AWS cloud · Production AI · Agentic systems
We design and deliver secure cloud platforms, production AI applications and intelligent agents that automate work, modernise operations and create new digital capabilities.
Users and applications
Staff, customers and existing systems raising requests and events
AI agents
Orchestration, task planning and approval gates
Models, tools and enterprise data
Foundation models, scoped tools and retrieval over your own content
Secure AWS cloud platform
Compute, networking, identity and encryption
Selected clients
The gap we close
Most organisations we speak to are not short of ideas. They are short of a route from a promising experiment to something they can rely on, secure and afford to run.
What we usually find
What it becomes
AI proofs of concept that never reach production
A repeatable path from experiment to a supported production service
Manual workflows consuming significant staff time
Multi-step processes automated end to end, with approvals where they matter
Fragmented cloud infrastructure that nobody fully understands
A documented architecture that a new engineer can navigate
AWS costs rising faster than the business
Spend attributed to a team or product, and infrastructure waste removed
Slow, risky and infrequent software releases
Smaller, more frequent deployments with automated rollback
Legacy applications limiting what the product team can attempt
Incremental modernisation that delivers value before it finishes
Security and governance concerns blocking adoption
Preventative controls in the pipeline and evidence ready for audit
Organisational knowledge that is difficult to find and use
Secure retrieval over your own content, with citations and access control
Automation tools that do not talk to one another
A single definition of the process, observable end to end
No clear basis for judging which AI use cases are worth pursuing
Use cases ranked by value, data readiness and technical risk
What we do
Agents need grounded AI. Grounded AI needs a secure, observable platform. We build all three, which is why they fit together rather than merely integrate.
Design intelligent agents that reason through tasks, retrieve organisational knowledge, use business tools and coordinate multi-step workflows under clearly defined controls.
Turn organisational data and domain expertise into secure AI applications that solve a specific business problem.
Build the secure, automated and observable AWS foundation required to run modern applications and AI workloads confidently.
Solutions
Concrete use cases, each with a defined business problem and a measurable outcome. These describe what we build, not claims about existing customers.
Retrieval and search
A secure assistant that answers questions from your own documents, policies and internal data, with citations and access control that follows the user.
Customer operations
An agent that answers common questions, retrieves account context, raises tickets and hands complex cases to a person with the history attached.
Platform operations
An agent that triages alerts, gathers diagnostic context, proposes remediation and assists the responder — without acting on production unattended.
Document processing
Extract, classify, validate and summarise information from contracts, reports, forms and operational documents.
Engineering enablement
An assistant grounded in your own systems that helps engineers understand services, troubleshoot behaviour and produce documentation.
Process automation
Coordinate multi-step processes across APIs, databases and business tools, with human approval at the points that warrant it.
Product engineering
Design and build AI-native features inside an existing product, or a new AI-enabled platform, with the delivery discipline of ordinary software.
Agentic architecture
We build agents that can understand objectives, retrieve trusted knowledge, call approved tools, execute workflows and escalate decisions to people when required.
Typically integrated with
User or business event
A question, a ticket, an alert or a scheduled trigger
Agent orchestration
Objective interpreted, budget and time limits applied
Reasoning and task planning
Work decomposed into steps, with deterministic fallbacks
Knowledge retrieval and memory
Grounding in your documents and data, filtered by permission
Approved tools and business systems
Scoped credentials per tool, rate limited and logged
Human approval for sensitive actions
Irreversible or high-value steps pause for a person
Logging, evaluation and governance
Full trace, quality scores and spend recorded per run
Controls applied at every stage
The AWS layer
The services we reach for most, grouped by the job they do rather than by console category.
Where models run, where knowledge is indexed, and where the data they depend on is prepared.
Bedrock provides managed access to foundation models without operating inference infrastructure. Retrieval sits on OpenSearch over content staged in S3 and prepared with Glue, so answers are grounded in your material rather than the model's training data. Lambda ties the pieces together, and SageMaker is reserved for the cases where a custom or fine-tuned model genuinely earns its cost.
Where your applications and agent workloads actually run.
