Opportunity discovery
Structured interviews and process walkthroughs with the teams who do the work, not just the sponsors.
Where to start
AI Strategy & Consulting
Find the handful of workflows where AI pays for itself, and the ones where it will not.
What this is
Most AI programmes stall because they begin with a technology and look for a use case. We start from your operations: where volume is high, rules are stable and judgement is repeatable. The output is a shortlist you can fund, each item with an honest feasibility read, a data dependency list and a cost envelope.
The problem
What this service addresses
If more than one of these is familiar, there is usually a case worth scoping.
- Leadership is being asked for an AI plan with no reliable way to judge which proposals are real.
- Pilots demo well, then quietly never reach production or get adopted.
- Nobody can say what an AI workflow will cost per transaction once it is live.
- Data is spread across systems and no one knows whether it is usable for AI.
Scope
What we deliver
Every item below is a deliverable you receive, not an activity we perform.
- A ranked opportunity shortlist, scored on business value, feasibility and data readiness
- A written feasibility read on each candidate, including the ones we advise against
- Data readiness assessment covering sources, permissions, quality and gaps
- Target architecture sketch and build-versus-buy recommendation per use case
- Running-cost model per workflow: model, volume and human review assumptions
- A sequenced roadmap with a defined first build and its success criteria
Capabilities
What the system does
Feasibility screening
Each candidate tested against data availability, tolerance for error and integration difficulty before it reaches the roadmap.
Cost and latency modelling
Per-task cost estimates from model choice, token volume and expected human review load.
Evaluation design
We define how you will know the system works, and the quality bar it must clear, before a line of code is written.
Build sequencing
A first project chosen for a clear outcome and a short path to production, not for its demo value.
Team and operating model
Who owns the system after launch, what they monitor and when they escalate.
Applied
Where it is used
Each of these is a bounded job with a measurable result, which is how we prefer to scope a first build.
01
Operations leadership
Map the processes consuming the most coordination time and identify which can be agent-owned.
02
Customer service
Quantify ticket mix to estimate realistic deflection before committing to a build.
03
Finance and back office
Assess document-heavy workflows for automation feasibility and control requirements.
04
Product teams
Decide which AI features genuinely differentiate the product and which are table stakes.
Architecture
How it is put together
An indicative architecture. The final design follows your systems, data boundary and constraints.
-
01
Discovery inputs
Process documentation, system inventory, ticket and transaction samples, stakeholder interviews.
-
02
Assessment layer
Feasibility, data readiness and value scoring applied consistently across candidates.
-
03
Reference architecture
A target design showing retrieval, orchestration and integration boundaries for the chosen use cases.
-
04
Cost model
Volume-driven projections for model spend, infrastructure and human-in-the-loop review.
-
05
Roadmap output
Sequenced phases with dependencies, owners and defined success measures.
Security & governance
Controls we build in
Access, privacy and oversight are part of the build rather than a later hardening phase.
- Data handling and residency options assessed before any use case is approved.
- Regulatory and sector constraints documented per candidate workflow.
- Human oversight requirements defined by decision risk, not applied uniformly.
- Clear criteria for stopping a workflow that fails its quality bar in production.
Delivery
Our approach
How we run the engagement
Discovery
Process walkthroughs, data review, constraints and a written scope you sign off before build.
Architecture
Data model, integration boundaries, hosting and the security model, agreed up front.
Proof of concept
A narrow build on your real data against an agreed quality bar, so the go-ahead is evidence-based.
Development
Two-week increments, each ending in a working deployment and a review.
Testing
Automated coverage on critical paths, evaluation sets for AI behaviour, acceptance testing with your users.
Deployment
Staged rollout into your infrastructure with monitoring and a tested rollback path.
Monitoring & support
Quality, cost and latency tracking after launch, with handover documentation or an ongoing retainer.
Proven work
Products we have built and sold
We are product builders as well as a services team. These are our own commercial platforms, not client references.
Digital sales & support agent
SIZH Virtual Agent
A virtual digital agent for sales, support and reception — trained on your business, live on WhatsApp, web, voice and email.
View product Mobility platformRideOn
A ride-hailing and fleet management platform, now with agentic dispatch, driver support and demand intelligence.
View product Multivendor marketplaceUniBooker
A configurable multivendor service-booking marketplace with agentic onboarding, matching and support.
View product
Questions
AI Strategy & Consulting — FAQs
The questions we are asked most often about this service.
Do we need a strategy engagement before building?
Not always. If you already have a specific, well-understood workflow in mind, going straight to a proof of concept is usually faster and cheaper. Strategy work pays off when there are many competing ideas and no agreed way to choose between them.
Will you recommend against AI?
Regularly. A rules engine, a database query or fixing a broken integration is often cheaper and more reliable. We say so in writing, with reasoning.
What do we actually receive?
A written assessment, a ranked shortlist, a data readiness report, a target architecture sketch, a cost model and a sequenced roadmap. All documents are yours.
How involved does our team need to be?
We need a few hours from the people who do the work and access to representative data samples. Typically five to eight interviews across two weeks.
Related
All AI First services
Services that pair with this
Artificial Intelligence Development
Custom AI applications built around your data, your systems and a measurable quality bar.
Read moreRetrieval-Augmented Generation (RAG)
Answers drawn strictly from your approved documents, with citations and permission-aware retrieval.
Read moreAI Workflow & Business Process Automation
End-to-end processes orchestrated across your tools, with code where it must be deterministic.
Read more