7
Registered sources
Technical to commercial
From evidence to investment conviction
A stronger product. A testable plan.
Could a reliability-first investment make Northstar’s workflow platform more competitive? Explore the evidence behind the case.
How this sample works
Start with the assessment, follow a finding back to its evidence, then adjust a local planning assumption. Real workspaces store company data behind authenticated APIs. This separate demo only displays fictional examples; it does not create an identity or read a company database.
24
Evidence records
Traceable sample observations
9
High-risk findings
Including provisional code concerns
12
Findings
6 reviewed · 6 provisional
21
Proposed initiatives
80 engineer-months of full scope
Investment thesis under review
Northstar Systems
Technology Due Diligence
Stabilize the core platform, reduce support effort, then test AI assistance against measured customer outcomes.
Explore the evidenceSee the technical investment case inside the code
Source flaws → architectural decisions → prioritized work
Explore an illustrative AI review with file/line evidence, six provisional findings and twelve architectural, functional and code-change proposals. See those tasks in the complete plan and model what fixing them would cost.
Explore source & architecture review →Prewritten synthetic review, not a live scan. Real repository code ingestion is a remaining build-out, not demonstrated by this tour.
Two products. One investment decision.
Fictional portfolioAtlas Core · Operational workflow
Protect the foundation before adding AI.
Case management, imports and customer history share a mature service and relational database. Recovery depends on manual steps; the deployment pipeline has limited regression coverage.
- Investment question
- Can reliability and lower support friction protect the installed base?
- Evidence gap
- Production-like restore results and measured incident handling time.
Northstar Insights · Reporting & analytics
Test value, not a longer feature list.
A reporting service reads Atlas data. Slow reports and interest in AI summaries suggest opportunity, but neither customer retention impact nor willingness to pay is established.
- Investment question
- Will faster reporting and a bounded AI workflow justify additional spend?
- Evidence gap
- Representative load tests, controlled customer pilot and pricing research.
Sample investment committee brief
Conditional case, not automatic approval
Protect
Close privileged access and recovery gaps before broad feature delivery.
Prove
Measure support effort and release reliability before claiming savings.
Grow
Fund reporting and AI experiments only after foundational gates pass.
All facts are invented. Return, valuation uplift and revenue projections remain excluded. See 21 evidence-linked initiatives →
What should we check next?
Challenge the case, not just the documents.
- Can the platform recover reliably?Compare the recovery claim with the test result.
- Will automation reduce support cost?Quantify repeat incidents before projecting savings.
- Does AI create a defensible advantage?Test willingness to pay; a feature list is not proof.
AI assists. Your team decides.
No live AI runs in this demo.
Real workspaces use a company-configured provider. Check the scope and provider pricing before starting a live evaluation. API charges are separate; drafts need human review.
Why is AI disabled in this demo?
This public demo has no AI credentials or connection to the application. It uses illustrative, prewritten content—not a live evaluation. Live analysis requires your authenticated workspace and a company-configured provider; API charges are separate.
No provider requests run when you browse or adjust this demo.
Evidence Room
A source is where material comes from. An observation is a reviewable statement drawn from that material.
Follow the evidence
Expand a source to inspect its fictional observations. Findings link back to these exact examples. Uploading and connector setup belong in your authenticated workspace; neither runs here.
Sources & reviewed observations
7 sources · 24 observationsRecovery test reportSample technical document · 2 accepted observationsTechnical
E1 · Recovery target
“The operating guide targets recovery within 30 minutes.”
This is a stated target, not proof that recovery meets it.
E2 · Observed recovery test
“The latest staging recovery exercise took 95 minutes and required manual repair.”
Fictional measurement from the sample report. Test production parity before generalizing.
See linked recovery finding →Support reviewSample incident export · 2 accepted observationsOperational
E3 · Repeated issue category
“Thirty of fifty sample tickets relate to failed imports.”
The sample points to a recurring problem. It does not establish annual cost.
E4 · Support effort is not measured
“Resolution-time and staff-effort fields were not included in the export.”
Measure handling time before projecting a financial saving.
See linked support finding →Customer discovery notesSample interviews · 2 accepted observationsCommercial
E5 · Interest in assistance
“Four interviewees asked for help summarizing long case histories.”
Interest suggests a pilot question; it is not committed revenue.
E6 · Pricing remains untested
“No willingness-to-pay or comparison with alternative AI tools was recorded.”
A differentiated workflow and measurable outcome need verification.
See linked AI pilot finding →Access-control reviewSample control assessment · shared platform · 2 observationsSecurity
E7 · Privileged roles are too broad
“Three operations roles can change tenant configuration; two use shared service identities.”
Fictional control gap. Separate human and machine access; verify effective permissions rather than inferring a breach.
See the access finding →E8 · Audit coverage is incomplete
“Role-change events are retained; bulk exports are not consistently attributed to an actor.”
Extend attribution and run negative authorization tests. This sample cannot establish a certification or compliance conclusion.
Repository & release reviewSample commit and CI notes · Atlas Core · 2 observationsEngineering
E9 · Release gates miss critical workflows
“The import recovery path has no automated regression test; two of six sample releases needed a corrective patch.”
Small invented sample, not an annual failure rate. Review commit ownership, test depth and deployment controls.
Inspect the release finding →E10 · Migration recovery is not rehearsed
“Schema changes are versioned, but rollback instructions have not been tested with a production-sized copy.”
