AI and Automation Services
Deploy AI agents across your SDLC to automate QA testing, release workflows, DevOps operations, and code quality checks. Scrums.com helps enterprises increase delivery velocity while reducing costs through intelligent automation orchestrated via our AI Agent Gateway.
Years of Service
Client Renewal Rate
Global Clients
Ave. Onboarding
When to Choose AI & Automation Services
Choose Scrums.com AI & Automation Services When:
- Manual QA bottlenecks slow your releases and you need automated test generation, execution, and regression coverage (reduce QA time by 60%)
- Your release cycles are too long due to manual deployment processes, coordination overhead, and deployment anxiety (deploy 3x faster with automated orchestration)
- Code review bandwidth is limited and PRs wait days for review while quality issues slip through (AI code review provides instant feedback on every commit)
- DevOps team is overwhelmed with repetitive infrastructure tasks, monitoring alerts, and incident response (automate 50% of operational tasks)
- Development velocity is constrained by boilerplate coding, repetitive features, and legacy system maintenance (AI-assisted coding increases developer productivity 40%)
- You lack visibility into SDLC efficiency and can't measure where automation would deliver the highest ROI (SEOP analytics identify top automation opportunities)
- Your team is skeptical about AI but competitors are gaining advantages through automation (expert-led implementation proves value quickly with pilot projects)
- Compliance and governance concerns block AI adoption despite clear benefits (AI Gateway provides enterprise-grade control, audit trails, and data sovereignty)
Consider Alternative Scrums.com Solutions:
- Need specialized QA automation only? → Managed QA Services
- Want full development team augmentation? → Dedicated Development Teams
- Need platform-wide orchestration first? → SEOP Platform
- Require DevOps and infrastructure automation? → Cloud Hub Services
- Building custom AI features into your product? → Custom Software Development
What's Included in AI & Automation Services
Our AI automation services deploy specialized AI agents across your entire software development lifecycle, from backlog grooming and code generation to automated testing, deployment orchestration, and production monitoring. All agents are governed through SEOP with full visibility, control, and compliance.
AI-Powered QA & Test Automation
Deploy AI agents that automatically generate test cases, execute regression suites, identify edge cases, and validate deployments across environments. Our QA automation reduces manual testing effort by 60% while catching bugs earlier in the development cycle through intelligent pattern recognition and continuous validation.
Intelligent Release & Deployment Orchestration
Automate release workflows with AI agents that manage CI/CD pipelines, coordinate multi-environment deployments, execute blue-green rollouts, and monitor production health post-deployment. Reduce deployment time from hours to minutes while maintaining safety and rollback capabilities through intelligent automation.
Automated Code Quality & Review
AI agents continuously analyze code for quality issues, security vulnerabilities, performance bottlenecks, and architectural inconsistencies. Receive automated pull request reviews, refactoring suggestions, and technical debt identification, maintaining code standards without manual oversight and catching issues before they reach production.
DevOps Process Automation
Automate infrastructure provisioning, configuration management, monitoring setup, and incident response with DevOps-focused AI agents. From automated scaling based on load patterns to self-healing infrastructure that resolves common issues autonomously, our agents reduce operational overhead and improve system reliability.
AI-Assisted Development & Code Generation
Accelerate development with Devin-style AI agents that generate boilerplate code, implement standard features, refactor legacy systems, and assist with debugging. Developers focus on complex business logic while AI handles repetitive coding tasks, increasing productivity by 40% without compromising quality or maintainability.
SDLC Orchestration & Workflow Automation
Deploy AI agents that automate sprint planning, backlog grooming, story refinement, dependency tracking, and progress reporting. Integrate with Jira, Azure DevOps, and ClickUp to automatically update tickets, notify stakeholders, identify blockers, and maintain accurate project visibility, reducing PM overhead by 50%.
Our AI & Automation Approach
We don't just provide AI tools, we deploy complete AI automation solutions with expert implementation, governance frameworks, and continuous optimization. Our platform-enabled approach ensures AI agents integrate seamlessly into your existing workflows while maintaining security, compliance, and control.
