AI is only as good as the
context behind it
Squivr is the Salesforce-native revenue context layer beneath Agentforce, Claudeforce, and our own rule-based AI. Relationship intelligence, account planning, analytics, and execution, all structured inside your org.
Salesforce has the data.
AI needs the context.
Ask a rep the simplest question in the business, "what should I do next in this account?", and a good answer needs far more than the account name, the open pipeline, and last week's activity. It needs to know who the decision makers are, who influences them, whether there is an executive relationship, who the champion is, which personas are missing, what the customer's strategic priority is, which competitors are in play, and where whitespace exists.
That is the difference between data and context. Salesforce holds enormous amounts of customer data. Squivr structures the revenue context around it, so that Agentforce agents, Claudeforce reasoning, and rule-based scoring all work from the same picture of the customer.
Agentforce, Claudeforce,
and rule-based AI
These are not competing choices. They answer different questions, and each one gets better when it is grounded in structured revenue context. Squivr supplies that context, natively, on the platform.
Agentforce
"Go do it."
Salesforce's agentic platform. Agents take action inside the org. Squivr gives them the relationship, planning, and scoring context to act on, plus native actions to call.
See how Squivr grounds agentsClaudeforce
"Why is this happening?"
Reasoning across your Salesforce data in natural language. The quality of the answer depends on the quality of the context underneath it. That context is what Squivr structures.
See the reasoning layerSquivr Rule-Based AI
"Show me your work."
Human-defined if-then logic and configurable scoring you can read, edit, and audit. Same inputs, same output, every time. Explainability is what makes adoption stick.
See the rules engineAn agent is only as good as
what it can see and do
An agent asked to prep an account review, flag renewal risk, or draft a stakeholder outreach plan needs more than field values. It needs to know who the buying group is, who is missing from it, what the account team already committed to, and which milestones are overdue.
Squivr structures all of that on standard and custom Salesforce objects, so Agentforce can retrieve it the same way it retrieves anything else in the org. No connector, no sync, no second system of record. Squivr is listed on Salesforce AgentExchange.
View on AgentExchange- Buying group coverage Who is mapped, who is missing, and which personas the deal has never touched.
- Influence and sentiment Champions, blockers, detractors, and executive relationships, scored rather than guessed.
- Account strategy Strategic initiatives, SWOT, competitive position, and the customer's stated priorities.
- Whitespace Products not yet sold, divisions not yet touched, and where the team believes growth sits.
- Open commitments Action plans, playbook steps, owners, and overdue milestones.
From "what happened"
to "what should we do"
Dashboards are very good at telling you what happened. They are much weaker at explaining why. Claudeforce changes the starting point: instead of navigating to the right report, you begin with the question and reason across the org.
But reasoning needs something to reason over. Squivr adds the specialized revenue context, relationships, stakeholders, account strategy, whitespace, scoring, plans, and actions, that turns a retrieval into an explanation. Salesforce provides the system of record. Squivr provides the context. Claudeforce reasons across it. Salesforce workflow turns it into action.
Read: Squivr in the Claudeforce eraLogic your team can
read, edit, and trust
A rep who cannot see why a recommendation was made tends to quietly ignore it, however accurate the model behind it happens to be. Squivr's rule-based AI uses human-defined if-then logic: "if" is a condition on your Salesforce data, "then" is an action, a score, or a flag.
Your admins define the factors that matter to your business rather than inheriting a generic model. The same inputs produce the same output every time, the logic is visible at every step, and anyone can challenge it when their own context calls for it. That is what makes scoring survive contact with a sales team.
Talk through your rulesAnalytics is not the destination.
It is the context layer.
A CRO does not need another table of 50,000 opportunity records. They need to know where the business is performing, where pipeline is at risk, and what the team should do next. Squivr Analytics brings Grid, reports and dashboards, scorecards, and configurable scoring together inside Salesforce.
What is happening?