Verify forward recovery and a restore rehearsal; a code rollback alone does not reverse schema or customer writes.
Observability & performance notesSample operational review · both products · 2 observationsPerformance
E11 · Customer journeys lack end-to-end visibility
“Service uptime is monitored, but import completion and report freshness have no end-to-end objectives.”
Define journey-level metrics before claiming reliability improvement or reduced support load.
Inspect the measurement finding →E12 · Report latency needs representative testing
“A sample account with a large history returned a report in 18 seconds; the target is below 5 seconds.”
One synthetic test does not generalize to every tenant. Benchmark query plans, workload distribution and cold-cache behavior.
Illustrative source-code & architecture snapshotInvented repository revision N4 · 11 file paths · 12 observations · not a live GitHub importSource code
These simplified excerpts are invented, omit surrounding code and never execute. File/line references identify this synthetic snapshot only. Open the illustrative AI review →
E13 · Tenant query has no visible organization predicate
api/cases.ts:42–47 · synthetic revision N4
return db.one('SELECT * FROM cases WHERE id = ?', [caseId]);The excerpt does not bind a tenant. Upstream guards are omitted: this is a testable boundary hypothesis, not proof of customer exposure.
E14 · Cache key omits tenant and permissions
lib/cache.ts:10–16 · synthetic revision N4
const key = `report:${reportId}`;
return cache.get(key);A shared key could collide across tenants or reuse a result after a permission change. Validate cache namespace and invalidation.
E15 · Import processing runs inside one request
jobs/import.ts:28–36 · synthetic revision N4
for (const row of input.rows) {
await processRow(row);
}
return { state: 'complete' };Batch size, checkpoints and restart behavior are not visible. Measure timeouts and design bounded tenant-owned jobs.
E16 · Retry path writes without an operation key
jobs/import.ts:38–43 · synthetic revision N4
await db.insert('imported_records', record);
return { accepted: true };No replay guard is visible in this excerpt. Confirm uniqueness constraints before concluding retries duplicate records.
E17 · API coerces input without a schema check
api/import.ts:21–27 · synthetic revision N4
const requestedRows = Number(body.rows);
return startImport(requestedRows);Reject invalid, non-finite and oversized input before enqueueing or writing. The omitted gateway may validate some inputs.
E18 · Client and server use different status names
shared/contracts.ts:8–15 · synthetic revision N4
// Client: queued | complete
// Server: pending | succeeded
// No shared contract in the sampleA stale client may misreport completion. Check actual serialized responses and version rollout; static mismatch alone does not establish an incident.
E19 · Report performs a query per record
reports/summary.ts:32–39 · synthetic revision N4
for (const item of cases) {
totals.push(await loadItemTotals(item.id));
}Likely N+1 hot path. Join this source clue to the report-latency observation and test representative tenant data.
E20 · Record API returns an unbounded list
repos/cases.ts:18–22 · synthetic revision N4
return db.all('SELECT * FROM cases WHERE tenant_id = ?', [tenantId]);Tenant predicate is present, but no cursor or page bound is visible. Test large histories and memory/latency limits.
E21 · Evidence deletion has no visible retention gate
storage/evidence.ts:51–56 · synthetic revision N4
await evidenceStore.delete(objectKey);
return { removed: true };No policy, legal hold or actor attribution is visible here. Review access checks and audit middleware before making a compliance claim.
E22 · Provider credential loader depends on source configuration
config/provider.ts:5–11 · synthetic revision N4
const credential = loadEmbeddedProviderConfig();
export const provider = makeProvider(credential);No credential value is shown or supplied. Validate whether the placeholder loader represents an embedded secret; use managed references and rotation.
E23 · Import UI lacks progress and cancellation
web/import-status.ts:18–24 · synthetic revision N4
render(job.state === 'complete' ? 'Done' : 'Working');This simplified UI leaves no progress, retry explanation or cancel action. Cross-check the support pattern and user workflow.
E24 · AI tests assert output existence, not quality
tests/ai-quality.test.ts:12–18 · synthetic revision N4
expect(result.text.length).toBeGreaterThan(0);
// No citation, factuality or task-cost acceptance gateOutput existence does not establish correctness or economics. Add a versioned evaluation set before productizing assistance.
No private files in this sample. Uploads and live GitHub/Jira connections require an authenticated company workspace.
Analysis & Findings
Look beyond a summary: inspect the evidence, the reviewer’s conclusion and the next verification step.
Findings outside Source & Architecture. These may still describe technical issues; all findings feed the same reviewed plan.
Review, don’t assume
These are prewritten sample findings, not AI output or investment advice. Each has linked observations and an explicit unknown. Real reviewers accept or challenge drafts; the demo cannot approve or save decisions.
All findings, Source & Architecture and Other findings are views of this same assessment. Source recommendations feed the same ranked plan; Other findings excludes the six source findings; the counts partition the complete twelve-finding register. Switching views never changes approval or starts AI.
Privileged access needs a tighter boundary
Critical concernBroad privileges and shared identities undermine accountability across both products. Treat this as a control-remediation priority, not proof of an incident.
- Reviewer conclusion
- Supported control gap · sample reviewed
- Confidence / limit
- Moderate; actual permission graph and negative tests remain unverified.
- Acceptance gate
- Named identities, least-privilege roles, export attribution and cross-tenant denial tests.
