Give business teams a private AI data agent that understands your metrics, works across approved ERP, CRM, finance, operations, and knowledge databases, and turns plain-English questions into traceable business insights.
Go straight to the source: your database. No middleman and no artificial limits. TTYD talks directly to the data layer, so your answers aren’t limited by what an API, connector, or MCP server chooses to expose.
Compare the old bottlenecks with the governed AI approach that replaces them.
Traditional way
Business users either work around existing dashboards or wait for analysts to translate a question into SQL, filters, and another report.
AI ERA
The agent translates intent into queries against approved sources and keeps the context alive for the next question.
A useful business answer should show how the agent interpreted the question and what data and logic produced the result.
Resolve metric, period, region, entity, and business meaning.
Identify the approved systems needed for the analysis.
Build the query plan against approved schema relationships.
Return the result with sources, filters, and useful query context.
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Deep analysis
THE ANSWER AND ITS EVIDENCE STAY TOGETHER FOR REVIEW.
Gross margin · West region
Revenue remained comparatively stable while freight expense and return-related costs increased during the same period.
Example gross-margin decline
Meaning resolved
Sources approved
Query validated
Evidence returned
🧊 “Why is gross margin declining in the West region?”
3.8 pts
Meaning resolved Sources approved Query validated Evidence returned
Business definitionGross Margin = (Net Sales – COGS)/Net Sales X 100
FiltersWest region + previous completed quarter
FreshnessLatest available approved refresh
Query logicApproved sales, COGS, freight, and returns joined by region and period.
Folio3 connects the approved data, configures the semantic model, validates the agent, and builds the business experience on top.
The governed data pathFolio3 connects the approved data, configures the semantic model, validates the agent, and builds the business experience on top.
Conversational assistants, executive insights, recurring reports, and workflow integrations.
Interpret intent, build the query plan, validate it, and execute against approved data.
Business glossary, metrics, entities, schema relationships, profiles, and sensitivity context.
ERP, CRM, finance, operations, custom SQL, and approved knowledge context.
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The same governed model can support different business roles without forcing every team into the same dashboard.
Revenue, cost centers, budget variance, P&L, margin, and forecast exploration.
Pipeline performance, account trends, regional comparisons, customer activity, and risk signals.
Inventory, service throughput, production trends, supply chain, logistics, and exception analysis.
Cross-functional questions, insights, trend summaries, and decision-ready drill-downs.
“What changed in operating margin, and which cost centers explain it?”
“Which accounts are slipping, and what changed in their activity?”
“Where are service delays increasing, and what do the affected cases have in common?”
“What changed this month across revenue, margin, inventory, and customer risk?”
Start narrow with a high-value data domain, validate real user questions, then expand into more data, agents, reports, and workflows.
Select the initial dataset and define high-value business questions.
Connect approved data and configure the initial semantic model.
Test real questions with a small pilot group and refine the experience.
Broaden the experience with additional data, views, or integrations.
Answers to the questions IT, finance, and business teams raise most often during evaluation.
The four-week roadmap targets an initial working agent around one high-value dataset. It covers discovery, data connection, semantic modeling, and validation with a pilot group. Timing depends on data access, data quality, integration complexity, and the agreed scope; broader rollout follows after the pilot is reviewed.
The starting point is your approved ERP, CRM, finance, operations, SQL databases, and relevant knowledge sources. Discovery identifies the first dataset, the questions it needs to answer, and the access method. Source compatibility and any additional integration work are confirmed before implementation.
The implementation should define approved sources, user permissions, data boundaries, and any sensitive fields before the agent is made available. Access and query rules are configured for the agreed business context and tested during the pilot. Your team should review deployment requirements and access controls before expanding usage.
The agent is designed to return supporting context alongside the result: business definitions, source systems, filters, data freshness, and query logic. A semantic layer gives metrics a shared meaning, while validation checks the query against approved relationships. Pilot testing helps identify gaps, and unclear questions should trigger clarification rather than an unsupported assumption.
Users ask questions in plain English and can follow up to explore a metric, period, region, or account. Technical teams still help connect sources, define metrics, and validate the agent. The experience can complement existing dashboards by supporting questions that need additional exploration, rather than requiring every team to use the same fixed report.
How long until we see a working agent?
Which business systems and data sources can it work with?
How is sensitive data and user access handled?
How can users check whether an answer is trustworthy?
Do business users need SQL skills or a new dashboard?
Start with a live demonstration, then identify the first dataset and business questions worth validating in your environment.
Let’s talk about your data