AI Agent Gateway for Enterprise Control
All AI agents deploy through Scrums.AI Gateway, a unified orchestration layer that provides centralized governance, access control, audit trails, and performance monitoring. Connect once to deploy QA agents, code review agents, DevOps automation, and more, all managed through SEOP dashboards with full visibility into agent activities, decisions, and outcomes.
Expert Implementation & Training
Our AI automation specialists work alongside your team to implement agents effectively, train your engineers on agent capabilities, configure workflows to your specific needs, and optimize agent performance over time. We provide the expertise to maximize AI ROI, not just technology handoff but complete adoption support.
Continuous Learning & Optimization
AI agents continuously learn from your codebase, development patterns, and deployment history to improve automation accuracy and relevance. We monitor agent performance, gather feedback from your team, and iterate on agent configurations to increase value delivery, ensuring automation gets smarter and more effective over time.
AI & Automation Implementation Process
Our structured implementation process ensures AI agents integrate smoothly into your SDLC while delivering measurable value quickly.
Discovery & Architecture
We begin by understanding your current development workflows, toolchain, pain points, and automation opportunities. Our team conducts assessment workshops, reviews your SDLC processes, identifies high-impact automation candidates, and designs an AI agent architecture tailored to your needs.
Key Activities:
- Development workflow and toolchain assessment
- Pain point identification and prioritization
- Use case mapping for AI automation opportunities
- Technical architecture design for agent deployment
- Integration planning with existing systems (Jira, GitHub, CI/CD)
- Governance framework establishment
- Success metrics and KPI definition
Deliverable: AI automation roadmap, architecture diagrams, implementation plan, ROI projections
Team Deployment & Agent Setup
Deploy your AI automation implementation team and configure the Scrums.AI Gateway. We set up initial agents, integrate with your development tools, configure governance policies, and prepare your environment for automated workflows.
Key Activities:
- SEOP and AI Gateway environment setup
- Tool integrations (Jira, GitHub, Azure DevOps, Jenkins, monitoring platforms)
- Initial agent deployment (QA, code review, or DevOps agents)
- Access control and permission configuration
- Agent training on your codebase and workflows
- Pilot group selection and onboarding
- Monitoring dashboard configuration
Deliverable: Configured AI Gateway, integrated tools, deployed pilot agents, trained team members
Delivery & Automation Rollout
Roll out AI agents progressively across teams and workflows. Start with low-risk automation (automated testing, code analysis) and expand to higher-impact areas (deployment orchestration, incident response) as confidence builds and value is demonstrated.
Key Activities:
- Pilot agent deployment with selected teams
- Agent performance monitoring and optimization
- Feedback collection and workflow refinement
- Gradual expansion to additional teams and use cases
- Agent training data enrichment from production usage
- Integration of additional agents (QA → DevOps → Development)
- Best practices documentation and knowledge sharing
Deliverable: Production-ready AI agents, performance metrics, adoption documentation, team training materials
Scale & Optimize
Continuously expand AI automation coverage, optimize agent performance, and measure ROI. We work with your team to identify new automation opportunities, fine-tune existing agents, and ensure AI continues delivering increasing value as your development practices evolve.
Key Activities:
- Performance analytics and ROI measurement
- Agent accuracy improvement through feedback loops
- New use case identification and implementation
- Workflow optimization based on usage patterns
- Regular strategy reviews with leadership
- Agent capability expansion and updates
- Training programs for new team members
Deliverable: Quarterly performance reports, optimization recommendations, expanded automation coverage, ROI documentation
Ready to Accelerate Delivery with AI Automation?
AI automation services combine specialized agents, expert implementation, and proven frameworks to increase delivery speed, reduce costs, and improve software quality, without disrupting your existing workflows or compromising governance.
AI & Automation Technologies We Deploy
Our AI automation services leverage cutting-edge platforms and frameworks to deliver intelligent automation across your SDLC. From specialized AI agents to orchestration platforms, we deploy the right technology stack for your automation needs, all integrated through SEOP for unified visibility and control.
What Our Clients Say
AI & Automation Services Pricing
What Impacts AI & Automation Costs?
Scope of Automation
Automating a single workflow (e.g., QA regression testing) costs less than comprehensive SDLC automation across QA, DevOps, code review, and deployment orchestration. The number of agents deployed and processes automated directly impacts implementation scope.