Grid, reports, dashboards, trends, and metrics give visibility. Inline editing, mass updates, conditional formatting, and hierarchical views let teams work with the data rather than just look at it.
Why is it happening?
Scores, relationship intelligence, stakeholder coverage, and account planning data supply the context. Configurable scoring turns raw signals into clear indicators of health, risk, readiness, or opportunity.
What should we do next?
Where analytics, AI, and workflow converge. Insights become action plans, playbook steps, and owners, executed inside Salesforce rather than in a separate tool.
Two opportunities. Same $1M. Same stage.
A pipeline report makes these look nearly identical. Relationship intelligence tells a very different story about risk.
- Strong executive sponsor
- Multiple mapped champions
- Complete buying group coverage
- Positive stakeholder sentiment
- Mature account plan in flight
- No executive relationship
- One primary contact, single-threaded
- Several decision makers unidentified
- No clear champion
- Buying process poorly understood
How Squivr builds your
revenue context layer
A repeatable path from raw Salesforce data to context that agents, reasoning, and rules can all work from, without ripping out what already works.
Decide what the AI needs to answer
Start from the questions your revenue team actually asks: where is pipeline at risk, which accounts are under-covered, what should this rep do Monday morning. The questions determine which context is worth structuring first.
Assess and prepare the data
AI amplifies whatever data quality you already have. We assess your Account, Contact, Opportunity, and activity data, flag the gaps worth closing, and get the foundation ready before anything gets scored or reasoned over.
Map relationships and buying groups
Org charts, relationship maps, influence, sentiment, decision authority, champions, blockers, and coverage gaps. This is the "who matters" layer, and it is the piece no CRM field can supply on its own.
Structure the account strategy
Action plans, playbooks, revenue summary and whitespace analysis, strategic initiatives, SWOT, competitive analysis, and positioning. The CRM now holds not only what happened, but what the account team believes should happen next.
Configure rules, scores, and analytics
Define the if-then logic and scoring factors that reflect how your business actually evaluates health, risk, and readiness, then surface them through Grid, dashboards, and scorecards where the team already works.
Activate agents and reasoning on top
With context in place, Agentforce agents have something substantive to act on and Claudeforce has something substantive to reason across. We then monitor accuracy, adoption, and outcomes, and tune the rules as the business changes.
Context applied across every
object your team works on
Squivr structures revenue context on the specific Salesforce objects your team uses every day, from standard records to complex custom and junction object relationships.
Account
Relationship coverage, influence and sentiment scoring, and next best action, grounded on the record so agents and reasoning both start from the same view of the customer.
Account Plan
Strategic initiatives, whitespace, SWOT, competitive position, and milestones, structured as data rather than as a slide, so AI can actually read the strategy.
Opportunity
Buying group coverage, champion presence, and rule-based deal risk flags that explain themselves, surfaced early enough to change the outcome.
Contacts and Leads
Persona coverage, reporting lines, and relationship strength across standard objects, with no custom development and no data migration.
Custom Objects
Extend the same rule-based framework to any custom object your org relies on, whether that is a product, a project, or an industry-specific record.
Junction Objects
Apply scoring and context to many-to-many relationships, unlocking insight across the complex data structures unique to your business.
Common questions
about Squivr and AI
What is the difference between Agentforce, Claudeforce, and Squivr's rule-based AI?
They solve different problems. Agentforce is Salesforce's agentic platform, where agents take action in your org. Claudeforce brings reasoning across your Salesforce data in natural language. Squivr's rule-based AI is deterministic if-then logic and configurable scoring that your team defines and can audit. Squivr sits underneath all three as the revenue context layer they draw on.
Is Squivr replacing Agentforce or Claudeforce?
No. Squivr is complementary and Salesforce-native. Salesforce supplies the platform and the system of record. Squivr structures specialized revenue context on top of it: relationships, stakeholders, account strategy, whitespace, scoring, plans, and actions. Agentforce and Claudeforce become more useful when that context exists.
