AI Development Services

AI development is the engineering discipline of building products and systems whose behaviour is driven by AI models, and of keeping those systems performing in production. It covers LLM integration, custom AI agent development, retrieval-augmented generation (RAG) systems, fine-tuning proprietary models, and deploying AI features into production applications. It also covers AI applied inside the software development lifecycle, where agents automate QA, code review, and deployment. It is distinct from standard software development, where no AI model is involved.

AI and Automation Services deploy AI agents inside the software development lifecycle to automate QA testing, code review, deployment orchestration, and DevOps operations. The goal is faster, more reliable software delivery. AI product development is different: it means building AI-powered features and products into your own applications so your customers or employees can interact with AI. One improves how software is built; the other determines what the software does. Most enterprise programmes need both, which is why this service covers both.

We are model-agnostic. Our engineers have production experience with OpenAI (GPT-4o, o1, o3), Google Gemini, Meta Llama (open-source, self-hosted), Mistral, Anthropic Claude, and Grok. Model selection is driven by your use case, latency requirements, cost targets, and data residency constraints, not by vendor agreements. Where appropriate we use multiple models in combination, routing each task to the model best suited to it.

Retrieval-augmented generation (RAG) is an architecture that connects an LLM to a searchable knowledge base of your own content, so the model grounds its responses in your proprietary data without exposing that data to third-party training. We recommend RAG when you have a large corpus of internal documents, policies, or product content; when you need responses that are specific and verifiable against a known source; or when you cannot afford hallucinations in regulated or high-stakes outputs. RAG is often the right choice before fine-tuning, because it is faster to update and easier to audit.

Hallucination prevention is an engineering problem, not just a prompt engineering problem. Our approach combines retrieval-augmented generation to ground responses in verified source documents; output validation pipelines that check responses against known facts or structured constraints; confidence scoring and fallback handling when the model is uncertain; human-in-the-loop review gates for high-stakes outputs; and continuous monitoring through SEOP that tracks output quality as a first-class delivery metric. No LLM system eliminates hallucinations entirely, but well-engineered systems contain them to acceptable rates for their use case.

Under 21 days from signed contract. We start from a permanent bench of senior AI engineers, not from a post-contract recruitment race. The 21-day window covers discovery, architecture design, environment setup, model selection, and first sprint kickoff. Focused integrations with clear scope can move faster. Complex enterprise systems with multiple data sources and compliance requirements take longer to architect, but not longer to staff.

We deploy specialised 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 the Scrums.AI Gateway with unified governance and visibility.

Pilot projects with focused automation, meaning a single team and a single workflow, typically deploy in 2 to 4 weeks with measurable results inside the first month. Standard implementations across multiple teams and workflows take 2 to 3 months from discovery to production rollout. Enterprise-wide programmes run 6 to 12 months with phased deployment, change management, and continuous optimisation. Timeline depends on integration complexity, team size, and organisational automation maturity.

No. AI agents augment human capability rather than replace it. 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% to 60% with the same headcount.

Yes. We have experience fine-tuning open-source models (Llama, Mistral) on client-specific datasets for domain adaptation, tone consistency, and task-specific accuracy. Fine-tuning requires sufficient labelled training data, a well-defined evaluation framework, and an MLOps pipeline for model versioning, deployment, and performance monitoring. We advise on whether fine-tuning or RAG is the better approach for your use case before committing to either.

Data handling architecture is designed before any code is written. For regulated clients this typically means keeping sensitive PII out of third-party model API calls by pre-processing or redacting inputs; deploying open-source models in self-hosted infrastructure where data sovereignty requires it; implementing role-based access controls on AI outputs; maintaining full audit logs of model inputs, outputs, and decisions; and aligning with SOC 2, GDPR, POPIA, and ISO 27001 where applicable. We do not treat compliance as a post-launch checklist item.

All AI agents deploy through the Scrums.AI Gateway with enterprise-grade security: data sovereignty controls, so 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, and HIPAA standards; and encrypted communication channels. For regulated industries we support on-premise deployment, bring-your-own-model architectures, and custom compliance configurations.

All agent actions are monitored, logged, and reviewable through SEOP dashboards. Critical operations such as production deployments and code merges have human approval gates, configurable to your risk tolerance. Agents include rollback capabilities, and our team monitors agent performance continuously to identify and correct errors quickly. We use gradual rollout, with pilot teams first, so issues are caught before organisation-wide impact. Agent accuracy improves over time through feedback loops and continuous training.

Typical results from our 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% to 60% cost savings versus scaling headcount. Most enterprises reach positive ROI within 3 to 6 months of deployment. We establish baseline metrics before implementation and track improvement continuously through SEOP analytics.

Yes. The 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 change, which minimises disruption and speeds up adoption.

No. Our implementation teams provide all necessary AI expertise, including agent configuration, workflow design, integration setup, training, and ongoing optimisation. We train your team on capabilities and best practices, but you do not need data scientists, ML engineers, or AI specialists in-house to benefit. Most successful clients have standard development teams who learn to work effectively with AI through our adoption programmes.

Production AI systems need attention that traditional software does not. Model providers release new versions, sometimes with breaking changes. Prompt performance drifts as user behaviour evolves. RAG retrieval quality degrades as content goes stale. Token costs change as usage scales. Our ongoing maintenance covers model version evaluation and migration planning, prompt optimisation based on production usage, RAG corpus refresh and retrieval quality monitoring, cost optimisation as scale increases, and performance benchmarking against your quality thresholds. This is what keeps an AI feature performing at launch quality twelve months later.

SEOP provides real-time dashboards tracking agent-specific metrics: automation coverage percentage, task completion accuracy, time saved versus 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. Quarterly business reviews assess ROI, identify optimisation opportunities, and plan capability expansion.

Yes, and we recommend it for organisations new to AI. A typical pilot focuses on a single high-impact use case, such as QA automation for one team or a single LLM feature, and runs 1 to 2 months to demonstrate value quickly. Successful pilots expand incrementally based on measured results and team feedback. This reduces risk, builds confidence, and proves value before major investment.

It depends on team composition, model strategy, integration depth, automation scope, and compliance requirements. As industry planning ranges: a focused LLM feature is roughly $50K to $150K; a full AI product build is $300K to $800K annually; an enterprise AI platform is $800K+ annually; an augmented AI specialist is $6K to $15K per engineer per month. For SDLC automation, a pilot is roughly $25K to $75K, a standard multi-team programme $75K to $200K, and an enterprise programme $200K to $800K+. These are planning ranges, not quotes. Permanent African AI engineering talent combined with AI-augmented delivery produces production-grade systems at 40% to 60% lower cost than US and UK firms.