AWS AI Competency validated
AI agents that reach production, not just the pilot.
Most enterprises have experimented with generative AI. Very few have an agent handling real customer or back-office work with the controls a regulator or auditor would accept. The gap is rarely the model. It is the data foundation, the evaluation discipline, and the engineering to run an agent safely inside an enterprise.
Your alternatives
What most organisations try first, and the trade-off.
A chatbot vendor
Fast to launch, but locked to one channel, trained on nothing of yours, and billed per seat in dollars.
An internal innovation team
Knows the business, but usually lacks MLOps, security review experience and time. Pilots stall at the demo.
A global AI consultancy
Strong playbooks, delivered from another continent at rates that make the business case fail before it starts.
Our commitments
What you can expect from us.
- Agents that read and write to your actual systems of record, with permissions you control
- Models running in the AWS Region your residency rules require, from Cape Town to the UAE to Frankfurt
- Multilingual conversations, including Arabic, Swahili, French and code-switching, handled accurately
- Evaluation suites that tell you the agent got better, not just different
- A team in or near your time zone who can sit with your risk and compliance people
- A clear answer on whether an AWS program could offset part of the proof of concept
What we deliver
AI and Agentic AI offerings.
Agentic AI for customer experience
Conversational agents on Amazon Bedrock and Amazon Connect that resolve, not deflect: onboarding, KYC follow-ups, claims status, order tracking, collections. Voice and text, in the languages your customers use.
Agents for internal operations
Agents that work alongside finance, operations and support teams: reconciliation, document intake, credit memo preparation, procurement queries, IT service desk. Built with Amazon Bedrock AgentCore, tool use and human-in-the-loop approvals.
Generative AI on your data
Retrieval-augmented generation with Amazon Bedrock Knowledge Bases, Amazon OpenSearch Service and Amazon S3 Vectors so answers cite your policies, contracts and product data.
AI readiness assessment
A two to three week review of data, security, skills and use cases that ends in a prioritised roadmap and a scoped proof-of-concept proposal.
Classical ML and forecasting
Fraud scoring, churn, demand and credit models on Amazon SageMaker, with MLOps pipelines that keep them accurate after launch.
LLMOps, evals and guardrails
Amazon Bedrock Guardrails, automated evaluation sets, red-teaming, cost controls and observability so the agent stays within policy and budget.
Outcomes
Numbers from engagements our teams have delivered.
Outcomes from engagements our engineering teams delivered for customers in North America and Asia Pacific.
How it runs
The process, with durations.
- 1
Discovery call 30 minutes
We listen for the business outcome, the systems involved and the constraints.
- 2
Scoping workshop Half a day
Use-case selection against value and feasibility, data access review, residency and risk mapping.
- 3
Proof of concept 3 to 6 weeks
A working agent on your data with an evaluation suite and a cost model. We check whether an AWS program applies before you commit.
- 4
Production build 8 to 14 weeks
Integration, guardrails, observability, security review, change management and go-live.
- 5
Run and improve Ongoing
Continuous evals, model upgrades, cost optimisation and hand-over to your team at the pace you choose.
AWS programs
AWS runs programs that can support AI assessments and proofs of concept delivered by qualified partners. Book a consultation and our experts will tell you whether your project could qualify.
AWS services we build with
The AWS toolkit behind ai and agentic ai.
- Amazon Bedrock
- Amazon Bedrock AgentCore
- Amazon Bedrock Knowledge Bases
- Amazon Bedrock Guardrails
- Amazon SageMaker AI
- Amazon Connect
- Amazon Q
- Amazon OpenSearch Service
- AWS Lambda
- Amazon S3
Industries
Where we apply it.
Financial Services
Banking, insurance and fintech on AWS, built for your regulator and your customers.
Telecommunications
Telecom platforms that scale like the networks they run on.
Healthcare
Health systems that connect patients, providers and payers securely.
Retail and E-commerce
Commerce that survives the flash sale and learns from it.
Public Sector
Cloud-first government, delivered within the policy.
Questions
Common questions
Can the models run in our region?
Usually, yes. Amazon Bedrock is available in the AWS Africa (Cape Town), Middle East (UAE) and European Regions, among others. Model coverage varies by Region, so we confirm the specific model you need is available in-region before we commit to a residency design.
How do you handle Arabic, Swahili and other local languages?
We evaluate candidate foundation models on your real conversations, including dialects and code-switching, before choosing one. Where quality is not good enough we add retrieval, prompt scaffolding or fine-tuning, and we measure it with a language-specific evaluation set rather than assuming.
What stops the agent from doing something it should not?
Scoped IAM permissions per tool, Amazon Bedrock Guardrails, human approval steps for irreversible actions, full audit trails in Amazon CloudWatch and an evaluation suite that runs before every release.
What does a proof of concept cost?
It depends on data access and integrations, but most run between three and six weeks. We scope the POC so it becomes the first slice of production rather than a throwaway, and we will tell you up front whether an AWS program could offset part of the cost.
Next step
Scope ai and agentic ai with us.
Tell us the outcome you are after. We reply within one business day with a suggested first step, and whether an AWS program could help offset the cost.