AI Consulting & Advisory
Know which AI investments will pay, before you commit a budget.
We sit with your teams, establish where artificial intelligence is genuinely worth applying, score each use case on commercial value and technical feasibility, and quantify the return before a line of code is written. You receive an executive business case and a twelve-month roadmap, produced in two weeks and at no cost on AWS engagements.
The approach
Where enterprise AI programmes lose momentum.
The common pattern is a pilot that was never costed, built on data without a clear owner and assessed without an evaluation set capable of demonstrating improvement. It presents well and then stalls. Our consulting practice is structured to avoid that outcome. We score use cases before any code is written, we build on infrastructure your own teams are able to operate, and we document a roadmap that remains valid when the sponsor changes.
We are partnered to implement on Amazon Bedrock, Google Gemini Enterprise and Oracle Cloud Infrastructure, and the platform is selected on where your data and applications already sit rather than on preference. In each case the workload runs within your own tenancy, your data remains inside your security boundary, and models can be substituted as pricing and capability change without the surrounding application being rewritten.
How we select the platform
Free AI assessment on AWS
A two-week AWS assessment, provided at no cost, concluding in a twelve-month roadmap.
The assessment is carried out before any commercial discussion, because a considered view on whether artificial intelligence is worth pursuing in your organisation depends on figures that neither party holds at the outset. We spend two weeks with your teams establishing them.
The assessment is provided at no cost where AWS is the target platform, which reflects the support available to us as an AWS partner. Engagements directed at Google Cloud or Oracle Cloud Infrastructure follow the same method and the same deliverables, and are scoped and quoted separately.
The output remains yours whether or not you appoint us to deliver the work. Should the assessment conclude that the investment is not justified within the current year, we will say so.
Request an assessment
Deliverables
What the assessment produces.
AI readiness evaluation
A scored view of where the organisation stands across data, platform, skills and governance, so the roadmap proceeds from a documented position rather than an assumed one.
Prioritised use case register
Candidate use cases identified with your teams and prioritised against the AWS use case catalogue, scored on commercial value and technical feasibility, including those we recommend against pursuing.
Pattern mapping
Each prioritised use case mapped to one of the six established data and AI patterns, which determines the architecture, the delivery effort and the risks carried into production.
Agentic readiness analysis
An automated review of your codebase against 43 criteria across eight dimensions, covering API surface, authentication, state management, human oversight, data accessibility, discoverability, observability and engineering maturity, returned as a severity-ranked report with file-level evidence and remediation steps.
Architecture and platform fit
A target architecture validated against the appropriate AWS services, including Amazon Bedrock AgentCore and Amazon Quick Suite, alongside identity, network isolation and integration with your existing estate.
Business case and twelve-month roadmap
A quantified return on investment model and an executive-ready business case, with the work sequenced into four quarters, each carrying an accountable owner, a dependency list and a measurable outcome.
The assessment is intended for organisations with a clear intention to adopt AI and a need for structured guidance on the first steps.
No fee on AWS engagements
Get your FREE
AI assessment with us.
Two weeks with your teams, ending in a prioritised use case register, a target architecture, a quantified business case and a twelve-month roadmap you keep either way. Answer the questions below and we will confirm eligibility and propose a start date within one business day.
How we work
Four phases, from discovery to a quantified business case.
Discovery
Readiness and intent
- Structured sessions with business and technical stakeholders
- Data, platform, skills and governance readiness scored
- Candidate use cases captured across the organisation
- Commercial objectives stated in your own measures
Pattern mapping
Six data and AI patterns
- Use cases prioritised on value and feasibility
- Each one mapped to an established delivery pattern
- Agentic readiness of the underlying systems assessed
- Use cases we advise against are recorded with reasons
Architecture and roadmap
Target state and sequence
- Reference architecture for the prioritised use cases
- Platform fit validated against the appropriate AWS services
- Identity, isolation and integration requirements defined
- Work sequenced across four quarters with owners
ROI and business case
Quantified, executive-ready
- Return modelled on your own volumes and costs
- Inference and operating costs carried in the model
- Executive-ready business case for the first phase
- Formal readout and sign-off with your sponsor
Capabilities
What our delivery teams implement.
Certified across the platforms we implement
Platforms
Where we build, and what each platform is suited to.
Amazon Web Services
Amazon Bedrock
Foundation models from Anthropic, Meta, Mistral, Cohere and Amazon behind a single API, running inside your own AWS account.
- Bedrock Knowledge Bases and retrieval
- Bedrock Agents and tool calling
- Bedrock Guardrails for PII and topic control
- Model evaluation and provisioned throughput
Google Cloud
Gemini Enterprise
Google's enterprise agent platform, suited to organisations whose data and collaboration already sit within Google Cloud and Workspace.
- Gemini Enterprise agents and connectors
- Vertex AI Agent Builder and model garden
- Grounding through Vertex AI Search
- BigQuery as the analytical foundation
Oracle Cloud Infrastructure
Oracle AI
The appropriate route where the systems of record are Oracle, keeping the models alongside the applications and data they depend on.
- OCI Generative AI and AI Agents
- AI Agent Studio for Fusion Applications
- Select AI within Autonomous Database
- Oracle Integration Cloud for system access
In production
An AI agent answering customers across three markets.
Car & General now takes its first customer response from an AI assistant across Kenya, Tanzania and Uganda, with at least one enquiry in five resolved without a person and a live agent picking up the same conversation when one is needed.
Read the Car & General story
Questions
What clients ask before starting.
A scored register of AI use cases across your organisation, a data readiness review, a target architecture, a twelve-month roadmap sequenced into quarters, an indicative cost model including inference costs, and a governance and guardrails plan. It runs over approximately two weeks and carries no obligation to proceed. The assessment is provided at no cost where AWS is the target platform, reflecting the support available to us as an AWS partner. Assessments directed at Google Cloud or Oracle Cloud Infrastructure follow the same method and are scoped and quoted separately.
We are partnered to implement Amazon Bedrock on AWS, Gemini Enterprise and Vertex AI on Google Cloud, and OCI Generative AI together with AI Agent Studio on Oracle Cloud Infrastructure. The platform is selected during the assessment on the basis of where your data and applications already reside, your residency obligations and your existing licensing, rather than on preference. In each case the workload runs within your own tenancy and your data is not used to train the underlying models.
A prototype on real data typically takes four to six weeks after the assessment. Production delivery depends on the integrations involved, and most first workloads go live within three to four months of the roadmap being signed off.
Yes. The assessment establishes where your data resides today, and the roadmap accounts for the migration and integration work an AI workload depends upon rather than assuming it has already been completed.