Product overview

One report. Every proof point ready.

Scopeworth turns delivery signals into invoice evidence, scope visibility, AI impact, and pricing confidence for software agencies and consultancies.

Integrations
Scopeworth · Product demo
8:45
Platform overview walkthrough
02:03
08:45
Product promise

From delivery activity to client ready proof.

Your data is already in Jira, Git, calendars, and delivery notes. Scopeworth turns it into a report clients can understand.

Prove delivery value

Show what shipped, what changed, and which evidence supports the bill.

Explain delivery cost

Connect work, meetings, rework, and rates into a clear cost story.

Protect margins

Spot scope movement and rework before margin disappears.

Forensics

Every shipped item, traced back to a client objective.

Tickets, PRs, reviews, releases, and scope changes, connected and costed. AI agents can accelerate implementation. They do not automatically create a clear account of what changed, why it changed, what it cost, or what the client received in return.

Scopeworth · Project Forensics deep dive
8:42
Deep dive · 8 min 42 sec
00:56
08:42
Problem & fix

The problems Scopeworth is built to fix.

Six reporting breakdowns, each turned into evidence a client can understand.

01

Clients question invoices and timelines

Breakdown

The invoice lands before the evidence does.

Scopeworth fix

Scopeworth gathers work, PRs, releases, reviews, and meetings into one client ready report.

Proof output
31 work items, 23 PRs, 4 releases, and $8,600 in added scope.
02

Scope creep quietly destroys margin

Breakdown

Small requests accumulate until the project is underwater.

Scopeworth fix

Scopeworth flags added work, reopened tickets, and changed acceptance criteria.

Proof output
13 added items and 4 reopened tickets, $8,600 estimated impact.
03

PMs waste hours assembling reports manually

Breakdown

Reporting still depends on copy paste work and memory.

Scopeworth fix

Scopeworth builds the report from delivery signals on a schedule you control.

Proof output
A monthly client report in minutes, not another Friday deck.
04

AI changes delivery economics, but the impact is unclear

Breakdown

Clients ask what AI changed. Most teams answer with guesses.

Scopeworth fix

Scopeworth separates implementation savings from review effort and rework.

Proof output
21 hours saved, 4 hours review overhead, 17 hours net gain.
05

Delivery metrics are disconnected from money

Breakdown

Speed metrics rarely explain cost or commercial value.

Scopeworth fix

Scopeworth connects delivery activity to rates, clients, projects, and outcomes.

Proof output
$3,200 rework cost and $5,100 coordination cost made visible.
06

Reporting trust suffers when data is messy

Breakdown

Gaps in source data make reports easy to challenge.

Scopeworth fix

Scopeworth shows confidence, coverage, and what needs cleanup.

Proof output
Medium confidence with 82% of PRs linked to tickets.
Inside the report

A live view of your client's delivery.

KPIs, signal flow, cost breakdown, and forensic highlights from one client engagement.

app.scopeworth.app/farhaven/command
Scope expansion
$0
13 items added after lock
After Apr 02 scope lock
Delivered work
0
23 PRs · 4 releases
Across 7 client objectives
Estimated delivery investment
$0
+21% vs planned
Includes scope expansion
AI net delivery gain
+0h
26h saved · 7h review
36% AI assisted code
Delivery investment flow

Inputs to cost buckets to client ready output

April 2026 · 1,248 issues · 372 PRs · 96 meetings ingested
Live
Jira tickets1,248 issuesGitHub PRs372 PRsCalendar96 meetingsAI assistedCursor · CopilotReleases4 releasesFeature build$00%Review$00%Rework$00%Meetings$00%Other$00%CLIENT READY OUTPUTClient ready ROI report$0APRIL 2026
Build & review signalsCoordination signalsReworkOtherClient ready output
Cost driver breakdown

April 2026 · estimated investment by activity

Feature build0%
$0
Review0%
$0
Rework0%
$0
Meetings0%
$0
Other0%
$0
What changed this month?

Forensic insights

April 2026
Scope expanded by $8,600 after scope lock
Apr 02 → Apr 26
Clarification meetings increased by 42%
5.5h unplanned coordination
AI work added 7h of review overhead
Net +19h delivery gain
Meeting tagging is the largest data gap
39% of meetings tagged
How it works

How delivery signals become ROI reports.

Four steps from raw activity to a client ready narrative.

01

Connect your tools

Start with Jira, GitHub or GitLab, and Google Calendar. More integrations can be added over time.

02

Map clients, projects, and rates

Define projects, cost bands, delivery phases, and reporting rules.

03

Analyze SDLC activity

Scopeworth links tickets, pull requests, releases, meetings, rework, and AI activity into a unified delivery model.

04

Generate client ready reports

Export reports for client reviews, QBRs, steering meetings, and invoice discussions.

Product modules

Everything needed to explain software delivery ROI.

Use the report on its own, or drill into scope, cost, AI, and evidence when a client asks for detail.

Module 01

Client SDLC ROI Reports

Generate client ready reports with delivery, cost, scope, AI, and action in one place.

Key capabilities
  • Monthly report generation
  • Client and project reporting
  • Report confidence score
Outcome
Use delivery reports in client reviews, QBRs, invoice conversations, and renewal discussions.
Module 02

Scope Creep Intelligence

Identify where scope expanded after work started and estimate the commercial impact of those changes.