Team Size & Agent Distribution
Deploying AI agents for a 10-person team requires less setup than enterprise-wide rollout across 100+ engineers. Larger teams need more agent capacity, governance complexity, and change management effort.
Integration Complexity
Simple integrations with standard tools (Jira, GitHub) are straightforward. Complex enterprise environments with custom tooling, legacy systems, or strict security requirements require specialized integration work and additional configuration.
Customization & Training Requirements
Pre-configured agents for common use cases deploy quickly. Highly customized agents trained on proprietary codebases, domain-specific workflows, or unique development practices require more setup time and ongoing optimization.
Governance & Compliance Needs
Standard deployments with basic governance are less complex than regulated environments requiring audit trails, data residency controls, model explainability, and compliance documentation (FinTech, Healthcare, Banking).
Existing Automation Maturity
Organizations with mature CI/CD pipelines and automation culture adopt AI agents faster than teams with limited automation experience. More manual processes = more change management effort and longer adoption timelines.
Industry Benchmarks: What AI Automation Typically Costs
These are general industry ranges to help you budget:
Pilot AI Automation (Single Team, 2-3 months)
QA automation or code review agents for one team, limited scope
Industry range: $25K - $75K
Standard AI Automation (Multiple Teams, 6 months)
QA, DevOps, and code quality automation across 3-5 teams
Industry range: $75K - $200K
Enterprise AI Automation (Organization-wide, 12+ months)
Comprehensive SDLC automation, custom agents, enterprise governance
Industry range: $200K - $800K+
Ongoing Optimization & Support
Agent monitoring, performance tuning, new use case implementation
Industry range: $5K - $50K/month
The Scrums.com Advantage: AI Automation With Expert Implementation
Unlike AI tool vendors that provide technology without implementation support, we deliver complete AI automation solutions with expert teams, proven frameworks, and ongoing optimization—achieving ROI faster at 40-60% cost savings compared to building AI automation capabilities in-house.
What Makes Our AI Automation Different:
AI Agent Gateway – Centralized orchestration layer with enterprise governance, audit trails, and unified visibility across all deployed agents
Expert Implementation Teams – AI automation specialists who configure agents, train your team, and optimize performance—not just technology handoff
Platform-First Delivery – All agents integrate through SEOP for real-time monitoring, performance analytics, and ROI measurement
Proven Use Cases – Deploy pre-configured agents for common workflows (QA, code review, DevOps) with faster time-to-value
Continuous Optimization – Agents improve over time through feedback loops, usage analytics, and regular tuning by our specialists
Flexible Engagement Models – Pilot projects, dedicated automation teams, or augmented AI engineers—scale based on success
94% Client Renewal Rate – Clients stay because AI automation delivers measurable value, not empty promises
Three Ways to Implement AI Automation
AI Pilot Project
Start small with a focused automation pilot (QA automation, code review) to prove value before expanding.
Best for: Organizations new to AI automation wanting to demonstrate ROI quickly
Dedicated AI Automation Team
Full-time team of AI engineers, DevOps specialists, and implementation experts deploying comprehensive automation.
Best for: Enterprises committing to organization-wide SDLC automation
Augmented AI Specialists
Add individual AI automation engineers, ML ops specialists, or DevOps experts to your existing team.
Best for: Teams with automation strategy defined needing specialized implementation skills
Ready to See What AI Automation Would Cost?
Pricing depends on automation scope, team size, and integration complexity. View our transparent pricing models or get a custom AI automation quote after a consultation.
Industries We Serve with AI & Automation
From FinTech firms automating compliance testing to healthcare platforms improving code quality, our AI automation services deliver measurable value across industries with unique regulatory and operational requirements.
Fintech
Banking & Financial Services
Logistics & Supply Chain
Technology & SaaS
Telecommunications
Insurance
Retail & Ecommerce
Healthcare & Telemedecine
AI & Automation Success Stories
AI & Automation Services FAQs
What types of AI agents can you deploy?
We deploy specialized AI agents across the entire SDLC: QA automation agents (test generation, execution, validation), code review agents (quality analysis, security scanning, refactoring suggestions), DevOps agents (infrastructure provisioning, monitoring, incident response), deployment orchestration agents (CI/CD automation, release management), development assistance agents (code generation, debugging support), and SDLC workflow agents (sprint planning, backlog grooming, progress tracking). All agents are orchestrated through Scrums.AI Gateway with unified governance and visibility.