- Suggested owner
- Security lead · shared platform
Recovery reliability needs proof
High riskThe stated 30-minute target conflicts with the 95-minute exercise. A reliability-first initiative is justified, subject to production-parity testing.
- Reviewer conclusion
- Supported concern · sample reviewed
- Confidence / limit
- Moderate; staging exercise may not reflect production data or load.
- Acceptance gate
- Repeat a production-like restore and demonstrate the recovery target.
- Suggested owner
- Engineering lead · Atlas Core
Release confidence is not demonstrated
High riskCritical workflow tests and migration rehearsals are missing. More feature throughput would compound this risk without stronger delivery gates.
- Reviewer conclusion
- Supported delivery concern · sample reviewed
- Confidence / limit
- Moderate; six releases are too few to estimate a long-run failure rate.
- Acceptance gate
- Critical-path regression suite and production-sized migration recovery rehearsal.
- Suggested owner
- Delivery lead · Atlas Core
Repeated imports may drive support burden
High riskThe incident mix suggests an opportunity, but missing staff-effort data prevents a defensible savings estimate.
- Reviewer conclusion
- Supported pattern · financial impact unverified
- Confidence / limit
- Low on economics; no annual incident volume or measured handling cost.
- Acceptance gate
- Measure handling time and validate fewer repeat tickets after an import fix.
- Suggested owner
- Support lead · Atlas Core
Measurement gaps weaken the improvement case
Medium concernUptime alone misses failed imports and slow reporting. Journey instrumentation should precede performance optimization and benefit claims.
- Reviewer conclusion
- Supported measurement gap · performance impact provisional
- Confidence / limit
- Low on generalization; one report account and incomplete end-to-end telemetry.
- Acceptance gate
- Journey objectives, representative load profiles and before/after measurements.
- Suggested owner
- Platform lead · both products
AI assistance warrants a bounded pilot
OpportunityInterest exists, but neither incremental value nor defensibility is established. Pilot a measurable workflow before committing to a broad AI roadmap.
- Reviewer conclusion
- Inconclusive · commercial evidence missing
- Confidence / limit
- Low; four interviewees are not a committed market or revenue forecast.
- Acceptance gate
- Compare task accuracy, time saved, provider cost and willingness to pay.
- Suggested owner
- Product lead · both products
Tenant boundaries may leak through queries and cache
Critical · provisional AI hypothesisCentralize tenant-scoped data access and partition caches; preserve product behavior while proving isolation.
- Confidence / limit
- 85% illustrative confidence. Upstream middleware, shared cache namespace and effective permissions have not been executed or verified.
- Review decision
- Needs human review; no approval or incident conclusion.
- Recommended work
- Enforce tenant-scoped data access · Partition and invalidate cached results
Synchronous imports lack safe restart boundaries
High · provisional AI hypothesisUse bounded asynchronous jobs, replay-safe writes and operator progress/cancellation.
- Confidence / limit
- 80% illustrative confidence. Queue, database constraints and concurrency configuration may alter the behavior inferred from excerpts.
- Review decision
- Needs human review; no approval or incident conclusion.
API and client contracts can drift
High · provisional AI hypothesisShare validated request/response schemas and enforce compatibility tests in delivery gates.
- Confidence / limit
- 85% illustrative confidence. Gateway validation and client version distribution are not shown.
- Review decision
- Needs human review; no approval or incident conclusion.
- Recommended work
- Validate API input at the boundary · Unify API contracts and regression tests
Large histories strain synchronous reporting and list APIs
Medium · provisional AI hypothesisBound lists with cursors and materialize reports incrementally; measure latency and freshness.
- Confidence / limit
- 75% illustrative confidence. A static hot-path pattern is not a benchmark or proof of customer latency.
- Review decision
- Needs human review; no approval or incident conclusion.
- Recommended work
- Page large customer record lists · Build incremental reporting views
Private evidence lifecycle lacks visible policy enforcement
High · provisional AI hypothesisDefine tenant-scoped retention, legal holds, deletion authorization and audit evidence.
- Confidence / limit
- 70% illustrative confidence. Omitted authorization/audit wrappers and contractual retention rules require review; no compliance certification is implied.
- Review decision
- Needs human review; no approval or incident conclusion.
- Recommended work
- Define evidence retention and deletion
Source-coupled credentials need a managed boundary
Critical · provisional AI hypothesisReplace embedded configuration dependency with managed secret references and a rotation rehearsal.
- Confidence / limit
- 85% illustrative confidence. The illustrative loader contains no secret value. Confirm real credential provenance rather than assuming a leaked key.
- Review decision
- Needs human review; no approval or incident conclusion.
- Recommended work
- Remove embedded credential loading
Analysis & Findings
One assessment, one reviewed finding register and one ranked plan.
Source & Architecture Review
Explore how AI-assisted review connects code-level concerns, architectural constraints and functional improvements to the same evidence-linked, prioritized plan.
How source review becomes a plan
This is prewritten, illustrative AI output from invented snippets—not a live scan, a security audit or a promise of exhaustive repository coverage. Follow each file/line observation to a provisional finding and a ranked task. Human reviewers must reproduce problems, challenge the interpretation and approve work. All proposed work is included in the fixed sample Investment Lab scope; adjusting assumptions never approves a task.