Key capabilities
  • Added work detection
  • Reopened ticket signals
  • Cost impact estimate
Outcome
Turn hidden scope creep into visible, defensible client conversations.
Module 03

Delivery Cost Modeling

Estimate delivery investment using rates, project mapping, meetings, rework, and SDLC activity.

Key capabilities
  • Role cost model
  • Client and project mapping
  • Rework and coordination cost
Outcome
Understand where delivery budget goes before margin disappears.
Module 04

AI Delivery Economics

Show how AI work affects delivery speed, review effort, quality, and net savings.

Key capabilities
  • AI work tagging
  • Cycle time comparison
  • Review overhead visibility
Outcome
Explain AI's real contribution without pretending it replaces engineering judgment.
Module 05

Engineering Evidence Graph

Connect tickets, pull requests, commits, releases, meetings, and project mappings into a traceable delivery story.

Key capabilities
  • Jira to Git linking
  • PR and ticket evidence
  • Release evidence
Outcome
Replace vague progress updates with evidence backed delivery narratives.
Module 06

Executive and Client Summaries

Turn complex SDLC signals into clear summaries that leadership and clients can understand.

Key capabilities
  • Narrative summaries
  • Commercial highlights
  • Recommended next actions
Outcome
Spend less time explaining the work and more time acting on it.
For agencies and consultancies

Built around the moments agencies and consultancies actually care about.

Invoices, scope, renewals, and retainers, each backed by the same delivery evidence.

Monthly client reporting

Generate consistent client ready reports without stitching together Jira updates, PRs, meeting notes, and spreadsheets.

Invoice defense

Use delivery evidence to explain why work cost what it cost and what changed during the delivery cycle.

Scope change conversations

Show exactly what was added, reopened, changed, or delayed after scope was agreed.

Retainer reviews

Show how monthly capacity was spent across features, fixes, meetings, rework, and delivery support.

AI delivery explanation

Explain how AI work changed implementation speed, review effort, and net delivery impact.

Margin protection

Identify the hidden delivery costs that reduce profitability before they become a financial surprise.

Invoice defense

When a client asks why, Scopeworth answers with evidence.

app.scopeworth.app/farhaven/invoice
Invoice defense mode

Client question

Asked in last QBR · April 28
From client
“Why is this month’s invoice $8,600 higher than the planned budget?”
Scopeworth answer
April delivery investment expanded by $8,600 after scope lock. 13 work items were added after Apr 02 (COPS‑412..434), 4 tickets were reopened after acceptance criteria changes, and 5 ad‑hoc clarification meetings added 5.5h of unplanned coordination.
+ $5,900 added scope+ $1,500 reopened+ $800 extra review+ $400 coordination
Invoice defense

Why this invoice changed

Planned baseline → actual investment, by line item. Hover any bar for the evidence.
Hover any bar to see what drove the change
$0Planned budget$0Added scope$0Reopened$0Extra reviews$0Coordination$0April investment
Cost drivers

Evidence behind every line

Each row links to the Jira tickets, PRs, and meetings that produced it
Cost driverEvidenceImpactClient explanation
Tickets added after scope lock13 Jira tickets after Apr 02, COPS 412 to 434$0Scope expanded after baseline.
Reopened tickets4 tickets reopened after merge, AC changes$0Late acceptance changes caused rework.
Clarification meetings5 ad hoc meetings, 5.5h. +42% vs baseline.$0Mid cycle scope shifts triggered overhead.
Review overhead3 PRs required 3+ review cycles vs baseline 1$0Larger changesets added review effort.
Trust by design

Delivery proof, not developer surveillance.

Scopeworth focuses on client, project, and team delivery proof. It is not designed to rank engineers, monitor keystrokes, or create review leaderboards.

No individual productivity rankings

Scopeworth focuses on delivery systems, project cost, and client value. Never individual scoring or review leaderboards.

Transparent confidence levels

Reports show where data is strong, where it is incomplete, and how much confidence the report should carry. No hidden gaps.

Commercial clarity without surveillance

The goal is to explain delivery investment and protect agency margins, not create a surveillance layer over engineering teams.

What we collectMetadata first
  • Ticket status (Jira / Linear / Azure DevOps)
  • PR timing & merge events (GitHub / GitLab)
  • Review cycle counts and latencies
  • Release events and deploy markers
  • Meeting metadata (count · duration · attendees)
  • AI assisted commit tags and branch conventions
What we never collectOff limits
  • Keystrokes or screen recordings
  • Webcam, microphone, or screen content
  • Individual productivity rankings
  • Private message content by default
  • Source code content by default
  • Time on task surveillance
AI delivery economics

+19h net delivery gain, with the math behind it.

Faster implementation, larger review effort, human accountability.

app.scopeworth.app/farhaven/ai
AI delivery economics

Cycle time, AI assisted vs baseline

14 day rolling window · hours per work item
4.8h3.6h2.4h1.2h0.0hD1D3D5D7D9D11D13AI assistedBaseline
Trend: AI assisted cycle time fell from 4.1h to 2.0h. Baseline cycle time held near 4.1h.
AI delivery economics

Review overhead by work type

Average review effort vs prior period baseline
UI changes0%0%API changes0%0%Refactors0%0%Bug fixes0%0%Generated tests0%0%
Watch: generated tests and refactors carry the largest review overhead. Tagging AI commits explicitly will improve next month’s confidence.
FAQ

Questions agencies actually ask.

Don't see yours? Send us a message.

Scopeworth helps agencies explain what clients paid for. It creates client ready reports, not internal scorecards.