Context engineering and semantic layer
We map KPIs, definitions, vocabulary, and goal thresholds so answers match how your business speaks and measures.
Generative BI
Generative BI that empowers business users where they work
From messy data to confident decisions in minutes. In recent proofs of value we stood up context-aware workspaces in under two weeks, then generated decision packets in minutes on modeled data, with validations and explanations.
How we work
How it works under the hood
The technical architecture that makes reliable Generative BI possible.
Deterministic text to SQL
The agent generates one clear query with safeguards and evidence columns.
Validation first
Totals, joins, filters, and time windows are checked before results are delivered.
Telemetry for speed
Instrumentation accelerates onboarding and helps us improve answer quality over time.
Scenarios in minutes
What-if comparisons show the tradeoffs between time-optimized and cost-optimized plans.
Governance built in
Results reflect the data connected to your workspace and the KPIs defined in your model. When detail is missing the agent calls it out and suggests the next step.
In practice
Day in the life
- Operations manager
- Reviews overall equipment effectiveness by line and shift, sees the top drivers, and compares a time-optimized and a cost-optimized recovery plan. Sets an alert if the metric trends toward a breach.
- Supply chain lead
- Checks stockout rate by SKU and location with revenue impact. The agent explains why items are short with evidence across forecast error, late advance ship notices, and quality holds. A replacement plan is proposed with coverage ratios.
- Revenue and sales
- Audits contract price realization by account and SKU, identifies gaps, estimates recovered value, and recommends the next best offer that protects margin based on buying patterns.
- What the answer looks like
- A ranked table with evidence columns and a two-plan comparison that shows timing, cost, expected lift, and the KPI path back to goal.
What we do
What Generative BI means at Beye
Six core principles that make our platform uniquely powerful for business users.
Faster decisions
Work that once took days to collect and model can now be explored in minutes. Beye turns questions into answers and scenarios so teams move sooner with confidence.
Built for business users
Ask in plain language and the agent follows up, remembers context, and stays in your workflow. Each interaction builds on goals and KPIs, saving time for real decisions.
Context that grows with you
We fine-tune a semantic layer with your terms, acronyms, and KPIs. As questions repeat, context sharpens so answers align with how your business measures success.
Agents do the preparation
Beye harmonizes messy files, builds SQL or Python, validates steps, and explains results with evidence. Scenario comparisons run in minutes, lowering costs and widening options.
Reliability by design
A decision-gate architecture selects the right model and runs checks before showing results. Answers include evidence columns and highlight missing data with clear next steps.
Human in the loop
Our team partners with yours on education and change management. We refine context, guide adoption, and help users craft better questions so value grows over time.
Methodology
Methodology and ownership
How we work together to ensure successful implementation and adoption.
What you bring
- One or two high-impact use cases that move the business
- Initial data sources in scope, such as exports from your systems or Excel and CSV
- How your teams talk about the problem and the questions they want answered
- Confirmation of KPI formulas, goal thresholds, and ownership
What Beye delivers
- An AI-ready database that harmonizes disparate, messy data into one place
- A context-aware semantic layer fine-tuned to your use cases and vocabulary
- A workspace pre-seeded with validated views, saved prompts, and executive summaries
- Decision-gate checks and evidence columns for reliability
- Telemetry and human-in-the-loop tuning for continuous improvement
- Change management and education so teams adopt and stay engaged
Typical timeline
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Day 1–3
Align on use case, KPIs, goals, and data handshake.
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Day 4–7
Context engineering, semantic model, workspace provisioned, first answers for review.
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Day 8–14
Validations, anomaly checks, saved views, and what-if scenarios at different risk levels, rollout plan.
Timing depends on scope and data readiness.
Question structure
How to ask great questions
Include a measure, a breakdown, and a time window. Add filters when you can.
Measure+ Breakdown+ Time window+ Filters (optional)
Example
Inventory management
“Stockout rate and revenue impact by SKU and region this week with the top reasons and a recovery plan”
Example
Financial analysis
“Gross margin by product category for last quarter versus prior quarter with bottom movers and drivers”
Example
Collections & AR
“Days sales outstanding trend by customer segment this month and the list to prioritize for collections”
Pro tip: when a question is vague the agent will ask one clarifying question and proceed.
Why Beye
Why mid-market teams choose Beye
Five key advantages that make Beye the right choice for growing businesses.
Speed to value
Go from messy files to a working setup in days and rollout in two weeks.
Reliability by design
Validations and evidence make answers explainable and trustworthy.
Lower total cost of ownership
Agents absorb data preparation and first-pass analysis so teams focus on decisions.
Education and guidance
We meet users where they are and reduce the friction of getting started.
Level the playing field
Capabilities once for enterprises are now accessible to SMB and mid-market teams.
Quick connect
Ready to transform your analytics?
Join leading mid-market companies using Beye to turn data chaos into decision-ready insights in seconds.