AI Projects
Led by a senior AI lead, from first idea to measured result
AI Projects finds where artificial intelligence can save your teams time or money, then builds and rolls out working solutions on your own data. Each project is led by a senior AI lead who stays accountable from first idea to measured result.
From AI experiments to AI that works
Most companies already use AI informally: staff paste emails into chatbots or try free tools. Few have AI working inside a real process, measured and governed. What usually holds them back is not the technology but picking the right problem, preparing the data and changing how people work.
AI Projects works in three stages: a discovery sprint to find and rank use cases, a pilot that proves the value on your own data, and a rollout with training, support and an AI use policy.
What the AI lead does
Discovery
- Interviews teams and maps how work gets done
- Checks what data you have and its quality
- Ranks use cases by value, effort and risk
Build
- Chooses the right tools for each use case
- Builds the pilot with the team that will use it
- Measures results against a baseline
Rollout
- Connects the solution to your systems
- Trains users and sets up support
- Monitors quality and cost over time
Governance
- Writes your AI use policy and approved tools list
- Sets rules for data, accuracy and human review
- Reports progress and risks to leadership
How your company benefits
Hours back every week
Automating repetitive reading, writing and searching frees staff for work that needs judgement.
Proof before you scale
A pilot measured against a baseline shows whether a use case pays off before you commit more.
Safer use of AI
An AI use policy and approved tools cut the risk of staff pasting confidential data into public tools.
Your data, your advantage
Solutions built on your own documents and history do things off-the-shelf tools cannot.
Skills that stay
Your team builds the solution with us and is trained to run and improve it.
Signs you need an AI project
Tick the ones that sound like your company.
How it works
From idea to rollout
- Weeks 1 to 3
Discover
Interview teams, map the work, check the data and agree a ranked shortlist of use cases with you.
- Weeks 4 to 12
Pilot
Build the top use case on your own data with the team that will use it, and measure it against today's baseline.
- After the pilot
Decide and roll out
Review the results together, then roll it out or stop, and put the AI use policy in place.
What you receive
- AI opportunity report with ranked use cases
- Data readiness assessment
- Working pilot on your data
- Baseline and results report
- Rollout plan and support model
- AI use policy and approved tools list
- User guides and training
Use cases we deliver
Open a use case to see the problem it solves, what you need and how success is measured.
- Document processingRead invoices, delivery orders and forms, and turn them into clean data in your systems.Read the use case
- Customer service assistantDraft accurate replies to customer emails and chats from your own policies and order data.Read the use case
- Knowledge searchLet staff ask questions in plain language across policies, manuals and past work.Read the use case
- Proposal and tender draftingProduce first drafts of proposals, tenders and questionnaires from your best past work.Read the use case
- Demand forecastingForecast demand and stock needs from sales history, seasons and events.Read the use case
- Reporting and meeting automationTurn meetings and raw data into summaries, action lists and weekly reports.Read the use case
Ways to engage
Discovery sprint
Find and rank the use cases worth doing, with value and effort estimates.
Best when you are starting out.
Pilot project
Build one use case, measure it and decide.
Best when you know the problem to solve.
AI advisory
Steer AI work across the company, including vendors and policy.
Best when several AI efforts need one direction.
How success is measured
- Hours saved per week
- Processing time per document or request
- Error rate against the manual baseline
- Active users
- Cost per transaction
When a full-time hire makes more sense
- AI is central to your product or service.
- You run several AI products in production at once.
- You have an in-house data team that needs daily leadership.
Your AI lead can define the role and help you hire for it.
What it looks like in practice
- Logistics
Reads delivery orders and invoices, checks them against purchase orders and posts clean records to the ERP.
- Professional services
Lets staff search past proposals and policies in plain language, with links to the source.
- Retail and F&B
Forecasts demand by outlet using sales history, promotions and public holidays.
Typical engagements by industry.
Local rules we work with
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AI governance
IMDA's Model AI Governance Framework and the AI Verify testing toolkit, plus PDPC guidelines on using personal data in AI systems.
Data protection
The Personal Data Protection Act (PDPA), enforced by the PDPC, including mandatory notification of significant data breaches.
Questions about AI Projects
Do we need clean data first?
No. The discovery sprint checks what data you have and picks use cases that work with it. Data clean-up joins the plan only where it pays off.
Which AI tools do you use?
We choose per use case, based on accuracy, privacy, cost and fit with your systems. We are not tied to any vendor.
Will AI replace our staff?
Projects target repetitive tasks within jobs, so people spend more time on work that needs judgement. Any change to roles is planned with your HR lead.
Is our data safe?
We use business-grade tools that do not train on your data, limit access to the people who need it, and write the rules into your AI use policy.
What if the pilot doesn't work?
You will know within weeks, against a measured baseline, without having committed to a full rollout. We document why, and what to try next.
Pairs well with
Not sure which seats you need?
The fit check takes about two minutes and suggests a starting bench, with reasons.