The current live GitHub connector retains repository tree and recent commit metadata, not source-file contents. Bounded private-source AI review is implemented locally in the real Evidence Room; automatic whole-repository ingestion still requires further implementation and validation. This demo shows the intended workflow without source uploads or provider charges.
This specialized view sits inside Analysis & Findings. All findings includes these six provisional source findings alongside six Other findings. Counts refer to findings, not tasks; these groups share one register. Overall priority refers to the shared plan, not a separate technical backlog. Follow Why this priority? to inspect the score and rationale.
Illustrative AI review · no model was run. Synthetic revision N4, invented source excerpts and history. Potential flaws require runtime validation; no customer systems or repository were accessed. The real product's local implementation supports bounded private-source reviews, including architecture inferred from code. Direct GitHub source ingestion is not yet implemented: connector snapshots contain metadata only.
Inferred architecture · illustrative, not verified
Two products share critical boundaries
- Atlas web clientCase workflows and imports
- Shared API / import serviceTenant access, contracts and long jobs
- Relational store + cacheTenant records and cached summaries
- Insights reportingReads shared data; repeated query loops
Private evidence storage and the AI adapter add retention and credential boundaries. Component relationships are inferred from simplified sample paths; deployment topology and runtime behavior are unknown.
What the review examines
- Product and trust boundariesTrace tenant queries, cache keys, credential loading and evidence deletion.
- Behavior and scalabilityFollow import retries, unbounded lists, report queries and operator feedback.
- Delivery confidenceCross-check API contracts, CI gaps and AI quality gates against documents and incidents.
- Actionable proposalsRecommend architectural, functional and code changes with estimates, acceptance tests and priority rationale.
Illustrative evolution and blind spots
- N1 · Core launchSynchronous imports and shared API contracts.
- N2 · Larger customersRetry logic added; replay behavior remains untested.
- N3 · Reporting expansionNew cache and report paths; tenant partitioning needs proof.
- N4 · AI pilotAdapter added before factuality and task-cost gates.
Invented milestones—not real commit dates or ownership judgments. Full history, transitive dependencies, infrastructure and runtime tests are not supplied.
Findings traced back to source
Inspect the file/line evidence →Tenant boundaries may leak through queries and cache
Centralize tenant-scoped data access and partition caches; preserve product behavior while proving isolation.
Upstream middleware, shared cache namespace and effective permissions have not been executed or verified.
Synchronous imports lack safe restart boundaries
Use bounded asynchronous jobs, replay-safe writes and operator progress/cancellation.
Queue, database constraints and concurrency configuration may alter the behavior inferred from excerpts.
API and client contracts can drift
Share validated request/response schemas and enforce compatibility tests in delivery gates.
Gateway validation and client version distribution are not shown.
Large histories strain synchronous reporting and list APIs
Bound lists with cursors and materialize reports incrementally; measure latency and freshness.
A static hot-path pattern is not a benchmark or proof of customer latency.
Private evidence lifecycle lacks visible policy enforcement
Define tenant-scoped retention, legal holds, deletion authorization and audit evidence.
Omitted authorization/audit wrappers and contractual retention rules require review; no compliance certification is implied.
Source-coupled credentials need a managed boundary
Replace embedded configuration dependency with managed secret references and a rotation rehearsal.
The illustrative loader contains no secret value. Confirm real credential provenance rather than assuming a leaked key.
Prioritized, not automatically approved
Sample score = impact × 3 + risk reduction × 3 + confidence − min(100, rounded likely hours ÷ 16). Each engineer-month assumes 160 hours for this illustrative work breakdown. The same deterministic ordering aid is used by the current product; the AI can propose inputs but cannot approve work.
Risk reduction is a planning judgment, not a probability of loss. ROI/revenue remain unvalidated. Dependencies, overlap and estimates need engineering review; a high score is not permission to deploy.
12 source-led changes in the overall plan
Model the complete 21-initiative plan →Overall priority 1 · Code · Shared platform
Enforce tenant-scoped data access
Replace unconstrained lookups with tenant-bound repositories and negative authorization tests.
Impact 100/100 · risk reduction 95/100 · Source evidence
Overall priority 3 · Code · Shared platform
Remove embedded credential loading
Use a managed secret reference with rotation and least-privilege access, not values read from source.
Impact 85/100 · risk reduction 90/100 · Source evidence
Overall priority 4 · Code · Atlas Core
Validate API input at the boundary
Reject malformed payloads before business logic and return stable typed errors.
Impact 86/100 · risk reduction 82/100 · Source evidence
Overall priority 5 · Architecture · Shared platform
Partition and invalidate cached results
Include tenant and input version in cache keys and define explicit invalidation semantics.
Impact 85/100 · risk reduction 87/100 · Source evidence
Overall priority 6 · Architecture · Atlas Core
Make import writes replay-safe
Introduce operation keys and atomic replay guards so retrying a job cannot duplicate customer records.
Impact 90/100 · risk reduction 85/100 · Source evidence
Overall priority 9 · Architecture · Atlas Core
Move long imports into bounded jobs
Split synchronous batches into checkpointed work with bounded retries, cancellation and tenant ownership.
Impact 86/100 · risk reduction 80/100 · Source evidence
Overall priority 12 · Code · Shared platform
Unify API contracts and regression tests
Share request/response schemas and add producer-consumer compatibility tests.
Impact 78/100 · risk reduction 75/100 · Source evidence
Overall priority 13 · Architecture · Shared platform
Define evidence retention and deletion
Add a reviewed retention policy and auditable tenant-scoped deletion workflow with legal-hold checks.
