Premade software scopes to help you move faster.
222 scopes, each with a defined finish state. Pick what you need, add it to your Software Factory Delivery Plan, and one team ships it from your backlog.
A defined piece of delivery work, with milestones and a finish state, run on your Delivery Plan.
One monthly Delivery Plan for one active workstream. Your scope sets the schedule, not the price.
Coding starts in 48 hours. First deliverable in 14 days, or month one is free.
222 scopes across sprints, pods and managed SLAs, or send us your own.
- AI Agent RolloutIn every plan→
One operational AI agent deployed into a live workflow with governance and guardrails: workflow selection, agent build and integration, enterprise controls, and a measured rollout with hours-saved reporting.
- AI Data Infrastructure FoundationIn every plan→
Set up the data pipelines, versioning, feature flow, and storage patterns required for repeatable ML and AI work. Finish with an ML-ready foundation running in your cloud.
- AI Feature ProductionizationIn every plan→
Take an existing AI proof-of-concept into monitored production with reliability, evaluation, security, and cost controls. Finish state: the demo that impressed everyone, now boring, monitored, and live.
- AI Ideation WorkshopIn every plan→
Facilitated 2-week workshops that align stakeholders, define goals, and produce an actionable design & development strategy.
- AI Model Routing & Cost OptimizationIn every plan→
Route requests across models based on quality, latency, availability, and cost while tracking spend and outcomes. Finish state: the right model per request, spend visible per feature, quality protected by evaluation.
- Automation Engineering PodIn every plan→
An ongoing pod building out AI agents and workflow automations across your SDLC and operations: an automation backlog delivered agent by agent, behind enterprise governance, with ROI measured on every rollout.
- Customer Support AI AgentIn every plan→
Implement an AI support agent with knowledge retrieval, tool actions, escalation rules, and conversation tracking. Finish state: a share of support conversations resolved well without a human, measured.
- DesignAI AcceleratorIn every plan→
Structured engagement that audits a design team, upskills designers in AI workflows and builds embedded AI capability.
- DesignAI SprintIn every plan→
A production-ready clickable prototype in two weeks, built with AI-accelerated design tools.
- Document Extraction AI WorkflowIn every plan→
Extract structured fields and classifications from documents with validation and exception handling. Finish state: documents becoming clean structured data automatically, with humans only on exceptions.
- Embedded QA PodIn every plan→
Dedicated QA team leveraging AI agents to deliver up to 1000 automated tests so regressions stop reaching production.
- Enterprise Knowledge ChatbotIn every plan→
Deploy a permission-aware conversational interface with RAG ingestion, retrieval, and grounding over approved internal knowledge. Finish state: staff getting cited, permission-correct answers from your own content.
Showing 12 of 16. Use the filters or search to narrow them, or see every scope A–Z.
Scopes by service
- DevOps Services52 scopes →
- Custom Software Development Services50 scopes →
- Data Engineering Services21 scopes →
- Application Modernization Services20 scopes →
- Software Testing Services15 scopes →
- Web Development Services15 scopes →
- Mobile App Development Services12 scopes →
- Software Maintenance Services12 scopes →
- Generative AI Development Services10 scopes →
- UI/UX Design Services10 scopes →
- AI Agent Development Services5 scopes →
How scopes work
Three ways to run a scope
Every scope is milestone-based, SLA-backed and delivered from your Delivery Plan backlog in the Software Factory.
Outcome-driven sprints
A bounded piece of work with an agreed finish state, such as an MVP live with users or an API serving real clients. Milestones are agreed before work begins.
See 199 sprints ↑Pods
A standing cross-functional team, such as QA, platform or commerce engineering, that works your backlog sprint after sprint.
See 17 pods ↑Managed SLAs
Ongoing upkeep under response targets: maintenance, OS updates, uptime, observability and release QA, month after month.
See 6 SLAs ↑The Software Factory
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Read the launch post →- Backlog
- Scope
- Build
- Test
- Ship
A scope is a defined piece of software delivery work with an agreed finish state and milestones, such as a production-ready SaaS MVP, a cloud migration or a QA subscription. You add scopes to your Delivery Plan backlog and one Scrums.com team delivers them in priority order.
An outcome-driven sprint delivers a bounded result with a fixed finish state. A pod is a standing cross-functional team that works your backlog sprint after sprint. A managed SLA keeps a system running under response targets, month after month.
Scopes run on the Software Factory Delivery Plan, from Was $8,000£6,700R140,000 now $4,500£3,750R79,000 a month for one active workstream. Your scope sets the delivery schedule and the total cost, not a separate price.
You can queue as many scopes as you like. One active workstream delivers them in your priority order. To run several scopes in parallel, talk to Scrums.com about an Enterprise plan.
Send it to us. Most work maps to an existing scope or a combination of scopes, and anything else is scoped into your Delivery Plan backlog with its own milestones.
Onboarding starts at checkout. Coding starts within 48 hours of access, and the team builds the first agreed deliverable within 14 days, or your first month is free.
Want the detail? Read the AI development documentation →
Every scope runs on one Delivery Plan
Core costs Was $8,000£6,700R140,000 now $4,500£3,750R79,000/month for one active workstream. Queue as many scopes as you need; one team works them in your priority order.
Add this scope to your backlog. Your delivery owner confirms the scope with you, and the team delivers it.
One plan for your software backlog.
Work is scoped and delivered within your plan’s capacity. Priorities can change; additional parallel workstreams require more capacity.