AWS cloud and AI, engineered to run in production.

We design, build, and operate the systems themselves — and stay engaged until your own team owns them. Migration and modernization, containers and serverless, GenAI on Amazon Bedrock, governance and FinOps, to Well-Architected standards. AWS is where we go deepest today, with the same engineering across Azure and Google Cloud.

Talk to us → See what we do
Engineering depth
Migration & Modernization
GenAI on Amazon Bedrock
EKS / ECS
Lambda · Step Functions · EventBridge
Control Tower
Well-Architected
What we do

Cloud engineering, end to end

Migration, modern platforms, GenAI, governance and cost — delivered as one practice, through four stages of how we actually run the work with your team. AWS is where we go deepest today; the same engineering extends to Azure and Google Cloud.

Migration and modernization flow: legacy and on-prem workloads move through a landing zone and modernize into containers, serverless, GenAI and agentic workloads. Legacy / on-prem Landing zone Containers EKS / ECS Serverless Lambda · Step Functions GenAI Amazon Bedrock · RAG Agentic Agents · tools · guardrails
Wherever you start — a migration, a greenfield build, or embedded delivery — workloads land in a governed foundation and move to the right target: containers, serverless, GenAI or agentic.
Stage 01 · Assess & move

We start from your workloads, not a template

Every engagement opens with discovery — what you run, what it costs, what it depends on. From there we sequence the work in waves and modernize toward the right target for each workload: containers, serverless, GenAI or agentic systems. Each wave has its own rollback path and success criteria, so progress is visible and reversible rather than a single big-bang cutover.

DiscoveryWave planningContainers · Serverless · GenAI · Agentic
How we deliver ↗
GenAI pipeline on Amazon Bedrock: source documents feed retrieval into a model core with an agent and guardrail, then observability. Source docs Retrieval RAG Bedrock model Agent + guardrail Observability traces · evals
Production-hardening in practice — shown here on a Bedrock GenAI pipeline: guardrails, evaluations and observability wired in, the same discipline we apply to any workload.
Stage 02 · Build for production

Built to run, not to demo

Whether it's a platform or a GenAI system, we engineer for day-two from day one — guardrails, traces, evaluations and rollback wired in so behaviour stays observable and safe once real traffic arrives. The pipeline shown here is how a Bedrock-grounded system looks when it's production-ready.

GuardrailsObservabilityEvaluations
Explore GenAI ↗
FinOps cost curve: spend bends downward after a rightsizing and Savings Plans marker, moving from a baseline toward a target. $ Baseline Rightsizing · Savings Plans Target
Illustrative cost curve bending down after rightsizing and commitment strategy.
Stage 03 · Prove the value

Progress you can see on the bill

We tie the work to numbers that matter — baselining spend, then bending the cost curve down and keeping it there through rightsizing and commitment strategy, so the business sees value, not just activity. Figures shown are illustrative of the targets each engagement works toward.

BaselineRightsizingMeasured outcomes
Explore FinOps ↗
Managed-operations loop: observe, detect, remediate and review against Well-Architected, running continuously. Well- Architected Observe Detect Remediate Review
A continuous observe → detect → remediate → review loop around Well-Architected.
Stage 04 · Hand over ownership

Operate on a loop, then hand it over

We run live systems on a continuous loop — observe, detect, remediate, review against Well-Architected — with multi-account guardrails in place. The assigned team stays engaged until the runbooks, guardrails and knowledge sit with your team and you own it outright.

Observe → remediateGuardrailsKnowledge transfer
Selected Work

Engineering outcomes that held up in production

A sample of recent engagements — AWS-led today, with the same approach carrying to Azure and Google Cloud. Figures are illustrative of the targets each engagement worked toward; full details are available on request under NDA.

Agentic AI

Candidate screening & ranking agent

Bedrock Agents with Claude on a serverless core — faster shortlisting and more recruiter time back, with higher peak throughput.

Read the Agentic AI work → GenAI

RAG knowledge assistant

A retrieval-augmented enterprise assistant on Amazon Bedrock — faster retrieval, higher answer relevance, and tight latency targets.

Read the GenAI work → FinOps

AWS cost optimization

Cost baselining, rightsizing, and a commitment strategy that cut spend and lifted tag compliance, with continuous monitoring.

Read the FinOps work → More

All case studies

Browse the full set of engagements across Professional Services, Agentic AI, GenAI, and FinOps.

View all case studies →
Reference architecture for a retrieval-augmented knowledge assistant on Amazon Bedrock, showing ingestion, retrieval, the Bedrock model, and observability.
Featured · GenAI

A knowledge assistant, end to end

A retrieval-augmented enterprise assistant on Amazon Bedrock — ingestion, retrieval, model serving, and observability wired together. The diagram is the actual reference architecture shape we build toward; figures in the write-up are illustrative of the engagement's targets.

Read the GenAI work ↗
Our Approach

Well-Architected delivery

Build & operate

We architect, build, deploy, and operate on AWS. The assigned team stays engaged until systems run and your team has full ownership.

Outcome-oriented

Each engagement opens with defined targets — cost, migration milestones, uptime — framed as shared objectives we work toward.

Engineering rigour

Well-Architected reviews, peer review, and security and cost gates built into every build — production-grade from the start.

Get started

Tell us what you're building in the cloud

Share the workload, the constraints, and the outcome you're after. We'll come back with how we'd approach it.

Talk to us → See our work