Does any of my Salesforce data leave Salesforce?
No. Squivr is 100% Salesforce-native. There is no external data store, no separate AI platform, and no pipeline moving your records outside the org for Squivr to operate. Details are in the Squivr Trust Center.
Why does rule-based AI still matter in an era of large models?
Because explainability drives adoption. When a recommendation comes from a visible, editable if-then rule, a rep can see which inputs produced which output, trust it faster, and challenge it when their own knowledge of the account says otherwise. Rule-based scoring also gives reasoning models a structured, consistent signal to work from rather than an opaque number.
How does Squivr Analytics fit in?
Analytics is the bridge between raw CRM data and business questions. Squivr Analytics brings together Grid experiences, reports and dashboards, scorecards, and configurable scoring inside Salesforce, then connects those numbers to the relationship and planning context that explains them.
Do I need to clean my Salesforce data first?
Clean, current data makes every layer more accurate, rules and reasoning alike. As part of activation we assess your existing Account, Contact, and Opportunity data and flag the gaps worth closing before anything goes live.
Where can I find Squivr on the Salesforce marketplace?
Squivr is listed on Salesforce AgentExchange. You can review the listing, ratings, and install options there.
Give your AI something worth reasoning about.
See how Squivr structures the revenue context layer beneath Agentforce, Claudeforce, and rule-based scoring, all inside the Salesforce org you already run on.
Squivr’s Product Alignment - AI
Squivr specializes in - Rule-based artificial intelligence (AI) is a type of AI that uses human-made rules to process data and make decisions. These rules are often written as "if-then" statements, where "if" represents a condition and "then" represents an action or conclusion.
“Leveraging artificial intelligence (AI) for enhancing relationship management (RM) has become a business necessity. At Squivr, we focus on Relationship Management and Account Planning to unleash the revenue team’s competitive advantage.
We optimize your investment in AI & Salesforce to streamline how revenue teams build strategies, foster relationships, personalize customer interactions, and predict future trends.”
JP Leggett
Activating AI with Relationship Management with Squivr in Salesforce:
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Define Your AI Objectives
Before diving into the technicalities, outline what you aim to achieve with AI in your relationship management strategy. Whether it's improving customer service, personalizing marketing campaigns, or increasing sales predictions accuracy, having clear objectives will guide your AI implementation process.
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Collect and Clean Your Data
AI thrives on data. The accuracy and effectiveness of your AI predictions depend heavily on the quality and quantity of the data you feed into the system. Ensure your Salesforce data is clean, up-to-date, and comprehensive. This includes customer data, interaction logs, sales history, and any other relevant information that can be analyzed by AI.
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Leverage Your Platform
Squivr helps you active AI in Salesforce. With your objectives in place and data ready, start exploring Einstein's features
Einstein Prediction Builder: Create custom AI models to predict outcomes based on your Salesforce data.
Einstein Next Best Action: Automate recommendations for your team's next steps.
Einstein Language and Vision: Utilize natural language processing to analyze text data from emails, social media, and web chatter to gauge customer sentiment.
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Integrate and Automate Workflows
With AI models and tools in place, the next step is to integrate them into your daily workflows. Automate routine tasks, like data entry or lead scoring, to allow your team to focus on more strategic activities. Use Einstein's insights to guide decision-making in real-time, ensuring a more personalized and efficient customer experience.
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Monitor and Iterate
AI is not a set-it-and-forget-it solution. Continuous monitoring of your AI tools' performance is crucial. Evaluate the predictions' accuracy, the impact on customer satisfaction, and overall business outcomes. Use these insights to refine your models and strategies, ensuring your AI evolves with your business needs and market changes.
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Stay Informed and Compliant
Lastly, keep abreast of the latest AI advancements and ethical guidelines. AI in CRM is a rapidly evolving field, with new features and capabilities regularly introduced. Additionally, ensure your use of AI complies with data protection regulations like GDPR or CCPA, respecting customer privacy and consent.