Crystal Reports → Power BI · AI-Accelerated

Migrate off Crystal.De-risk it. Ship faster. Spend less.

Move your Crystal Reports estate to Power BI on the Azure stack you may already own or are planning to build about 40% faster and 48% less effort than manual conversion, with 100% validation coverage and every output approved by a named engineer.

Runs inside your Azure tenant 100% automated output diff Named engineer on every report
everlign / Crystal → Power BI
LIVE
Bottom line for a 120-report scope
Timeline19 wks faster
Effort1,885 hrs saved
Cost$179,094 saved
Live ROI Model

Run your own scope. See the delta instantly.

Set your report count, hours per report, and blended rate. The model compares a fully manual conversion against Everlign's AI-accelerated method across timeline, effort, and cost.

everlign / Crystal → Power BI LIVE MODEL

Your scope

Adjust the inputs to match your reporting estate.

$
Bottom line for your scope
19 weeks faster
·  1,885 fewer effort hours  ·  $179,094 saved
Manual conversion timeline48 weeks
29 wks
saved
AI-accelerated (Everlign) Time removed
Timeline
Traditional48 wks
AI-accelerated29 wks
Faster by40%
Effort
Traditional3,900 hrs
AI-accelerated2,015 hrs
Less effort48%
Cost
Traditional$370,500
AI-accelerated$191,406
You save$179,094

Where the hours go

Effort breakdown by phase (person-hours) for your scope.

PhaseTraditionalAI-accel.Reduction

All figures are indicative estimates derived from Everlign's AI-accelerated methodology and comparable engagements. The full conversion pipeline runs inside your Azure tenant no report logic, schema, or data leaves your environment. Every converted report is reviewed by a named BI engineer and passes an automated Crystal-vs-Power BI output diff. Confirmed numbers are produced after the fixed-scope Phase 1 assessment.

The Method

Three things AI changes.

The gap isn't "AI does it faster." It's structural: a one-time foundation feeds repeating 2-week sprints, each shipping a wave of reports to production drafted by AI, approved by an engineer, validated end to end.

Automated discovery
Auto
Automated .rpt scanning, SQL and formula parsing, and dependency mapping across every report replacing weeks of manual reading.
Discovery cut from ~7 weeks to ~4.
INVENTORY SCAN120 / 120
SalesByRegion.rpt14 formulas · 3 subreportsPARSED
InventoryAging.rpt9 formulas · 1 subreportPARSED
OpsDailyKPI.rpt22 formulas · cross-tabPARSED
AI-drafted conversion
HITL
An LLM drafts the DAX, SQL, and layout. Correctness, performance, and business meaning stay with a named BI engineer no output reaches production unapproved.
Conversion cut from ~20 to ~11 hrs / report.
SPRINT 3 · WAVE18 / 20
SalesByRegion → DAXreviewed by A. MehtaAPPROVED
OpsDailyKPI → DAXfilter-context checkIN REVIEW
InventoryAging → DAXdraftingAI DRAFT
100% validation diff
100%
Every report passes an automated Crystal-vs-Power BI output diff plus business UAT full coverage instead of the sample-based spot checks manual projects settle for.
Validation moved from sampling to 100% automated diffing.
OUTPUT DIFFmatch rate
SalesByRegion1,204 rows · 18 measures100% MATCH
InventoryAging860 rows · 9 measures100% MATCH
OpsDailyKPIflagged · roundingREVIEW
Phase 1 Assessment

See your Crystal estate's migration plan in 30 minutes.

A fixed-scope assessment scans your actual .rpt inventory and hands back a confirmed timeline, effort, and cost - no guesswork, no commitment to the full build.

Automated .rpt inventory scan

Every report parsed for formulas, subreports, and data dependencies.

Confirmed effort, timeline & cost

Your real numbers, replacing the indicative model above.

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