Sprout Modernize
Assess. Modernize. Sustain.
One unified pipeline takes your application estate from first assessment to fully managed, continuously improving operations. You buy an outcome — documented, monitored, supported apps — not a project. Three specialist teams, governed handoffs, and a shared artifact trail keep momentum from discovery to day-2 support, with your team always in control of the decisions that matter.
Sprout Modernize, in the system.
One step in the cycle — the Ready step that gets your apps documented and AI-ready, then hands them to operations. Sprout OS runs and governs it, the same as everything else.
Sprout Discover
figure out how your work gets done
Sprout Modernize
get your apps ready for AI
Sprout Agents
put AI agents to work on the tasks
Sprout Operate
we run it for you, and keep it running
Inside Sprout Modernize, the same shape repeats one level down: Survey → Forge → Watch ↓
Three stages. One governed pipeline.
The stages move in sequence. Each one produces artifacts that become the next team’s starting point — a continuous, governed flow with no cold starts.
Sprout Survey
Connect to your estate, scan it, and let the factory build each app’s complete knowledge layer — fast.
Get a clear, ranked picture of the whole application estate in under 5 business days.
- Full portfolio inventory & ranked, factory-ready shortlist
- Live onboarding dashboard (real-time connector status)
- Supportability runbook for every app
Sprout Forge
Uplift each app to a known-good, support-ready state — with monitoring wired in before it ever enters live support.
Apply factory-fit standards consistently across the cohort.
- Apps in a consistent, support-ready state
- Per-app monitoring dashboards (Grafana / App Insights)
- Agreed SLO commitments
Sprout Watch
The factory keeps your apps running and continuously improving — this stage never ends.
Keep apps healthy, secure, and cost-efficient with Overwatch monitoring + AI triage.
- Live Overwatch dashboards & SLOs
- AI-triaged incident & change records
- Auto-refreshing runbooks (per release)
◓ Color = team ownership. Arrows = governed handoff gates where artifacts are reviewed, signed off, and transferred.
Activities, artifacts, handoff, metrics.
Stage 1 · Discovery & KnowledgeSprout Survey — Assessment
- Get a clear, ranked picture of the whole application estate in under 5 business days.
- Run every selected app through the standardized GitHub Enterprise intake pipeline.
- Let AI agents generate each app’s full knowledge layer — no more tribal knowledge.
- Stand up a customer-facing onboarding dashboard with live connector status
- Connect sources: GitHub, Azure DevOps, file shares, Azure subscriptions
- Run an automated portfolio scan and rank a factory-ready first cohort (typically 10–20 apps)
- AI agents generate runbooks, extract business rules, and map data models
- Produce CodeQL call-stack maps and a tech-stack “passport” per app
- Full portfolio inventory & ranked, factory-ready shortlist
- Live onboarding dashboard (real-time connector status)
- Supportability runbook for every app
- Business-rules doc & data model
- CodeQL call-stack map & tech-stack passport
- Modernization uplift plan
A fully documented estate plus a per-app uplift plan, runbooks, and passports — handed to Sprout Forge to make support-ready.
Stage 2 · Engineering & DeliverySprout Forge — Modernization
- Apply factory-fit standards consistently across the whole cohort.
- Wire monitoring and observability in before any app enters live support.
- Agree the SLOs each app will be operated and measured against.
- Uplift each app to a known-good, support-ready standard
- Wire per-app monitoring dashboards (Grafana / Azure App Insights)
- Establish logging, health-check & secrets hygiene — a minimal-supportable baseline
- Agree SLO commitments with you, app by app
- Produce the operating baseline that becomes the start of live operations
- Apps in a consistent, support-ready state
- Per-app monitoring dashboards
- Logging, health-check & secrets baseline
- Agreed SLO commitments
- Operating baseline
- Executed modernization uplift plan
A standardized, monitored app with agreed SLOs — handed to Sprout Watch as an always-on, SLO-governed service.
Stage 3 · Overwatch Operations & SupportSprout Watch — Operate & Improve
- Keep apps healthy, secure, and cost-efficient — continuously, with Overwatch monitoring + AI triage.
- Resolve issues fast through an agentic Tier 1–2 helpdesk and approval-gated fixes.
- Improve the estate every release — this stage never ends.
- Stream health signals from Azure App Insights & Azure Monitor
- AI triage and cluster incidents to root cause and a fix
- Run the agentic Tier 1–2 helpdesk front door for your users
- Prioritize an SLO-governed backlog, ranked with you
- Deploy fixes via CI/CD (Dev → Test → Prod), approval-gated
- Auto-refresh runbooks with every release
- Live Overwatch dashboards & SLOs
- AI-triaged incident & change records
- Auto-refreshing runbooks
- SLO / MTTR / cost reporting
Operations data flows back to assessment — every cycle is smarter than the last, and apps keep improving without you asking.
Most programs lose time in the handoff. Here they’re formal gates.
Packaged, reviewed, and signed off before work moves forward — so intent survives all the way from discovery to operations.
Assessment → Modernization
The AI-generated knowledge layer becomes the blueprint the engineering team uplifts against.
- Portfolio inventory & supportability runbooks — a documented estate, no tribal knowledge.
- CodeQL call-stack maps & tech-stack passports — traceable code paths and per-app tech footprint.
- Modernization uplift plan — a per-app plan to reach a known-good, support-ready state.
- Business-rules doc & data model — so intent, not just code, transfers to the build team.
Modernization → Management
A standardized, monitored app becomes an always-on, SLO-governed service.
- Support-ready, standardized apps — factory-fit standards applied across the cohort.
- Per-app monitoring & SLOs — Grafana / App Insights dashboards and agreed service levels.
