Trusted for Laravel, WordPress, ecommerce, automation & AI delivery
Fix messy systems. Ship useful AI. Move faster.
Nought Digital helps agencies, founders, and growing teams rescue
Laravel, WordPress, ecommerce, and internal systems — then adds
practical AI automation where it actually saves time.
We focus on measurable operational improvement — not vanity AI
demos.
Case Study: Support workflow automation
Support workflow automation for a growing team
A growing team had thousands of support tickets and too much manual triage. We mapped the workflow, identified repeated ticket patterns, built an AI-assisted triage system, and improved internal reporting — reducing manual handling and giving the team clearer visibility over recurring issues.
Client testimonial:
“They didn't just bolt on a chatbot. They understood our workflow, automated the parts that were eating our time, and gave us reporting we actually use.”
— Head of Customer Success, B2B SaaS
1
Mapped the workflow
We reviewed real tickets to find the repeated patterns and the genuine manual bottlenecks.
2
AI-assisted triage
Routine queries are categorised and drafted automatically; complex cases escalate to a human with full context.
3
Clearer reporting
Internal dashboards surface recurring issues so the team can fix root causes, not just respond.
2,000+
tickets reviewed
8 weeks
of support data analysed
41%
reduction in repeat issues
£35k
estimated operational value
Results vary by project. We focus on measurable operational improvement, not vanity AI demos.
Case Study: Legal Document Processing
From 40 hours/week to 2 hours: 95% time reduction in 6 weeks
A law firm was spending 40 hours/week on document review. We built an AI system that processes contracts, extracts key terms, and flags potential issues automatically.
Client testimonial:
“This system has transformed our practice. What used to take our junior associates days now takes minutes. The accuracy is incredible.”
— Managing Partner, Law Firm
1
Automated extraction
AI extracts key terms, dates, and clauses from legal documents.
2
Risk flagging
Automatically identifies potential legal risks and inconsistencies.
3
Smart summarisation
Generates concise summaries highlighting key points and action items.
~95%
less review time
3x
faster processing
6 wks
to first rollout
0
downtime at deploy
Indicative figures from the engagement. Results vary by project and document volume.
Case Study: E-commerce Personalisation
From 12% to 34% conversion: 183% increase in 10 weeks
An e-commerce retailer was struggling with low conversion rates. We built an AI-powered personalisation engine that delivers tailored product recommendations and dynamic pricing.
Client testimonial:
“The AI personalisation system has been a game-changer. Our conversion rates have nearly tripled, and our average order value is up 45%. This is exactly what we needed.”
— Head of Digital Marketing, Retail Company
1
Smart recommendations
AI analyses user behaviour to suggest highly relevant products.
2
Dynamic pricing
Real-time price optimisation based on demand and inventory levels.
3
Behavioural targeting
Personalised content and offers based on individual user patterns.
Higher
conversion rate
Higher
average order value
10 wks
to first rollout
0
downtime at deploy
Indicative outcomes from the engagement. Results vary by project, traffic, and catalogue.
Results vary by project. Figures shown are indicative of the specific
engagement and are not a guarantee of future outcomes.
Proof of capability
Recent builds and experiments
Quiteful
Filterable allergen-aware menu and local guide platform for restaurants, hosts, and travellers.
Next.js Product Data
A Little Bit Nuts
Ecommerce experience and operational tooling for a product-led food brand with stock, content, and fulfilment needs.
Ecommerce Automation Operations
ForageMap
Location-aware mapping product for discovering, organising, and publishing useful local food data.
Maps Product Data
Xlistr
Marketplace listing and inventory platform for sellers managing product data across multiple channels.
Laravel Filament Marketplaces
The ORBIT Framework
Outline, research, build, integrate, test
A practical loop for turning messy systems and AI ideas into working
software without pretending a project is linear.
Every engagement is outcome-scoped. Fixed fees. No timesheets. This
ladder gives you a lower-risk way to start a useful working relationship.
How we use AI
AI where it helps. Engineering where it matters.
We use AI to speed up analysis, documentation, automation, testing,
and repetitive workflows — not to blindly generate fragile
systems. Private code and business data are handled carefully, and
production changes are reviewed like normal engineering work.
No blind AI code dumps
Human-reviewed implementation
Private-code-conscious workflows
Local-first options available
Clear rollback and testing approach
AI used for measurable operational outcomes
Frequently Asked Questions
What do you actually do?
We parachute into broken and stalled projects, find the real blockages, and ship a simple version that works. The system uses AI for speed and humans for judgment. We improve it every week until it delivers clear business results.
Will this replace my team?
No. The best results come from AI + humans together. AI drafts, sorts and fetches; your people approve, decide and handle exceptions. The goal is fewer repetitive tasks and more time for high-value work.
How do we stay in control and avoid “rogue” AI?
Critical actions always need a person to approve them — no auto-send emails, orders or code changes without a human click. Every run is logged so you can see who did what and when. If confidence is low, the system asks for help or falls back to a safe answer.
Is our code or data safe?
Yes. We can work inside your existing repo, staging environment, or private systems under NDA. For AI-assisted work, we avoid sending sensitive code, credentials, customer data, or private business logic to external tools unless explicitly agreed. Local-first workflows are available where required. We don’t sell your data or use it to train public models.
What happens when the AI gets it wrong?
The system is built to catch mistakes. People can review and correct outputs, sources are shown where possible, and changes are tracked. We roll out gradually to a small group first, watch for issues, and only then expand.
How long until we see something working?
Most clients see a working demo in 2–4 weeks. In week one we set goals and risks; in weeks two to four we ship a focused MVP you can use. Then we iterate based on real feedback.
Will this work with our tools and data?
Yes. We plug into what you already use — email, docs, chat, CRM, data stores — so you don’t have to rebuild your stack. If something isn’t available, we agree a simple workaround instead of stalling the project.
What does it cost?
We price by outcome, not hours. Starter projects from £1,500 cover small websites, audits, or focused fixes, with simple maintenance from £200/month. Sprints start from £5k, larger build partnerships from £15k+, and technical retainers from £1,500/month.
Who owns the work?
You do. Code, prompts, playbooks and documentation are handed over at the end, along with training for your team.
What if we already have a dev team?
Perfect. We plug in beside them — handle the AI architecture, pipelines, or tricky integrations so your team can focus on what they do best.
Got a messy system, broken workflow, or AI idea that needs shipping?
Send a few details. If it is a fit, we’ll suggest the smallest
sensible next step — usually an audit or focused sprint.
No pressure. No vague strategy deck. Just a practical conversation about
what needs fixing.
Values
We like working with useful, ethical, founder-led businesses
Nought Digital is happy working with ambitious agencies, SaaS
teams, ecommerce businesses, and local operators —
especially when the work improves real operations rather than
adding noise.