Playbook

In brief

What it is: A reference architecture for an AI assistant that supports sellers with account context, guided next steps and grounded answers.
Who it is for: Sales leadership and revenue operations in complex B2B selling environments.
The problem it solves: Sellers spend a large share of their time assembling context — account history, product fit, pricing and service exposure — instead of selling.
Technologies involved: Agentforce, Salesforce Sales Cloud, Data 360, retrieval over governed enterprise content.
Industries it applies to: Manufacturing, technology, semiconductor, financial services and B2B services.
What AX3 provides: Agent design and guardrails, grounding data architecture, Salesforce implementation and evaluation of agent quality in production.

Business Problem

What is actually going wrong.

Industry: Cross-Industry

Complex products slow sellers down. Discovery, configuration, eligibility and pricing live across catalogs, rule engines and policy documents. New sellers take too long to become productive, and quality of advice varies by individual rather than by process.

Why this is hard

The constraints most programmes underestimate.

Grounding is the hard part

An agent is only as good as the account, product and pricing data it can be trusted to read.

Content is scattered

Product, pricing and reference material lives across CMS, drives, decks and tribal knowledge.

Trust must be earned

One confidently wrong answer in front of a customer sets adoption back months.

Measurement

Agent quality has to be evaluated continuously, not signed off once at go-live.

The AX3 approach

How we sequence the work.

01
Start with a narrow, high-value question set
Deploy where the seller loses most time, then widen the agent's remit.
02
Ground on governed data
Retrieval scoped to approved product, pricing and account sources with clear permissions.
03
Instrument quality
Evaluation harnesses, feedback capture and guardrails from the first release.

Reference architecture

From business process to business action.

01Business process
Research → prepare → engage → follow up → forecast
02Data
Account, opportunity, product, pricing and service history, permission-aware
03AI
Agentforce assistant with retrieval, guardrails and evaluation loop
04Salesforce
Sales Cloud as the workflow surface, Data 360 for grounding
05Integration
Content sources, ERP pricing and service systems
06Engineering
Retrieval pipelines, permission enforcement and observability
07Business action
Sellers arrive prepared, with answers they can defend

Workflow

The end-to-end path we design against.

01
Ask
02
Retrieve
03
Reason
04
Recommend
05
Evaluate

01
Understand the customer need in natural language
02
Retrieve eligible products, configurations and constraints
03
Recommend a configuration with rationale the seller can defend
04
Price within governed guardrails and route any exception
05
Generate the quote and hand off to the approval and order path

Technology involved

Industry expertise, applied through four connected layers.

Salesforce

Explore →

Agentforce
Sales Cloud
Revenue Cloud for configuration and pricing
Data 360 for grounding
Experience Cloud for partner-facing use

Data

Explore →

Product catalog, configuration rules and compatibility
Pricing, discount policy and approval thresholds
Customer entitlement, contract and installed base
Historic deal shape and win/loss context

AI & Agentforce

Explore →

Agentforce sales agent with retrieval over catalog and policy
Guided selling with explainable recommendation rationale
Guardrails constraining the agent to eligible, in-policy actions
Evaluation harness measuring recommendation quality over time

Engineering

Explore →

Retrieval grounding over product and policy content
Integration to configuration and pricing engines
Action boundaries and human-in-the-loop approval design
Agent observability, logging and evaluation tooling

Industry application

Where this applies.

Complex B2B selling environments — long cycles, technical products, many stakeholders — where seller preparation time and answer consistency are the constraint.

Agentforce Data 360 AI & Data

Delivery capability

How AX3 gets this built and sustained.

Forward Deployed Engineering

Most sales AI pilots stall at grounding and permissions. AX3 FDE teams take the agent from demo to production inside your estate.

Permission-aware retrieval over enterprise content
Evaluation harnesses and quality monitoring
Integration with pricing and service systems

Explore FDE

Expected Business Outcome

What changes when this works.

Reduced manual effortMore consistent executionFaster workflowsGreater operational intelligence

Shorter time from customer conversation to defensible quote

More consistent advice across experienced and new sellers

Configuration and pricing errors caught before approval

Faster seller onboarding on complex portfolios

These are the outcomes this solution concept is designed to produce. They are directional, not measured results from a named engagement.

FAQ

AI Sales Concierge: common questions

What is the AI Sales Concierge playbook?

A reference architecture for a grounded AI assistant that gives sellers account context, guided next steps and answers drawn from approved data.

What business problem does it solve?

Sellers spend disproportionate time assembling context instead of selling, and answers vary from person to person.

What technologies are involved?

Agentforce and Salesforce Sales Cloud, Data 360 for grounding, retrieval pipelines over governed enterprise content.

Can AX3 provide the engineering capability required?

Yes. AX3 designs the agent and its guardrails and can embed Forward Deployed Engineers to build grounding, permissions and evaluation in your environment.