Solutions · AI Development
Artificial intelligence, practically applied
Not gimmicks. Assistants that answer your customers at 2am, automation that clears the admin pile, and insight pulled from data you already own — measured in hours saved and leads won.
First, an honest question
Does this actually need AI?
Often it doesn't. Plenty of problems badged as AI are solved faster and more reliably with rules, a better form or a clearer process — and we'll say so before recommending anything more expensive.
AI earns its place where the input is messy: reading, summarising, classifying and drafting from text that people currently have to wade through by hand.
AI is right when
- There's a lot of unstructured text — emails, notes, documents, enquiries.
- Triage needs judgement — sorting, routing or prioritising by what something actually says.
- Replies are repetitive but not identical — so templates never quite fit.
- Answers are buried in your documents — and people ask the same questions daily.
- Notes need to become records — a voice memo or call summary turned into structured data.
The Journey
From curiosity to working AI — safely
AI projects fail when they start with the technology. Ours start with your workload.
We hunt for hours, not hype
Together we list the tasks that eat your team's week — quoting, answering the same questions, chasing paperwork, summarising documents. We score each for AI-fit and pick the one with the clearest payback. That's the pilot.
- Free AI opportunity audit
- Ranked by hours saved
- Honest "AI won't help here" advice included
A working pilot you can poke
Within weeks you're testing a real assistant trained on your services, prices and tone of voice — on your own phone. You'll see exactly how it handles real customer questions before you commit to anything bigger.
- Working pilot in weeks
- Trained on your business
- Judge it on real conversations
"We asked it the ten questions customers always ask. It got ten out of ten — politely."
Wired into your world
The pilot graduates: connected to your website, diary, CRM and inbox, with guardrails so it always knows when to hand over to a human. Privacy comes first — your data stays yours and is never used to train public models.
- Human handover built in
- Privacy & GDPR by design
- Works inside your existing tools
Judged by numbers, not novelty
We agree the metrics before launch — response times, admin hours saved, leads captured out of hours — and report on them plainly. If a feature isn't earning its keep, we say so and fix it.
- Agreed success metrics
- Monthly plain-English reports
- Continuous tuning included
One win leads to the next
Once the first assistant is paying for itself, we look at the next task on the list — document drafting, photo assessment, forecasting. AI at Digenixa is a ladder you climb at your own pace, not a leap of faith.
- Roadmap reviewed quarterly
- New capabilities as models improve
- Always your call, never a push
How we work
Three frameworks holding the project up
One for how we engage, one for how the AI is built safely, one for how it's proven and rolled out — because an AI feature has to earn trust before it earns scale.
Engagement
A small, fixed-fee proof of value comes first, tested on your own real examples. If it doesn't clear the bar we agree up front, you've spent a little to learn a lot — and we stop.
- Success threshold agreed before we start
- Tested on real, anonymised examples
- Honest go/no-go at the end
- No build commitment until it's proven
Architecture
A person stays in the loop for anything customer-facing: the AI drafts, somebody approves. Every AI step has a fallback so the system keeps working if the model is unavailable.
- Human approval for customer-facing output
- Fallbacks when the AI is unavailable
- Clear rules on what data is sent where
- Accuracy checked against a test set
Delivery
Prototype on real examples, pilot with a small group, measure, then roll out. Each stage has to earn the next one.
- Evaluation set kept and re-run
- Pilot with a small group first
- Cost per task tracked from day one
- Monitoring for quality drift
The process
Seven phases, idea to trusted tool
Durations are typical, not promises — scope, integrations and the state of what already exists move them more than anything else. Discovery gives you a version of this table with your own numbers in it.