Impact 75/100 · risk reduction 82/100 · Source evidence
Overall priority 14 · Functional · Atlas Core
Page large customer record lists
Provide bounded cursor pages and explicit load-more behavior rather than returning all records.
Impact 80/100 · risk reduction 55/100 · Source evidence
Overall priority 16 · Functional · Northstar Insights
Build incremental reporting views
Use tenant-owned incremental aggregates and expose freshness rather than repeating per-record query loops.
Impact 84/100 · risk reduction 48/100 · Source evidence
Overall priority 17 · Functional · Atlas Core
Expose import progress and safe cancellation
Show job progress, retry state and cancellation in the product without permitting conflicting edits.
Impact 72/100 · risk reduction 50/100 · Source evidence
Overall priority 19 · Code · Atlas Core
Add an AI quality and cost evaluation gate
Create a versioned evaluation set for factuality, citations, sensitive-data handling and per-task cost before releasing AI assistance.
Impact 72/100 · risk reduction 40/100 · Source evidence
Source-led work is combined with recovery, operational, customer and commercial initiatives—not kept in an isolated AI report. Inspect all 21 prioritized initiatives and delivery gates →
From diligence to a funding decision
Investment Lab
What does the existing plan cost? What changes if you fund a larger team or assume AI-assisted delivery? Compare planning assumptions against the balanced baseline without changing the initiatives.
Understand the cost model
Planning timeframe is the first adjustable assumption. Expand What team would meet this timeframe? (closed by default) for estimated completion time, delivery cost and the preview button. The full-timeframe team budget stays in the main summary for the currently selected team. Opening or closing it does not change your assumptions. The team estimate searches for the smallest whole team up to 100 engineers that fits the timeframe, including coordination overhead. Preview this team applies it only to this unsaved preview; impossible timeframes are disclosed. Exact inputs keep keyboard arrows and typing; duplicate spinner arrows are hidden. All 21 initiatives and their effort come from the existing plan and are read-only. Click a planning assumption for −/+ controls or an exact whole number. Apply value sets both the value and slider maximum, even when reducing it; dragging keeps that maximum. Expand range enlarges it without changing the value. Displayed safety limits reject unsupported values without changing the case. Reset or reload restores the baseline and initial ranges; no edits are saved.
Human capacity = people ÷ (1 + 0.04 × people above four). Delivery capacity then applies 80% delivery allocation and assumed AI uplift. The 4% coordination drag per additional person is a planning heuristic, not measured productivity. Larger teams deliver faster but less than proportionally and incur more engineering cost. Base delivery months = plan engineer-months ÷ delivery capacity. Engineering cost = base months × team × fully loaded monthly cost; AI allowance = base months × monthly AI budget; contingency increases both cost and duration.
The “Team budget for N months” label updates with the selected timeframe. Team budget = (team × monthly cost + monthly AI budget) × horizon. It is an alternative view of the same spend, not an additional cost. One shared team delivers sequentially; multiple squads, onboarding, dependencies, taxes and investment returns are excluded. AI uplift defaults to zero and must be validated; the allowance is not provider pricing or a charge.
Each starting-point card shows how many initiatives fit within its own funding horizon, calculated from that preset’s settings. Choosing a preset makes the live summary match it. Slider changes customize the live summary, not the preset cards. The top summary shows how many whole initiatives finish within the selected funding horizon, out of the fixed 21-item plan. It updates with capacity, contingency and horizon assumptions using the same sequential schedule as the roadmap below. Initiatives beyond the horizon remain in the plan; the count is a capacity projection, not a recommendation to drop work. Priority order and effort determine which whole initiatives finish; dependency scheduling is not modeled.
Starting points use their own time horizons. Choosing one restores its settings; sliders customize the live results below. All 21 initiatives remain in the plan.
- Projected within timeframe
- — —
- Delivery cost
- —
- Team budget for 12 months
- —
- Delivery time
- —
Calculating projected completion…
Six planning assumptions
Adjust the assumptions
Click a planning assumption for −/+, exact entry or an expanded slider range. Plan scope and underlying effort are read-only.
Initiatives in this plan21 initiatives · fixed plan scope
Defined by the existing value-creation plan, not this simulator.
What work do these estimates cover?
This synthetic sample uses legacy estimates without separate verification and implementation scope. Follow-on work has not been assessed; additional cost is unknown and is not included. Completing a verification task does not prove that the underlying risk is resolved. In the product, reviewers can record work scope, included assumptions and unestimated follow-on for each initiative before capturing a new investment plan. Verification uses decision value, not delivered remediation credit; a positive delivered-risk-reduction score must be reviewed before approval. These sample scores have not been reclassified or recalculated.
Planning timeframe12 months
Allowed: 1–120 months. Apply sets the value and slider maximum; dragging keeps that maximum.
Slider range: 1–24
Changes the timeframe and team budget, not the delivery speed or cost to complete the whole plan.
What team would meet this timeframe?
Calculating…
Uses current effort, AI, allocation, contingency and coordination assumptions, not a staffing commitment. Recruiting, onboarding and dependencies are not modeled. Only Preview this team changes the local team control; nothing is saved.
Engineering team4 engineers
Allowed: 1–100 engineers. Apply sets the value and slider maximum; dragging keeps that maximum.
