AI Data, Infrastructure and Agents
Build AI on Trusted Business Context, Not Raw Data
AI initiatives fail when business data is fragmented, inconsistent, and disconnected from how the company actually operates.
Discern provides the AI transformation infrastructure that turns CRM, billing, ERP, Product, and operational data into trusted business context, powering AI agents, automation, and decision-making at scale.
From semantic data layer and AI-ready metrics to agent development, data-quality audits, and governance — Discern helps B2B companies move from AI experimentation to production.
GTM & Finance Teams
Partner with Discern to operationalize AI on their data
Data Consolidation Across All Your Business Systems
Whether your data lives in CRM, billing, ERP, product, operational systems, or your warehouse, Discern ingests, transforms, and turns it into trusted business context.
Why AI Performs Better on Discern
100% Accurate Business Metrics
AI retrieves trusted calculations rather than recreating business logic. Every team and every AI system operates from the same definitions.
Faster Time to Production
Move from AI pilot projects to production-ready deployments in weeks instead of months. Business context, metrics, and operational logic already exist.
Lower AI Infrastructure Cost
Enables models to work with a smaller, more structured context, reducing token usage, model calls, and compute requirements.
Faster AI Responses
AI agents retrieve calculated, business-ready metrics directly rather than processing raw operational records at each query time.
Reduced Hallucinations
Business definitions, KPI logic, and relationships are already encoded within the semantic layer. AI works from trusted context instead of assumptions.
Governance & Auditability
Every output can be traced back to underlying business logic, calculations, and source systems. Build trust while maintaining control.
Four Core AI Transformation Services
AI-Ready Semantic Business Layer
Discern transforms operational data into a semantic business layer containing business definitions and thousands of calculated metrics. Examples include:
- ARR/MRR/GRR/NRR
- Churn Risk
- Conversion Rates
- Pipeline Coverage
- Sales Cycle
- ASP
- Marketing ROI
AI Agent Development & Operations
Discern designs, deploys, and maintains AI agents that support real business workflows. Examples include:
- Data Quality agents
- Rep Coaching agents
- Customer Retention agents
- Cross-sell / Upsell agents
- Deal Inspection agents
- Pipeline movement agents
Data Quality & AI Governance
AI can only be as reliable as the underlying data. Discern continuously identifies and helps correct:
- Missing fields
- Duplicate records
- Revenue attribution issues
- Pipeline inconsistencies
- Broken hierarchies
- Process violations
End-to-End AI Infrastructure
Discern helps companies build the complete foundation for enterprise AI, including:
- Data pipelines
- ELT architecture
- Data warehouses
- Semantic layers
- Governance frameworks
- Agent platforms
AI Agents, Powered by Discern Intelligence Modules
Every Discern AI agent is built on top of one of our proven intelligence modules. The agent inherits the module’s data transformations, business logic, and metric definitions — ensuring Investor-Grade Truth™ in every output.
Cross-Sell / Upsell Agent
Which customers are ready to grow — and what should we offer them?
- Analyzes subscription mix, product usage, and account profile (segment, size, industry, stage).
- Matches patterns against expansion playbooks to surface high-propensity targets.
- Creates prioritized Salesforce opportunities for the account team, biweekly or monthly.
Output: Ranked expansion accounts, recommended products with rationale, auto-created CRM opportunities.
Data Quality Agent
Can we trust the data in our source systems?
- Scans CRM, billing, and subscription records for hygiene issues — overlapping subs, gaps, and stale contacts.
- Verifies company names, domains, and contact employment against external reality, and flags changes.
- Routes exceptions through a human-in-the-loop queue, then writes approved fixes back to source.
Output: Exception queue, approved write-backs to CRM and billing, full audit trail of every change.
Pipeline Health Agent
Do we have enough pipeline to hit our targets?
- Continuously evaluates pipeline coverage against bookings goals.
- Analyzes coverage ratios by segment, region, and team.
- Applies historical conversion rates and velocity trends.
Output: Clear “on track / at risk” signals, root cause breakdown, recommended actions.
Funnel Performance Agent
Where are deals breaking down in the funnel?
- Analyzes conversion across every stage of your sales funnel.
- Tracks stage-by-stage conversion rates and velocity.
- Flags statistically significant deviations from historical norms.
Output: Real-time red flags, root cause analysis, targeted conversion recommendations.
Rep Coaching Agent
Which reps are on track — and who needs support?
- Evaluates rep-level performance across pipeline activity, conversion, and outcomes.
- Benchmarks against team averages.
- Identifies reps at risk of missing targets early in the quarter.
Output: Rep scorecards, early intervention alerts, data-driven coaching recommendations.
Deal Inspection Agent
Which deals are real — and which are at risk?
- Performs deep deal-level analysis to assess quality, risk, and likelihood of closing.
- Evaluates progression, activity, and engagement signals.
- Detects stalled or at-risk deals.
Output: Deal-level risk scoring, prioritized “deals to inspect” list, suggested actions.
From AI Experiments to AI Adoption
Most companies start with AI agents and MCP servers; the successful ones start with AI infrastructure. Discern provides the trusted business context, semantic data layer, governance framework, and operational expertise required to make AI work at scale.
Reliable, governed, and high-quality data provides the foundation for accurate analytics and AI-driven decisions. Use Discern to prepare business-ready data for AI. Access it through MCP or sync it back into your warehouse.
A semantic layer enriches data with business definitions, metrics, and relationships that make it meaningful and actionable.
Leverage business context to understand, reason, and respond using consistent business logic rather than raw data.
Context-aware AI delivers faster insights, better decisions, and measurable improvements in business performance.
Build Your First AI Agent
Connect your CRM and get access to Discern’s out-of-box AI agents — free. See pipeline health signals, deal risk scores, and data quality alerts in minutes, not months.
Frequently Asked Questions
The foundation layers enterprises need to run AI on their own business data — a business context layer, a semantic data layer of pre-calculated metrics, and the infrastructure to build, deploy, and govern AI agents — so AI works from trusted context, not raw, fragmented data.
Business data is fragmented and inconsistent across systems, key metrics are defined differently across teams, and AI lacks business context. Data-quality issues erode trust, costs rise as context windows grow, and agents get hard to maintain. The bottleneck is the data foundation, not the model.
It turns raw operational data into business-ready definitions and thousands of pre-calculated metrics — ARR, churn risk, pipeline coverage, conversion rates. AI retrieves these trusted calculations instead of recreating business logic at query time, making answers faster, cheaper, and more accurate.
Business definitions, KPI logic, and data relationships are encoded in the semantic layer, so AI works from trusted context rather than assumptions — and every output traces back to its underlying calculations and source systems.
Every Discern agent is built on a proven intelligence module and inherits its transformations, business logic, and metric definitions — ensuring Investor-Grade Truth™ in every output, with a full audit trail back to source.
Weeks, not months. Because the business context, metrics, and operational logic already exist in Discern, teams move from AI pilots to production-ready deployments far faster than building the foundation from scratch.
Across your stack — CRM, billing, ERP, product, and operational systems, plus spreadsheets and your data warehouse. Access the result through MCP or sync business-ready data back into your warehouse.
Ready to Build AI That Actually Works?
AI agents that actually work — built on decision-ready data, not raw inputs. Pipeline health, deal inspection, data quality automation, and proactive insights.