| Phase | Focus | What you get | Typical |
|---|---|---|---|
| 1. Discovery & use-case choice | Where time goes, which tasks suit AI, what good looks like | Ranked use cases, success measures, data review | 1–2 weeks |
| 2. Proof of value | A working prototype on your real examples | Prototype, accuracy results, go/no-go recommendation | 2–4 weeks |
| 3. Build | The production feature, interfaces, approvals and logging | Working feature on staging, admin controls | 4–10 weeks |
| 4. Integrate | Connecting to the systems where the work actually happens | Integration map, data-handling documentation | Overlaps build |
| 5. Evaluate & pilot | Test set, safety checks, a small group using it for real | Evaluation report, pilot feedback, go/no-go | 2–4 weeks |
| 6. Roll out & train | Wider release, training on when to trust it and when not to | Live feature, guidance for users | 1–2 weeks |
| 7. Monitor & improve | Accuracy, cost, feedback and new use cases | Monthly quality and cost report | Ongoing |
The proof of value is deliberately small. It's the cheapest point at which to discover that a task isn't a good fit for AI — and sometimes that's the most useful thing we can tell you.
What we build
AI with a day job
Customer assistants
Website chat that genuinely answers — quotes, availability, FAQs — and books the job or hands to a human. Try ours: it's the bubble in the corner.
Smart quoting & assessment
Customers describe (or photograph) the job; AI drafts the estimate using your pricing rules. You approve, they book.
Admin automation
Drafted replies, summarised threads, auto-filled forms and documents — your inbox on easy mode.
Voice & notes
Site notes spoken into a phone become tidy job records, quotes and follow-up emails.
Insight & forecasting
Your years of jobs, quotes and invoices turned into answers: what to stock, when to hire, which work wins.
Computer vision
Photo-based assessments, measurements and quality checks — from roof surveys to document processing.
Why Digenixa
The benefits you'll actually notice
- Answers out of hours — enquiries handled and leads captured while you sleep.
- Payback you can point at — every deployment measured in hours and leads.
- Private by default — your data stays yours; guardrails and human handover as standard.
- Start small, grow smart — pilot first, expand only when it's earning.
Handover
What you actually walk away with
A project ends properly or it doesn't end at all. This is what "finished" means with us.
You receive
- The AI feature live and in use
- Source code, prompts and configuration
- The evaluation set and its results
- Data-handling documentation
- A running-cost model
- A runbook, including what happens if the AI is down
How it's delivered
- Tested on your real examples from the start
- A demo at each stage, not just at the end
- A pilot before anything goes wide
- Approval steps for customer-facing output
- Close support after roll-out
- Monthly quality and cost reporting
How we measure it
- Hours saved on the target task
- Accuracy against a human baseline
- How often people correct its output
- Response time
- Running cost per task
- How the people using it rate it
Commercials
Priced so the risk sits with us
Every project is quoted individually — but the shape is always the same.
Proof of value
Fixed fee, short and focused. You get working evidence and a straight recommendation.
Build
Fixed price per phase, approved one at a time, once the proof of value has cleared the bar.
Run
Monthly support and monitoring. AI usage costs are shown to you plainly, so you always know what each task costs.
Changes are normal — you'll learn things partway through that you couldn't have known at the start. Each one gets a short written note on cost, time and knock-on effects, and nothing starts until you approve it.
Straight answers
What clients ask before they commit
Is our data used to train the AI?
We use commercial AI services whose business terms don't use your data to train their models, and we'll walk you through those terms before anything is built. We also minimise what's sent in the first place.
What if it gets something wrong?
It will, sometimes — so it's designed for that. Customer-facing output goes to a person to approve, accuracy is measured against a test set, and anything it's unsure about is flagged rather than guessed.
What happens if the AI service is down?
The feature falls back to a non-AI route — a template, a queue or a person — so your process carries on. It's one of our standard engineering rules.
What does it cost to run?
Usage costs depend on volume and are usually small per task. We model them during the proof of value and report them monthly, so there are no surprises.
Can it use our own documents?
Yes. It can search and answer from your documents, with the source shown alongside each answer so people can check it.
Will it replace people?
It's best at removing the tedious part of someone's job — reading, sorting, first drafts — so they spend their time on the judgement only they can make.
See AI answer your customers' questions — live
In your free demo we'll point an assistant at your business and let you interrogate it. Bring your trickiest customer question.
Book a Free Demo