AI Integrated Software Delivery
Built in. Not Bolted on
At Cleverbit, AI isn’t a novelty or a shortcut.
We build and test our own AI tools, embed only what improves outcomes, and apply them within strict engineering standards and governance.
The result: faster delivery without compromising quality, intent, or accountability.
AI as a force multiplier
At Cleverbit, AI is a system component of our software development process and the broader lifecycle. We define exactly where it adds value, from early prototyping to test generation, documentation and review support, often through internal AI agents built to reinforce how our teams deliver.
AI is deliberately applied across:
- Scaffolding and structured prototyping
- Test generation and coverage expansion
- Documentation and knowledge capture
- Code review and refactoring support
Every use case has clear expectations, constraints and measurable outcomes. We benchmark performance continuously to ensure AI improves speed, quality and consistency… not just activity.
Avoiding “Vibe code drift”
One of the biggest risks of AI-generated code isn’t that it’s obviously wrong, it’s that it looks right. Clean syntax. Confident logic. Subtle flaws.
That means:
Teams can begin trusting output because it feels correct, not because it has been properly verified. Over time, that erodes code quality and increases long-term risk.
Our process prevents that. AI outputs are treated as inputs to engineering judgement, not replacements for it.That means:
- AI-generated code is always reviewed against intent
- Assumptions are explicitly validated
- Logic is traced, not skimmed
- Human engineers remain accountable for what ships
- Reviews stay rigorous, not performative.
Regenerate, don’t patch
When AI gets something wrong, the instinct is often to patch it; tweak a few lines and move on. That approach leads to tangled logic and incoherent systems over time.
We favour regeneration over patching. When something doesn’t meet the bar, we reset context, clarify objectives, and regenerate cleanly.
Instead of incremental fixes, we prioritise:- Clear problem definition
- Simplified context
- Clean regeneration
- Structural coherence
The result is software that’s faster to build and easier to maintain – without sacrificing quality.
Humans where it counts
AI is exceptional at speed, breadth and synthesis. It can explore options, analyse constraints and generate solutions rapidly. What it cannot do is decide what matters.
At Cleverbit, humans stay firmly in charge. Engineers and product leaders define the problem, make the trade-offs and take responsibility for outcomes.
Our model ensures:- Humans define direction and intent
- AI accelerates execution
- Critical decisions remain human-owned
- Governance sets clear boundaries
- AI supports judgement. It never replaces it.
Benefits
Guardrails that enable speed
We don’t believe in AI free-for-alls. We also don’t believe in banning tools out of fear. The answer is clear guardrails.
We selectively embed AI tools that demonstrably add value, integrating them into teams in a governed way rather than leaving adoption to individual preference or chasing the latest release.
Our delivery model intentionally varies how AI is used depending on the stage and risk profile:
Fast exploration in early prototypes, UI concepts and idea validation
Strict rigor in core logic, security-sensitive areas, and financial systems
Defined standards for prompt quality, code review, and what “AI-assisted” actually means
Automated safety through CI checks, static analysis, and vulnerability scanning
What this means for you
When you work with Cleverbit, you’re not just getting a team that uses AI. You’re getting a team that has engineered AI into the way software is delivered. Behind this consistency is a delivery model where AI usage is measured, adjusted, or removed based on real performance data, not assumptions.
That means:
- Faster delivery without cutting corners
- Higher consistency across teams and codebases
- Lower long-term risk from AI-generated technical debt
- Clear ownership, accountability and quality standards
AI isn’t the headline. Better outcomes are.
let's build it properly
If you want AI to compound your engineering capability, we should talk.
Frequently asked questions
How do you use AI without compromising code quality?
AI is treated as a delivery accelerator, not an authority. All generative AI output and AI code is subject to the same engineering standards, reviews, and testing as human-written code. We use AI where it adds leverage, such as scaffolding, test generation, and documentation, while humans retain ownership of intent, logic and final decisions. Guardrails prevent “vibe-code drift” and long-term technical risk across the development workflow.
What guardrails are in place for AI usage?
AI usage is governed by clear standards across the delivery lifecycle. This includes defined use cases, prompt hygiene guidelines, review expectations, and automated safety checks such as CI validation, static analysis, and security scanning. The goal is not restriction, but consistency, predictability and trust when teams integrate AI into their delivery process.
Who owns the IP created with AI?
You do. All code; documentation, AI-generated outputs, and derived artefacts produced during delivery are owned by you. AI is used as a tool within your delivery system, not as a separate or proprietary layer, ensuring there are no hidden IP or licensing risks.
How do you prevent over-reliance on AI?
We design for human ownership at every critical decision point. AI explores options, accelerates execution, and surfaces alternatives. Humans define the problem, make trade-offs, and sign off on what ships. This balance ensures teams don’t lose engineering judgement, accountability, or architectural coherence over time.
Can AI practices scale safely as teams grow?
Yes, because they are designed to scale. AI standards, governance, and workflows are embedded into onboarding, reviews, and team structures. This ensures new engineers adopt the same practices and quality bar from day one, allowing teams to scale without inconsistency or quality drift.
What if our organisation has strict compliance or security requirements?
Our AI usage model is compatible with regulated and security-sensitive environments. We adapt tools, data access, and workflows to meet compliance, data sovereignty, and security constraints. AI is applied within clearly defined boundaries, ensuring regulatory requirements are respected without sacrificing delivery speed.