Slider range: 1–12
Changes monthly burn, retained-team budget and delivery time.
Monthly cost per engineer$12,000 / person
Allowed: 1–100,000 USD. Apply sets the value and slider maximum; dragging keeps that maximum.
Slider range: 1–24000
Fully loaded USD planning assumption, including employer overhead.
Assumed AI capacity uplift0% assumed uplift
Allowed: 0–100 percent. Apply sets the value and slider maximum; dragging keeps that maximum.
Slider range: 0–50
Hypothetical extra capacity, not a measured saving or live AI run.
Delivery contingency20% reserve
Allowed: 0–100 percent. Apply sets the value and slider maximum; dragging keeps that maximum.
Slider range: 0–50
Monthly AI allowance$1,000 / month
Allowed: 0–100,000 USD. Apply sets the value and slider maximum; dragging keeps that maximum.
Slider range: 0–5000
Hypothetical delivery-tool allowance; not provider pricing.
Cost to deliver selected work
—
Baseline comparison
—
—
—
- Engineering before contingency
- —
- AI allowance before contingency
- —
- Delivery contingency
- —
- Total delivery investment
- —
80% delivery allocation; no measured AI gain assumed.
Amounts are rounded to dollars; totals use unrounded values and fractional months.
Team budget for 12 months
—
Cost of funding the whole team and AI allowance for this timeframe, even if some initiatives finish later.
—
- Monthly team + AI burn
- —
Not added to delivery investment: these are alternative views of the same spend. The plan scope stays fixed; team size, rates, allowance and funding horizon change this budget.
—
—
—
What your budget is funding
Open the detailed roadmap →Whole initiatives, not a percentage of every feature. Dates below are relative model months, not commitments.
- 1. Enforce tenant-scoped data access—
- 2. Close privileged-access gaps—
- 3. Remove embedded credential loading—
- 4. Validate API input at the boundary—
- 5. Partition and invalidate cached results—
- 6. Make import writes replay-safe—
- 7. Prove recovery reliability—
- 8. Rehearse schema migration recovery—
- 9. Move long imports into bounded jobs—
- 10. Instrument critical customer journeys—
- 11. Add release confidence gates—
- 12. Unify API contracts and regression tests—
- 13. Define evidence retention and deletion—
- 14. Page large customer record lists—
- 15. Prevent repeat import incidents—
- 16. Build incremental reporting views—
- 17. Expose import progress and safe cancellation—
- 18. Speed up customer reporting—
- 19. Add an AI quality and cost evaluation gate—
- 20. Pilot AI case-history assistance—
- 21. Validate paid AI packaging—
Scenario settings live only in this page’s memory. Reload restores the sample. No budget is saved and no charge is incurred.
Value-Creation Roadmap
A sample sequence from operational risk to a measured growth experiment—not an automatically approved plan.
Why this order?
Close access gaps, prove recovery and measure critical journeys before optimizing delivery and funding growth experiments. The 21 initiatives and their effort stay fixed; relative delivery months follow Investment Lab capacity and contingency assumptions. One shared team works through this priority sequence without overlap, with diminishing capacity from coordination overhead. Multiple squads, onboarding, dependencies and commercial returns are not forecast. Each effort estimate is an invented planning assumption; no real AI productivity is measured. Real workspaces preserve approved findings and evidence links; PrioNav handoff remains disabled.
Every initiative has a Why this priority? explanation. Impact × 3 + risk reduction × 3 + confidence − effort penalty sets the shared ranking across every product and lane. Effort penalty is likely hours ÷ 16, rounded and capped at 100. Scores are illustrative, not measured ROI or approval. Investment sliders change capacity and horizon coverage, never priority; dependency-aware scheduling is not implemented.
Balanced baseline
- Priority 1 · Code · Shared platform
Enforce tenant-scoped data access
Replace unconstrained lookups with tenant-bound repositories and negative authorization tests.
- Delivery gate
- Cross-tenant reads and writes denied across API and repository paths.
- Estimate source
- Illustrative source-led work breakdown at 160 hours/engineer-month; 85% assumed confidence. Range 2–5 months before team capacity. Validate overlap and dependencies.
- Why this priority? · Rank 1 · Code
- Score 640: impact 100 × 3 + risk reduction 95 × 3 + confidence 85 − effort penalty 30. Inputs are illustrative, not validated ROI.
- Priority 2 · Protect · Shared platform
Close privileged-access gaps
Split shared identities, constrain administrative permissions and attribute export events. First because every later product change depends on a trustworthy access boundary.
- Delivery gate
- Named identities and repeatable tenant/role denial tests.
- Estimate source
- Fictional security remediation work breakdown; confidence low until permission graph reviewed.
- Why this priority? · Rank 2 · Security
- Score 594: impact 100 × 3 + risk reduction 98 × 3 + confidence 80 − effort penalty 80. Inputs are illustrative, not validated ROI.
- Priority 3 · Code · Shared platform
Remove embedded credential loading
Use a managed secret reference with rotation and least-privilege access, not values read from source.
- Delivery gate
- Repository scan and rotation rehearsal confirm no embedded credential dependency.
- Estimate source
- Illustrative source-led work breakdown at 160 hours/engineer-month; 85% assumed confidence. Range 1–3 months before team capacity. Validate overlap and dependencies.
- Why this priority? · Rank 3 · Code
- Score 590: impact 85 × 3 + risk reduction 90 × 3 + confidence 85 − effort penalty 20. Inputs are illustrative, not validated ROI.
