Public beta
BacklogHygiene turns messy Jira and Linear activity into cleaner source hygiene, higher-confidence duplicate signals, and reporting operators can actually act on.
Sources
Jira and Linear first.
Slack stays optional on paid delivery flows.
Trial
Starts on your first successful scan.
No fake forever-free tier.
Output
Duplicates, sprint views, strategic reporting.
Escalate into Pro or Team only when value is proven.
Trial begins on first successful scan. Typical setup depends on source health and access readiness.
Illustrative product preview
Mock data for layout demonstration
Signal
High-confidence duplicate preview
Control
Fail-closed access, scoped retention, and clear plan boundaries.
Delivery
Slack delivery remains optional and only appears on paid workflows.
Operating envelope
Real trial window
14 days
Starts on your first successful scan, with a 3-scan ceiling.
Trial and Pro footprint
1 source
Tight self-serve containment before you scale.
Team operating range
3 sources
Built for teams that need more surface area without going enterprise-first.
Strategic reports
12/mo
Paid tiers expand monthly reporting headroom from 4 to 12.
Trusted stack surface
Connect the systems already carrying sprint reality. Jira and Linear lead the workflow. Slack stays available later when you want in-channel delivery.
Primary source system for backlog hygiene and reporting.
Primary source system for backlog hygiene and reporting.
Optional delivery layer on paid workflows.
System design
Instead of another generic AI assistant, BacklogHygiene is structured as a deliberate operating system: clean the source, interpret the motion, then escalate into reporting only when the underlying signal is trustworthy.
Large tile
Surfaces likely duplicates, stale tickets, and structural hygiene gaps before they calcify into sprint drag.
Signal
Catch hygiene drift before it compounds into planning noise.
Drift
Highlight stale or duplicated issue pressure without touching writeback.
Action
Give operators a bounded preview before any paid escalation.
Fast summaries
Turns source activity into sprint-level signal without asking an operator to translate raw issue movement by hand.
Reporting layer
Converts backlog behavior into higher-level reporting with clear quota boundaries instead of vague AI promises.
Operator controls
Plan caps, delivery gates, and source containment stay explicit throughout the product.
Optional delivery
Slack is available when you need it, but it no longer pretends to be the product center of gravity.
Typical setup flow
The goal is not to get you to “AI” as fast as possible. It is to get you from connected sources to useful signal with clear gates at each step.
Step 01
Start with Jira or Linear. Slack is intentionally deferred until you actually want paid delivery.
Step 02
That first completed pass activates the real trial and exposes duplicate preview, hygiene signal, and reporting context.
Step 03
Upgrade into Pro or Team when you need more sources, scheduled scans, larger strategic-report quotas, or optional Slack delivery.
Sticky operator preview
Stage 1: Source truth
Confirm access, source integrity, and workspace readiness before pretending anything intelligent is happening.
Stage 2: Duplicate and hygiene signal
Use bounded previews and contained caps so the trial shows real value without silently overcommitting runtime cost.
Stage 3: Reporting and delivery
Expand into recurring scans, strategic reporting, and optional Slack delivery only when the team has already seen operational payoff.
Security posture
This is a workflow product touching operational backlog data, so the trust story needs to be concrete. The runtime posture is built around explicit access boundaries, scoped retention, and optional delivery scopes.
Operational issue data is encrypted in transit and at rest inside managed infrastructure.
Access checks, request limits, and tenant boundaries are treated as first-class runtime contracts.
Runtime trust map
Source access
Jira and Linear remain the primary access boundary.
Entitlement layer
Plans, caps, and quota state stay explicit throughout the runtime.
Data handling
Retention is scoped to running the product, not training a shared model.
Optional delivery
Slack only enters the picture when the team explicitly enables it.
The product retains the data needed to run scans, quotas, and reports. Customer backlog data is not used to train shared models.
Jira and Linear access come first. Slack scopes only appear when you opt into paid delivery.
Early design partner feedback
“What stood out was the operator discipline. The product does not ask us to believe in magic first. It starts by making the backlog legible.”
Public beta participant
Identity withheld while the design-partner cohort is still private.
The useful part was not the AI theater. It was finally seeing duplicate pressure and stale backlog risk in one place.
Engineering manager
B2B product org, public beta
The trial framing felt honest. We could see the product's value before deciding whether we wanted recurring scans.
Product operations lead
Mid-market software team
Strategic reporting mattered only after the source layer looked trustworthy, which is exactly how the product is staged.
Head of delivery
Platform team, design-partner cohort
Plans, limits, and pricing are loaded from the live backend contract so the landing stays honest.
Closing move
Connect Jira or Linear, complete the first real scan, and let the trial begin on evidence instead of on optimism.
Trial contract
Trial starts on your first successful scan. Pro and Team unlock scheduled scans, more sources, and larger strategic-report quotas.
Start Your First Scan