The choice between containers and serverless follows the workload rather than fashion. Long-running or stateful services sit on EKS or ECS with Fargate; event-driven and spiky workloads — which most agent and retrieval traffic is — run on Lambda behind API Gateway. CloudFront and Elastic Load Balancing handle entry, caching and distribution.
The controls that decide what every workload — including an agent — is allowed to do.
Access starts at IAM Identity Center and narrows to short-lived, workload-scoped roles; an agent gets its own credentials rather than borrowing the application's. KMS and Secrets Manager keep keys and credentials out of code. GuardDuty, Security Hub and Config provide detection and drift visibility across accounts organised under Organizations and Control Tower.
How changes reach production, and how you know what happened once they did.
Infrastructure is defined in Terraform or CDK and promoted through environments by pipeline, never by hand. CloudWatch, X-Ray and OpenTelemetry cover metrics, traces and logs for conventional services and for agent runs alike — a model call and a tool call appear in the same trace as the request that triggered them.
Engineering practice
The interesting part of an AI system is rarely the model. It is everything around it that determines whether the thing can be trusted, changed and afforded a year after launch.
Identity, permissions and data boundaries are decided during design, not retrofitted after a review.
Every environment is defined in source and rebuilt from it. Manual console changes are treated as drift.
Build, test, scan and deploy run without manual steps, with rollback as a first-class path.
Quality is measured against a golden dataset and gates releases the way any other test does.
Metrics, structured logs and distributed traces cover model calls and tool calls alongside ordinary requests.
Short-lived, workload-scoped credentials. An agent holds its own permissions, not the application's.
Irreversible or high-value actions pause for approval, and the approval is recorded against the step.
Failure modes are designed for: timeouts, retries with backoff, deterministic fallbacks and graceful degradation.
Spend is instrumented per feature and per workflow from the first deployment, not reconstructed later.
Architecture decision records, runbooks and working sessions are deliverables, not optional extras.
Data handling, refusal behaviour and failure modes are reviewed before launch and monitored after it.
Managed services where they reduce work; interfaces at the boundaries so a provider choice stays reversible.
How we work
Every engagement runs the same shape, scaled to its size. The measures of success are agreed before design starts, not reconstructed at the end.
Understand the business problem, the systems around it, the data available and what success would look like.
Define the architecture, security model and implementation roadmap, and record the decisions behind them.
Develop the platform, application, agent workflow and integrations in short iterations with working software at each step.
Test functionality, security, reliability, model behaviour and agent actions against the measures agreed at the start.
Deploy with monitoring, documentation and knowledge transfer, using a controlled rollout rather than a switch.
Use operational data, evaluation results and user feedback to improve performance, cost and business value.
Real outcomes from migrations, modernization, DevOps, and AI engagements.
Cost visibility, workload rightsizing and automated guardrails reduced monthly cloud costs by 38% for a South African online education provider, with no reduction in performance.
Migrating a monolithic content platform to a service-based architecture on AWS, delivered without downtime and with a measurable improvement in page performance.
Building an internal developer platform for a professional services firm, reducing developer onboarding time substantially and making environment provisioning self-service.
Real stories from teams we've helped succeed in the cloud.
"Working with them was a game-changer. Our migration to AWS was flawless, and we saw a 40% reduction in costs within the first quarter. Their DevOps expertise has revolutionised our deployment process."
Nsuku Mathebula
CTO, ASW Tutors
"The platform engineering team built us an internal developer platform that has cut developer onboarding time by 80%. We're shipping features faster than ever before."
Nsuku Mathebula
Head of Engineering, Tech With Manny
"Their security audit and remediation plan were incredibly thorough. We're now confident in our compliance posture and have a clear roadmap for continuous improvement."
Bornagain Mathebula
Managing Partner, BMathebula Law Firm
"We finally have observability that developers actually use. MTTR is down 55% and on-call is far calmer."
Priya Desai
Director of Engineering, Study Verse
"The team's expertise in cloud architecture transformed our business. We're now able to scale effortlessly and focus on what matters most — our customers."
Zika Mabunda
Managing Partner, Philness Accounting
Next step
Tell us what you are trying to achieve. Thirty minutes with an engineer is usually enough to say what it would take — and whether there is a simpler way to get the same outcome.
