AI in Salesforce · Enterprise Revenue Architecture

Salesforce gives you the AI platform. We give you the revenue logic.

Agentforce. Einstein. Data Cloud. Salesforce’s AI capabilities are significant — they only compound revenue performance when the commercial architecture underneath them is built to absorb them. That’s the work we do.

Salesforce AI Stack · Twopir Practice
Agentforce — Autonomous Agents
Einstein Predictive AI — Scoring & Forecasting
Einstein Generative AI — Copilot & Content
Salesforce Data Cloud
CRM Analytics — Revenue Intelligence
Enterprise AI for
SaaS
FinTech
IT Services
Manufacturing
Law Firms
Real Estate
Enterprise & Growth-Stage
Most enterprises activate Salesforce AI. Few build the foundation it needs — the data model, commercial logic, and governance architecture that determines whether every AI output is worth acting on.
Let’s Talk →
The Salesforce AI Stack

Three distinct AI layers. One commercial architecture.

Salesforce’s AI capabilities span autonomous action, embedded intelligence, and unified data. Each layer depends on the one beneath it. We design all three as a connected revenue system — not a list of features to activate.
01
Agentforce — Autonomous Agent Architecture
AI that doesn’t just recommend. It acts.
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Agentforce is Salesforce’s most significant architectural shift in years. Agents that qualify leads, update records, surface deal risk, trigger downstream workflows — without a rep clicking anything.

But autonomous action without defined commercial logic is a risk, not a capability. Before we build any agent, we design the operating model it runs inside: what it acts on, what triggers human escalation, how actions are logged, what happens at the edge cases.
02
Einstein AI — Predictive & Generative Intelligence
Calibrated to your pipeline reality, not Salesforce defaults.
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Einstein is Salesforce’s embedded AI layer — and it runs across every cloud. But its value is entirely conditional on the quality of data it’s trained on and the business logic it’s calibrated against.

We calibrate Einstein against your commercial motion — not out-of-the-box configurations.
03
Salesforce Data Cloud — The Data Foundation
Every AI output is only as reliable as the data beneath it.
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Data Cloud isn’t a feature — it’s the infrastructure layer that determines whether Agentforce acts on truth or on noise. We design Data Cloud implementations that begin with your commercial data model.
04
Revenue Data Architecture — The Prerequisite
AI readiness begins at the CRM object and data model level.
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Data remediation and governance design must precede AI activation. This is the single most common structural gap we find in enterprise Salesforce engagements.
How We Engage

Architecture first. AI second. Outcomes always.

Every engagement follows a deliberate sequence. Revenue systems built in the wrong order are the ones that fail at scale.
Step 01

Revenue Infrastructure Audit

We assess your Salesforce environment — data quality, CRM architecture, process logic, automation integrity, and AI readiness across all clouds.

Step 02

Commercial Motion Mapping

We document your selling motion, buyer journey, and team handoff logic. This blueprint becomes the design spec every AI component is anchored to.

Step 03

Data Cloud & CRM Foundation

We rebuild or restructure the data layer — the work most firms skip.

Step 04

AI Activation — Agentforce, Einstein & Analytics

With the foundation validated, we activate AI capabilities in deliberate sequence.

Step 05

Governance, Enablement & Ongoing Performance

We hand off with technical documentation, role-based enablement, and a governance model.

Our Model

Embedded. Not parachuted in.

We operate as an extension of your team — outcome accountability, not go-live dates.

Industries

Complex commercial environments are where Salesforce AI gets genuinely difficult.

We operate best with revenue teams where the commercial model isn’t simple — multi-stakeholder buying, long cycle selling, mixed revenue streams, or Salesforce environments built across multiple acquired companies.

SaaS & Software
PLG to enterprise, mixed motion, expansion revenue intelligence via Einstein, and Agentforce calibrated to both high-velocity and complex deal cycles.
FinTech & Financial Services
AI activation where data classification, compliance logic, and audit requirements shape every architecture decision.
Manufacturing & Distribution
Partner relationship management, CPQ complexity, Agentforce for channel enablement.
IT Services & MSP
Managed service contract intelligence, upsell signal activation via Einstein, Agentforce for renewal operations.
Law Firms & Professional Services
Salesforce architecture for relationship-led revenue — matter integration, business development pipeline design.
Real Estate & PropTech
High-volume lead qualification via Agentforce, Einstein buyer intent scoring.
Start the Conversation

Salesforce AI is not a feature problem. It’s an architecture problem.

If the revenue outcomes haven’t moved despite the platform investment, the answer is almost never more features or more licences. It’s the structural layer underneath them. Let’s look at what you have and design what it needs to become.

Salesforce AI Architecture Review
A focused diagnostic of your Salesforce AI readiness — data quality, CRM architecture, and activation gaps.
  • CRM data quality and AI readiness assessment
  • Agentforce commercial operating model review
  • Einstein model calibration gap analysis
  • Data Cloud architecture and profile quality audit
  • Multi-cloud alignment and integration assessment
Book a Strategy Session
No pitch deck. No obligation. Just a direct conversation.