Illustrative example. The organisation, people and events below are fictional.
How it could work in a care provider
The setting
A care provider runs six homes with 240 staff. It has safeguarding policies and training, but staff say they would not report a colleague or a manager. "We are a small team, and everyone knows everyone."
The report
A care assistant sees a senior colleague handle a resident roughly. They open the reporting page, describe what happened, say the risk is ongoing and choose to stay anonymous.
What happens next
The report is flagged for urgent review by a person. The safeguarding officer follows the organisation's procedures, asks follow-up questions through the anonymous inbox, and every step is recorded.
Spotting patterns
Over time, anonymised analytics show recurring themes by category or location. A count of reports is not proof of wrongdoing; each concern is assessed on its own merits.
SpeakToUsNow provides the reporting route. Trained safeguarding professionals decide what action to take. Immediate risks must still go through emergency and safeguarding channels.
Recruitment · HR
Faster CV screening for an HR team hiring at volume
Client
A busy HR department. Name not disclosed.
The problem
The organisation employs many people on short-term contracts, so the team was inundated with CVs and had to read them all by hand.
The brief
Automate the first stage of screening, so the team could spend its time on the strongest candidates.
What I built
Automated CV scoring against a rubric: pass/fail gates for must-have requirements, then weighted criteria to rank everyone who passes.
Two-stage shortlisting, with human review and manual overrides at every stage.
Rubric templates, generated from a job description in one click and version-controlled.
AI voice interviews that ask every candidate the same structured questions, with automatic transcripts.
Justification interviews to check the claims on a CV before an offer goes out.
A central candidate pool with tags and search. Upload a CV once and score it against several roles.
Full scoring breakdowns and evidence trails, so every decision can be explained, plus PDF and CSV exports.
The AI does the heavy lifting, but people make the decisions.
SiftCV is now available to other organisations, with a 14-day free trial.
Illustrative example. The company, people and figures below are fictional.
How it could work for a growing consultancy
The setting
An 85-person consultancy needs three business analysts and gets 240 applications in two weeks. One HR manager handles recruitment alongside everything else.
Scoring the CVs
SQL and stakeholder skills are must-haves; Power BI and Agile experience are nice-to-haves. The recruiter checks the evidence behind each score, especially for unusual career paths, and shortlists 24.
Voice interviews
All 24 answer the same competency questions. Voice, accent and perceived personality play no part, and other formats are available for anyone who needs them. Eight reach the final interview and three get offers.
People stay in charge
A score is not proof that one candidate is better than another. Candidates are told how AI is used and can ask for a human review.
Sales · CRM
An affordable CRM to grow a small consultancy's pipeline
Client
A small consultancy. Name not disclosed.
The problem
The firm wanted to grow its sales pipeline, but software built for big companies would have cost a fortune.
The brief
Bespoke software, at a price a small firm could justify, to track every lead and move more of them through to a sale.
What I built
Contact and company records with tags, custom fields and relationship tracking.
Lead capture from the website, with AI scoring that puts the most promising prospects first.
A drag-and-drop pipeline with stages that match the firm's sales process.
Follow-up automation: actions triggered by deal stage, plus reminders and a built-in calendar.
Email campaigns to targeted segments, with open and click tracking.
AI deal-health scores, next-best-action suggestions and dashboards that show the whole pipeline at a glance.
Smart insights, deal scoring and automation included, not sold as expensive add-ons.
Illustrative example. The company, people and events below are fictional.
How it could work for a maintenance company
The setting
An 18-person maintenance firm gets about 80 enquiries a month. They sit in a spreadsheet, two salespeople's lists and engineers' phones, and nobody can say what is in the pipeline. "I couldn't tell you which ones are likely to come in."
One opportunity
A landlord asks for a quote to maintain several properties. It becomes a deal in the pipeline, the quote goes out and a follow-up is set for five working days later.
The follow-up
The reminder comes up, the salesperson calls, and finds the landlord needs a slightly different arrangement. A revised quote wins the contract.
Scoring the leads
AI scoring compares enquiries using customer history, enquiry details and past outcomes. A high score means "look at this soon", not "this will win". The team adds what it knows about each customer.
The CRM didn't win the contract. The salesperson did, because they had the information and the reminder at the right time.
IT support · Charity
One place for IT tickets and equipment, for a UK charity
Client
A UK charity. Name not disclosed.
The problem
The charity had no single system for handling IT support requests or for keeping track of its equipment.
The brief
A bespoke app to manage IT tickets and an asset library together.
What I built
A help desk where staff raise tickets and follow their progress in real time.
AI that categorises and routes tickets, suggests replies and raises the priority of urgent ones, with the team in control.
An asset library: devices and their specs, software licences, and who each item is assigned to.
Asset-aware troubleshooting, so every ticket shows the device and software behind it.
A self-service knowledge base, with AI that spots common problems no article covers yet.
SLA tracking by department, automatic escalations and performance dashboards.
AI categorises, routes and suggests responses, while the team stays in control of every ticket.
ResolvedIQ is now available to other organisations, with free onboarding and a free demo.
Illustrative example. The company, people and events below are fictional.
How it could work for a growing firm
The setting
A 180-person firm across three offices has an IT team of three. About 300 requests a month arrive by email, phone, Teams and in the corridor, and the equipment spreadsheet is out of date. "Sometimes three of us end up looking at the same issue, while another request sits unanswered."
One ticket
An analyst can't open a reporting app before a client deadline. The ticket is routed and prioritised, and the engineer sees the device and software straight away. A recent update caused the same fault for someone else, so the fix is quick.
The asset library
Each device record holds its spec, operating system, warranty, software and past tickets. A laptop with repeat problems stands out, and replacements, licences, starters and leavers are easier to plan.
Fewer repeat questions
Password resets, multi-factor sign-in and printer set-up go into the knowledge base, so staff can search before raising a ticket. The team reviews the AI's suggestions for new articles.
ResolvedIQ doesn't fix the fault itself. It gives the engineer the context to fix it with less guesswork.