- Priority 4 · Code · Atlas Core
Validate API input at the boundary
Reject malformed payloads before business logic and return stable typed errors.
- Delivery gate
- Invalid and oversized inputs fail before any durable write.
- Estimate source
- Illustrative source-led work breakdown at 160 hours/engineer-month; 85% assumed confidence. Range 1–3 months before team capacity. Validate overlap and dependencies.
- Why this priority? · Rank 4 · Code
- Score 569: impact 86 × 3 + risk reduction 82 × 3 + confidence 85 − effort penalty 20. Inputs are illustrative, not validated ROI.
- Priority 5 · Architecture · Shared platform
Partition and invalidate cached results
Include tenant and input version in cache keys and define explicit invalidation semantics.
- Delivery gate
- No cross-tenant cache collision or stale permission-dependent response in regression tests.
- Estimate source
- Illustrative source-led work breakdown at 160 hours/engineer-month; 82% assumed confidence. Range 2–5 months before team capacity. Validate overlap and dependencies.
- Why this priority? · Rank 5 · Architecture
- Score 568: impact 85 × 3 + risk reduction 87 × 3 + confidence 82 − effort penalty 30. Inputs are illustrative, not validated ROI.
- Priority 6 · Architecture · Atlas Core
Make import writes replay-safe
Introduce operation keys and atomic replay guards so retrying a job cannot duplicate customer records.
- Delivery gate
- Repeated operation keys return the same outcome without duplicate writes.
- Estimate source
- Illustrative source-led work breakdown at 160 hours/engineer-month; 80% assumed confidence. Range 3–6 months before team capacity. Validate overlap and dependencies.
- Why this priority? · Rank 6 · Architecture
- Score 565: impact 90 × 3 + risk reduction 85 × 3 + confidence 80 − effort penalty 40. Inputs are illustrative, not validated ROI.
- Priority 7 · Stabilize · Atlas Core
Prove recovery reliability
Automate restore steps and rehearse them with production-like data. Protect service continuity before broadening the feature set.
- Delivery gate
- Repeatable restore meets 30-minute target; independent witness records result.
- Estimate source
- Fictional restore automation and rehearsal tasks; production parity unverified.
- Why this priority? · Rank 7 · Operations
- Score 560: impact 90 × 3 + risk reduction 90 × 3 + confidence 80 − effort penalty 60. Inputs are illustrative, not validated ROI.
- Priority 8 · Harden · Shared platform
Rehearse schema migration recovery
Test forward recovery on production-sized synthetic data and retain verified recovery evidence alongside release decisions.
- Delivery gate
- Rehearsed migration and recovery preserve records and tenant boundaries.
- Estimate source
- Fictional rehearsal tasks; data-volume and schema constraints still unknown.
- Why this priority? · Rank 8 · Architecture
- Score 531: impact 82 × 3 + risk reduction 80 × 3 + confidence 75 − effort penalty 30. Inputs are illustrative, not validated ROI.
- Priority 9 · Architecture · Atlas Core
Move long imports into bounded jobs
Split synchronous batches into checkpointed work with bounded retries, cancellation and tenant ownership.
- Delivery gate
- Load and restart tests complete large imports without request-timeout dependence.
- Estimate source
- Illustrative source-led work breakdown at 160 hours/engineer-month; 78% assumed confidence. Range 3–8 months before team capacity. Validate overlap and dependencies.
- Why this priority? · Rank 9 · Architecture
- Score 526: impact 86 × 3 + risk reduction 80 × 3 + confidence 78 − effort penalty 50. Inputs are illustrative, not validated ROI.
- Priority 10 · Measure · Shared platform
Instrument critical customer journeys
Add import-success and report-freshness objectives, traces and customer-impact alerts. Measurement precedes any savings claim.
- Delivery gate
- Representative journeys traced end to end with agreed baseline.
- Estimate source
- Fictional instrumentation work breakdown; integration depth needs a spike.
- Why this priority? · Rank 10 · Operations
- Score 523: impact 85 × 3 + risk reduction 76 × 3 + confidence 80 − effort penalty 40. Inputs are illustrative, not validated ROI.
- Priority 11 · Improve delivery · Atlas Core
Add release confidence gates
Cover imports, permissions and reporting with regression tests and observable release gates before accelerating feature throughput.
- Delivery gate
- Critical regression suite blocks failing releases and records evidence.
- Estimate source
- Fictional test-harness and coverage tasks; legacy coupling may increase effort.
- Why this priority? · Rank 11 · Code
- Score 514: impact 85 × 3 + risk reduction 78 × 3 + confidence 75 − effort penalty 50. Inputs are illustrative, not validated ROI.
- Priority 12 · Code · Shared platform
Unify API contracts and regression tests
Share request/response schemas and add producer-consumer compatibility tests.
- Delivery gate
- Every client contract matches server validation in the release gate.
- Estimate source
- Illustrative source-led work breakdown at 160 hours/engineer-month; 80% assumed confidence. Range 2–5 months before team capacity. Validate overlap and dependencies.
- Why this priority? · Rank 12 · Code
- Score 509: impact 78 × 3 + risk reduction 75 × 3 + confidence 80 − effort penalty 30. Inputs are illustrative, not validated ROI.