- Runbooks & operating baseline — the known-good starting point for live operations.
- Logging, health-check & secrets hygiene — a minimal-supportable baseline in place.
Shared artifact repository
One source of truth every team reads from and writes to — no email attachments, no version drift.
Overlapping membership
Each handoff includes a 1–2 week overlap where outgoing and incoming leads co-own the work.
Definition of done at gates
Every gate has an explicit checklist; nothing advances until artifacts are accepted.
Traceability end-to-end
Every decision links back to an assessment finding, so intent survives all the way to operations.
You make the call at every checkpoint.
Between checkpoints, the factory runs — transparently and with guardrails.
Factory-fit confirmation
Up front, we confirm fit together: GitHub Enterprise, Microsoft-first monitoring, subscription pricing.
Cohort & shortlist sign-off
You confirm which apps enter the factory and in what order — nothing proceeds without your yes.
Standardization & SLO agreement
You approve the uplift plan and agree the SLO targets we’ll be measured against.
Approval-gated changes
Every AI-generated fix or feature requires human sign-off before it reaches production.
Backlog prioritization
Enhancement requests are triaged and ranked with you on a regular cadence.
SLO & service reviews
Recurring reviews of SLO, MTTR, and backlog burndown keep performance visible and honest.
Every artifact, owned and traceable.
Every major artifact produced across the pipeline, who owns it, and why it matters. Filter by stage to trace the flow of knowledge.
Portfolio inventory & ranked shortlist
Full estate catalog plus the factory-ready first cohort.
Live onboarding dashboard
Real-time connector status and progress before any work begins.
Supportability runbook (per app)
AI-generated operating knowledge for every application.
Business-rules doc & data model
Captures what each app does and the data it relies on.
CodeQL call-stack map
Traceable view of code paths and dependencies.
Tech-stack passport
One-page summary of each app’s technology footprint.
Modernization uplift plan
Agreed plan to bring each app to a support-ready state.
Support-ready, standardized apps
Apps uplifted to factory-fit standards.
Per-app monitoring dashboards
Grafana / App Insights visibility wired per application.
Logging, health-check & secrets baseline
Minimal-supportable-product hygiene across the cohort.
Agreed SLO commitments
The service levels operations will be measured against.
Operating baseline
The known-good starting point for live operations.
Live Overwatch dashboards & SLOs
Continuous health visibility and agreed service levels.
AI-triaged incident & change records
Auditable operational history with AI root-cause clustering.
Auto-refreshing runbooks
Knowledge that updates automatically with every release.
SLO / MTTR / cost reporting
Transparent performance, burndown, and subscription spend.
The Overwatch operations loop.
Once you’re live, this loop runs continuously inside Sprout Watch — every fix feeds the next monitoring cycle automatically.
Collect signals
App Insights & Azure Monitor stream health data.
AI triage & cluster
Agents + runbooks find root cause and a fix.
Helpdesk support
Agentic Tier 1–2 front door for your users.
Prioritize backlog
SLO-governed burndown, ranked with you.
Deploy fix via CI/CD
Dev → Test → Prod, approval-gated.
Update runbooks
Knowledge auto-refreshes per release.
“You experience this as: apps that just work, fast answers when something breaks, and steady improvement you didn’t have to ask for.”
One connected pipeline, not three disconnected projects.
You buy an outcome
Managed, improved, and supported applications — not a project or a body of engineers. We own the toolchain and the result.
You stay in control
Every change is approval-gated. Nothing ships to production without your sign-off, and priorities are set with you.
Full transparency
A live dashboard, runbooks, and SLO reporting mean you always see app health, progress, and exactly what changed.
AI does the heavy lifting
AI generates the knowledge layer and triages incidents — deflecting ~40% of Tier-1 tickets and cutting time-to-runbook to under 48 hours.
A closed loop
Operations data flows back to assessment, so every cycle is smarter than the last and apps keep improving without you asking.
Predictable economics
Fixed, subscription-based pricing and ongoing cost reporting — with 35–50% lower total cost of ownership over years 1–3.
A representative engagement.
Durations vary by portfolio size — the sequence and the gates stay constant.
Three specialist teams.
Onboarding specialists and AI agents that connect sources, scan the estate, and generate each app’s knowledge layer.
Cloud and application engineers who apply factory-fit standards, deliver MSP uplift, and wire monitoring.
Overwatch SRE, agentic helpdesk, and an approval-gated AI pipeline that run, support, and improve apps.
Anticipated risks, contained.
| Risk | Impact | Mitigation |
|---|---|---|
| Tribal knowledge / undocumented apps | High | AI generates a full knowledge layer (runbooks, business rules, passports) in Sprout Survey — zero undocumented apps. |
| Loss of control over what ships | High | Every AI-generated fix or feature is approval-gated; nothing reaches production without your sign-off. |
| Apps enter support unmonitored | High | Sprout Forge wires monitoring and agrees SLOs before any app enters live support — zero unmonitored apps. |
| Scope drift in the cohort | Medium | Cohort and uplift-plan sign-off gate; the first cohort is a defined 10–20 apps agreed with you. |
| Runaway / unpredictable cost | Medium | Fixed subscription pricing plus SLO and cost reporting; 35–50% lower TCO over years 1–3. |
| Helpdesk overwhelmed at scale | Medium | Agentic Tier 1–2 helpdesk plus AI triage deflect ~40% of Tier-1 tickets, keeping MTTR under 4 hrs for P1. |
Put your portfolio on the line.
Start with a Sprout Survey assessment. In weeks you’ll have a costed roadmap, a target architecture, and a clear path from legacy to managed.