How long does AI automation implementation take?
Pilot projects with focused automation (single team, single workflow) typically deploy in 2-4 weeks with measurable results within the first month. Standard implementations across multiple teams and workflows take 2-3 months from discovery to production rollout. Enterprise-wide automation programs run 6-12 months with phased deployment, comprehensive change management, and continuous optimization. Timeline depends on integration complexity, team size, and organizational automation maturity.
Will AI agents replace our developers and QA engineers?
No. AI agents augment human capabilities rather than replace them. QA engineers shift from manual test execution to test strategy, edge case identification, and exploratory testing. Developers focus on complex business logic, architecture decisions, and creative problem-solving while AI handles boilerplate code, repetitive tasks, and routine reviews. The result is higher-value work for your team, not job elimination—most clients see productivity gains of 40-60% with same headcount.
How do you ensure AI agent security and data privacy?
All AI agents deploy through Scrums.AI Gateway with enterprise-grade security: data sovereignty controls (your data never leaves your environment for training), access control and permissions management, audit trails for all agent actions, model governance and explainability, compliance with SOC 2/GDPR/HIPAA standards, and encrypted communication channels. For regulated industries, we support on-premise deployment, bring-your-own-model (BYOM) architectures, and custom compliance configurations.
What ROI can we expect from AI automation?
Typical ROI metrics from our AI automation clients: 60% reduction in manual QA effort, 3x faster release cycles through deployment automation, 40% developer productivity increase with AI-assisted coding, 50% reduction in code review cycle time, 70% decrease in production incidents through automated testing, and 40-60% cost savings vs. scaling headcount. Most enterprises achieve positive ROI within 3-6 months of deployment. We establish baseline metrics before implementation and track improvement continuously through SEOP analytics.
Can AI agents integrate with our existing tools and workflows?
Yes. Scrums.AI Gateway integrates with standard SDLC tools including Jira, Azure DevOps, GitHub, GitLab, Bitbucket, Jenkins, CircleCI, GitLab CI, AWS (CodePipeline, CloudWatch), Azure (Pipelines, Monitor), Kubernetes, Docker, Terraform, Slack, Microsoft Teams, PagerDuty, Datadog, New Relic, and custom internal tools via REST APIs. We design agent workflows around your existing processes rather than forcing process changes—minimizing disruption and accelerating adoption.
What happens if an AI agent makes a mistake?
All agent actions are monitored, logged, and reviewable through SEOP dashboards. Critical operations (production deployments, code merges) have human approval gates configurable based on your risk tolerance. Agents include rollback capabilities, and our team continuously monitors agent performance to identify and correct errors quickly. We implement gradual rollout strategies (pilot teams first, then expansion) to catch issues before organization-wide impact. Agent accuracy improves over time through feedback loops and continuous training.
Do we need AI expertise in-house to adopt AI automation?
No. Our implementation teams provide all necessary AI expertise including agent configuration, workflow design, integration setup, training, and ongoing optimization. We train your team on agent capabilities and best practices, but you don't need data scientists, ML engineers, or AI specialists to benefit from automation. Most successful clients have standard development teams who learn to work effectively with AI agents through our adoption programs.
How do you measure AI agent performance and effectiveness?
SEOP provides real-time dashboards tracking agent-specific metrics: automation coverage percentage, task completion accuracy, time saved vs. manual processes, bug catch rate (for QA agents), deployment success rate (for DevOps agents), code quality improvement (for review agents), and developer satisfaction scores. We establish baseline metrics before implementation and track improvement monthly through automated analytics. Quarterly business reviews assess ROI, identify optimization opportunities, and plan capability expansion.
Can we start with a small pilot before committing to enterprise-wide automation?
Absolutely. We recommend pilot projects for organizations new to AI automation. Typical pilots focus on single high-impact use case (QA automation for one team, code review for specific repos) running 1-2 months to demonstrate value quickly. Successful pilots expand incrementally based on measured results and team feedback. This approach reduces risk, builds confidence, and ensures AI automation delivers real value before major investment.