- Priority 13 · Architecture · Shared platform
Define evidence retention and deletion
Add a reviewed retention policy and auditable tenant-scoped deletion workflow with legal-hold checks.
- Delivery gate
- Lifecycle tests cover expiry, holds and deletion attribution without cross-tenant effects.
- Estimate source
- Illustrative source-led work breakdown at 160 hours/engineer-month; 70% assumed confidence. Range 3–6 months before team capacity. Validate overlap and dependencies.
- Why this priority? · Rank 13 · Architecture
- Score 501: impact 75 × 3 + risk reduction 82 × 3 + confidence 70 − effort penalty 40. Inputs are illustrative, not validated ROI.
- Priority 14 · Functional · Atlas Core
Page large customer record lists
Provide bounded cursor pages and explicit load-more behavior rather than returning all records.
- Delivery gate
- Large-data pagination preserves stable ordering and tenant isolation.
- Estimate source
- Illustrative source-led work breakdown at 160 hours/engineer-month; 85% assumed confidence. Range 2–5 months before team capacity. Validate overlap and dependencies.
- Why this priority? · Rank 14 · Functional
- Score 460: impact 80 × 3 + risk reduction 55 × 3 + confidence 85 − effort penalty 30. Inputs are illustrative, not validated ROI.
- Priority 15 · Reduce friction · Atlas Core
Prevent repeat import incidents
Improve validation and resumable recovery; use journey telemetry to measure incident recurrence and handling effort.
- Delivery gate
- Before/after repeat incidents and support handling time measured.
- Estimate source
- Fictional import-flow work breakdown; no financial saving assumed.
- Why this priority? · Rank 15 · Functional
- Score 458: impact 76 × 3 + risk reduction 65 × 3 + confidence 75 − effort penalty 40. Inputs are illustrative, not validated ROI.
- Priority 16 · Functional · Northstar Insights
Build incremental reporting views
Use tenant-owned incremental aggregates and expose freshness rather than repeating per-record query loops.
- Delivery gate
- Representative benchmarks meet the report target and show data freshness.
- Estimate source
- Illustrative source-led work breakdown at 160 hours/engineer-month; 75% assumed confidence. Range 3–6 months before team capacity. Validate overlap and dependencies.
- Why this priority? · Rank 16 · Functional
- Score 431: impact 84 × 3 + risk reduction 48 × 3 + confidence 75 − effort penalty 40. Inputs are illustrative, not validated ROI.
- Priority 17 · Functional · Atlas Core
Expose import progress and safe cancellation
Show job progress, retry state and cancellation in the product without permitting conflicting edits.
- Delivery gate
- Operators can track, cancel and resume their own imports with complete audit attribution.
- Estimate source
- Illustrative source-led work breakdown at 160 hours/engineer-month; 82% assumed confidence. Range 1–3 months before team capacity. Validate overlap and dependencies.
- Why this priority? · Rank 17 · Functional
- Score 428: impact 72 × 3 + risk reduction 50 × 3 + confidence 82 − effort penalty 20. Inputs are illustrative, not validated ROI.
- Priority 18 · Optimize · Northstar Insights
Speed up customer reporting
Benchmark representative data, optimize expensive queries and evaluate precomputed aggregates after end-to-end measurement is available.
- Delivery gate
- Representative report latency meets agreed target without stale results.
- Estimate source
- Fictional query and load-test tasks; one sample account is insufficient.
- Why this priority? · Rank 18 · Functional
- Score 390: impact 80 × 3 + risk reduction 45 × 3 + confidence 65 − effort penalty 50. Inputs are illustrative, not validated ROI.
- Priority 19 · Code · Atlas Core
Add an AI quality and cost evaluation gate
Create a versioned evaluation set for factuality, citations, sensitive-data handling and per-task cost before releasing AI assistance.
- Delivery gate
- A reviewer accepts measured quality and cost thresholds before any production decision automation.
- Estimate source
- Illustrative source-led work breakdown at 160 hours/engineer-month; 65% assumed confidence. Range 2–5 months before team capacity. Validate overlap and dependencies.
- Why this priority? · Rank 19 · Code
- Score 371: impact 72 × 3 + risk reduction 40 × 3 + confidence 65 − effort penalty 30. Inputs are illustrative, not validated ROI.
- Priority 20 · Test growth · Atlas Core
Pilot AI case-history assistance
Run a bounded, human-reviewed experiment; do not automate production decisions or assume the feature creates a moat.
- Delivery gate
- Accuracy, task time, provider cost and data boundaries independently evaluated.
- Estimate source
- Fictional prototype/evaluation tasks; commercial value remains unvalidated.
- Why this priority? · Rank 20 · Functional
- Score 350: impact 75 × 3 + risk reduction 35 × 3 + confidence 60 − effort penalty 40. Inputs are illustrative, not validated ROI.
- Priority 21 · Validate value · Northstar Insights
Validate paid AI packaging
Compare alternatives and willingness to pay after a successful pilot. Fund research, not an invented revenue projection.
- Delivery gate
- Pricing and unit economics validated with representative customers.
- Estimate source
- Fictional discovery and packaging tasks; no ROI estimate included.
- Why this priority? · Rank 21 · Commercial
- Score 289: impact 68 × 3 + risk reduction 20 × 3 + confidence 55 − effort penalty 30. Inputs are illustrative, not validated ROI.
No initiative is approved by browsing this demo. Automated plan handoff is not enabled.