Build Your AI-Augmented Software Delivery Factory
We design and implement a complete AI-driven delivery pipeline with human-in-the-loop governance: from idea to requirements, user stories, code, CI/CD, and cloud infrastructure (Azure/AWS/GCP)—all with built-in security, compliance and human oversight.
Most organisations struggle to deliver change quickly and safely. We solve this by building a complete, AI-driven delivery factory with human-in-the-loop governance at critical gates: from idea to requirements analysis, user story generation, work item creation, TDD scaffolds, code, pipelines, and infrastructure-as-code across any cloud platform (Azure, AWS, or GCP). AI handles the heavy lifting, humans make the decisions, developers move faster, security is baked-in, and leadership gets auditability and predictable outcomes.
Who This Is For
Public sector, regulated industries, and any team that must deliver software quickly with strong governance (security, privacy, audit, procurement rules, and budget oversight).
Outcomes
Measurable benefits without hype
Faster delivery with fewer handoffs and less toil
Consistent architectures and repeatable deployments
Built-in security, privacy and compliance guardrails
Lower cloud risk via policy-enforced Terraform modules
Clear audit trail of prompts, code, tests and releases
Happier engineers: one paved road, less yak-shaving
What We Build
Core components of your delivery factory
AI Requirements & Planning Engine
- AI-generated Product Requirements Documents (PRDs) and technical specifications
- Architecture Decision Records (ADRs) with trade-off analysis
- Automated user story generation with acceptance criteria and estimates
- Work item creation in Azure DevOps, Jira, or GitHub Projects
- Dependency mapping and sprint planning assistance
AI Development Layer (multi-model, governed)
- Adapters for Azure OpenAI / OpenAI / Anthropic / Groq (swappable models)
- Guardrails: prompt hardening, PII redaction, toxicity filters, jailbreak and data-leak prevention
- Prompt & output logging with retention, replay and masking
- Policy controls for data residency, safe use and licence compliance
Developer Experience
- TDD-first blueprints: AI scaffolds tests, code, docs and ADRs
- Repo patterns (mono/multi), protected branches and PR templates
- Code quality & security: linters, SAST/DAST, dependency/licence checks
- Ephemeral preview environments for each PR
Pipelines (CI/CD)
- GitHub Actions/Azure DevOps pipelines with mandatory quality gates
- Build, test, scan, SBOM, artefact versioning, release promotion
- IaC scanning and drift detection
Infrastructure-as-Code (Terraform)
- Reusable, pre-approved modules for landing zones and workloads
- Plan → policy review → apply with change approval
- Cost tags, budgets and auto-destroy for non-prod
Cloud Platform Architecture
- Multi-cloud support: Azure, AWS, GCP with native equivalents
- Compute (App Service/Lambda/Cloud Run), Serverless Functions, Containers
- Database (SQL/NoSQL), Storage, Caching, Message Queues
- API Gateways, CDN/WAF, Private Networks, VPNs
- Identity & Access Management, Secrets Management, Encryption
- Observability: Application monitoring, logs, dashboards, alerts
Human-in-the-Loop Gates
- Requirements approval: Review AI-generated PRDs and architecture decisions
- Story approval: Validate user stories, priorities and sprint planning
- Code review: Mandatory human review before merging AI-generated code
- Deployment approval: Human sign-off before production releases
- Audit trail: All human decisions logged with reasoning and timestamps
Governance & Audit
- End-to-end traceability—from idea, prompt and commit to release
- DPIA/ethical use patterns, model cards, risk register hooks
- Change logs and evidence packs for assurance and audit
The Delivery Flow
16 stages from idea to production with human oversight
Click any stage to explore, or let it auto-play
Explore Each Stage
Click any stage above or use the controls below
Idea / User Input
Human DecisionEvery great product starts with an idea and clear business goals.
- 1Capture business requirements and user needs
- 2Define success criteria and constraints
- 3Identify stakeholders and compliance requirements
- 4Document initial scope and objectives
How We Work
Engagement flow from discovery to operation
Discover & Baseline
Goals, constraints, standards, current repos/pipelines.
Target Architecture & Guardrails
Agree the "paved road", security and compliance controls.
Build the Factory
AI adapters, repo patterns, pipelines and Terraform modules.
Pilot a Real Service
Run a thin-slice from idea → production using the new line.
Handover & Upskill
Playbooks, training, templates, runbooks, and knowledge transfer.
Operate & Improve
Optional support, platform backlog and periodic reviews.
Service Tiers
Choose the right level for your needs
Foundation
Baseline assessment, target architecture, minimal paved road (repo+pipelines), seed Terraform modules, AI guardrail gateway with human-in-the-loop approval gates, cloud-agnostic templates (Azure/AWS/GCP), one pilot workload.
Ideal for: Teams getting started with AI-assisted delivery
Plus
Everything in Foundation + observability dashboards, cost controls, API gateway & networking patterns, multi-cloud deployment templates, additional workload templates, training for engineers and product teams.
Ideal for: Organizations scaling beyond proof-of-concept
Enterprise
Everything in Plus + multi-model AI routing, advanced governance (model cards, DPIA templates, policy packs), multi-tenant patterns, cross-cloud migration playbooks, dedicated human approval workflows, ongoing platform backlog & support.
Ideal for: Large organizations with complex compliance needs
Frequently Asked Questions
Will our code or data be sent to third-party models?
Only if you choose to. The AI layer enforces data-handling rules (masking/redaction), logs prompts/outputs, and can be restricted to sovereign or private endpoints.
What does "human-in-the-loop" mean in practice?
AI generates requirements, stories, and code—but humans review and approve at key gates: after requirements, after story generation, during code review, and before deployment. Every decision is logged with full audit trails.
Which cloud platforms do you support?
We support Azure, AWS, and GCP with native equivalents. The factory patterns are cloud-agnostic, so you can choose your preferred platform or even operate multi-cloud.
Does this lock us into a specific LLM vendor?
No. We abstract the model interface so you can switch providers (OpenAI, Anthropic, Azure OpenAI, AWS Bedrock, etc.) without refactoring applications.
How do you ensure security and compliance?
Security is in the path: RBAC, identity management, secrets vaults, policies, network isolation, SBOMs, SAST/DAST, IaC scanning, human approval gates and full audit trails.
What changes for our developers?
They use the paved road: templates, PR checks, automated tests, preview environments and standard modules. AI accelerates their work, but they remain in control. Less ceremony, more delivery.
Ready to Build Your Delivery Factory?
Start with a discovery call to see your current pipeline and risks in 60 minutes