Sage agents transform, build with and govern your enterprise data for AI.
01 — Transform
Automate your data transformation program.
Sage agents discover how your data estate works, recover the business meaning embedded within it, and help transform it into AI-ready data.
Discover
Map schemas, pipelines, transformations, reports, dependencies and business logic.
Understand
Learn from your existing query corpus and usage patterns to recover definitions, relationships and business logic.
Model
Build the semantic layer and enterprise blueprint that gives AI a consistent understanding of your data.
Transform
Visualize and build new data products and accelerate migration from legacy to modern data platforms.
Discover → Understand → Model → Transform
02 — Build
Build powerful agentic data apps.
Assemble the context, tools and business logic required for real enterprise jobs — on the agent stack you already use.
Cross-sell
Performance management
Planning
Customer service
03 — Govern
Automate data governance and operations.
Sage agents continuously maintain the quality, integrity and usability of your enterprise data.
Masters & metadata
Maintain master data, business definitions, metadata, lineage and ownership.
Pipeline operations
Monitor pipelines and dependencies for failures, freshness issues, schema changes and anomalies.
Data product operations
Detect issues, trace root causes through lineage and help resolve broken data products.
Semantic governance
Keep semantic models, mappings and business definitions synchronized as the underlying estate changes.
Don’t just make your data AI-ready. Keep it AI-ready.
Explore Sage+
Inside Sage
Your data estate. Understood and put to work.
Explore how Sage discovers your landscape, builds its semantic blueprint, creates data products and keeps them running.
SAGE / Customer domainIllustrative workspace
See how your data estate fits together.
Connected estate
Sources & assets
DB
Customer warehousecustomers · accounts · products
ETL
Data pipelinescustomer_sync · account_daily
SQL
Queries & reportscustomer_activity · revenue
API
Business applicationsCRM · service · transactions
Recovered from usage
Business logic in queries
SELECT c.customer_id,
SUM(a.balance) AS balance
FROM customers c
JOIN accounts a
ON c.customer_id = a.customer_id
WHERE a.status = 'active'
GROUP BY c.customer_id
Relationship found: a customer owns accounts. Business rule found: only active accounts contribute to the balance.
Connected lineage
Follow the dependency
SourceCRM customer
↓
Pipelinecustomer_sync
↓
Data productcustomer_360
↓
Used byCross-sell application
Discover schemas, pipelines, queries, dependencies and lineage across your data domain. Example data shown.
SAGE / Customer domainIllustrative workspace
Recover the business meaning in your data.
Entity
Customer
customer_idsegmentrelationship_start
Source: CRM customer
owns →
Entity
Account
account_idcustomer_idstatus · balance
Source: core accounts
holds →
Entity
Product
product_idproduct_typeeligibility_rules
Source: product catalog
Business definition
Active customer
A customer with at least one active account.
Derived from customer_activity queries
Metric
Customer balance
The sum of balances across a customer’s active accounts.
Linked to accounts.balance + status
Ownership
Customer domain
Definitions, relationships and mappings with explicit sources and owners.
Owned by the customer data team
Connect entities, definitions and business logic to the sources and queries that explain them. Example data shown.
SAGE / Customer domainIllustrative workspace
From the existing estate to a usable data product.
Measure your agents. Understand why they fail. Turn experience into proven improvement.
Connect agent traces, evals and real outcomes to find the changes that measurably improve performance.
Measure→Improve→Prove
01 — Measure
Know how your agents perform.
Connect every run to evals and real outcomes. Find recurring failure modes.
02 — Improve
Learn what makes them better.
Use successful experience and failures to propose better context, procedures and guardrails.
03 — Prove
Proof before behavior change.
Test every improvement against meaningful controls. Only what measurably works gets fed back.
Every failure routes to a fix: better context, a new skill, or a model update.
✓
Proof before behavior change.
Compare proposed improvements against meaningful controls on held-out work. Measure task success, recurring failures and business outcomes. Only changes that measurably work are promoted.
Explore Sygnal+
Inside Sygnal
From agent traces to proven improvement.
Sygnal connects agent traces, evals and outcomes so you can inspect failures and evaluate proposed changes.
01
Agent traces
Reconstruct episodes from actions, decisions and the context available to your agents.
02
Evals + outcomes
Check performance against enterprise rules and business outcomes. Understand where and why agents fail.
03
Proven improvements
Compare candidate changes with a baseline on held-out work and prospective controls before changing behavior.
Learn from what you can measure and prove.
Three weeks · Your agents
See what makes your agents better.
In three weeks, Sygnal helps you understand how your agents perform, why they fail, and which changes actually improve them.