AIengineering,built
forproduction.
We design, build and run GenAI, conversational AI and AI coding assistants on cloud, DevOps and SRE foundations.
- Retrieval
- Index freshness
- 98%
- Recall@5
- 0.92
p95 latency · 120 msHealthy - LLM
- Context
- 32k
- Output
- 450 tok/s
p95 latency · 820 msHealthy - Guardrails
- PII check
- Pass
- Policy check
- Pass
- Evaluation
- Groundedness
- 0.94
- Human review
- 4.7 / 5
- Deploy
- Version
- v2.4.1
- Rollout
- 100%
Seven services. One team that ships.
AI leads. Cloud, DevOps, SRE and platform engineering are the foundation that keeps it running in production.
GenAI solutions
Production LLM applications that are grounded, evaluated and safe.
Evaluation run · release 2.4Example Accuracy0.93Groundedness0.95Safety0.99Tone0.88All checks passed · ready to ship
Conversational AI
AI agents for every customer conversation, built on Amazon Connect Customer.
Contact centre · chatExample Can I move my booking to Friday, same time?
Sure. I can see Wednesday at 3:00 PM. Move it to Friday at 3:00 PM?
Yes please.
Done. A confirmation is on its way.
Resolved by the bot · no hand-off needed
AI coding assistants
Roll out Claude Code, Cursor, Codex and Copilot with guardrails, and measure what they change.
Coding assistant pilotExample Weekly active developers
Payments team82%Mobile team64%Data team71%Pull request cycle time
Cycle time down since the pilot began
Cloud
Architecture, migration and cost: the ground AI runs on.
Monthly cloud costExample DevOps and MLOps
CI/CD, infrastructure as code, and the pipelines that ship models and agents.
Pipeline · support-assistantExample - Checkoutpassed
- Tests and scanspassed
- Build imagepassed
- Evaluate modeleval gate passed
- Canary deploy25% traffic
- Promotewaiting
SRE
Reliability you can measure: SLOs, observability and incident response.
checkout-api · SLO 99.9%Example 99.94% availability, 30 days72% error budget leftPlatform engineering
Internal developer platforms, golden paths and self-service infrastructure.
New serviceExample Templatepython-api- CI/CD pipeline
- Dashboards and alerts
- Runbook and on-call
- Cost tags
AI is the part you see. Production is everything underneath.
A model in a notebook is a demo. In production, every AI workload stands on seven layers working together. We build and run all of them, so the AI you launch keeps working.
How we workAI applications and agents
What your users see: GenAI apps, conversational AI agents and AI coding assistants.
RAG apps · voice and chat agents · coding agentsModels, evaluation and guardrails
The right model for each task, tested before every release and kept inside policy.
Model routing · evaluations · Bedrock GuardrailsData, knowledge and integrations
Clean data, knowledge bases and vector search, plus the APIs, MCP and A2A links that let agents act.
Data pipelines · vector search · MCP · A2APlatform automation and delivery
CI/CD, infrastructure as code, MLOps and self-service platforms that ship code, models and agents safely.
Terraform · CI/CD · SageMaker AI · AgentCoreObservability and reliability
Monitoring, tracing and SLOs across services, models and agent steps, and incident response that works.
OpenTelemetry · Splunk · Dynatrace · CloudWatchSecurity, governance and cost
Identity and policy for people and agents, compliance, and a cloud bill you can explain.
IAM · AgentCore Policy · audit · FinOpsCloud infrastructure
Compute and GPUs, networking and storage, secure by default.
AWS · Azure · Google Cloud · Kubernetes
How an engagement runs
Four stages, the same discipline every time, whether we deliver a project or join your team.
Assess
We review your architecture, data, goals and constraints, and agree what success looks like.
Design
Target architecture, a delivery plan and the measures we will be judged on.
Build
Short iterations, working software early, built alongside your team in your accounts.
Run
We operate it to agreed service levels, improve it, or hand it over with runbooks.
Ways to work with us
- Delivery projectsWe own an outcome end to end: scope, build, launch and handover.
- Embedded engineersOur engineers join your team and work in your rituals, tools and codebase.
- Run and improveWe operate what is live, keep it reliable and keep improving it.
The stack we work in
We work in your cloud accounts and your repositories, with the tools your teams already know.
AI and data
- Amazon Bedrock
- Bedrock AgentCore
- Amazon SageMaker AI
- OpenAI and Anthropic APIs
- Vector search
- Python
Conversational AI
- Amazon Connect Customer
- Agentic CX Designer
- Amazon Lex
- AWS Lambda
- Contact Lens
- MCP and A2A
AI coding assistants
- Claude Code
- Cursor
- OpenAI Codex
- GitHub Copilot
Cloud and platform
- AWS
- Azure
- Google Cloud
- Kubernetes
- Terraform
- Backstage
Delivery and MLOps
- GitHub Actions
- GitLab CI
- AWS CodePipeline
- Argo CD
- MLflow
- AWS DevOps Agent
Reliability
- OpenTelemetry
- ELK Stack
- Splunk
- Dynatrace
- Datadog
- Prometheus
- Grafana
Training: The Human Job
We also train the next generation of engineers. The Human Job is our 14-week AI-Native Engineering Bootcamp for final-year engineering students, built on the stack we deliver: AI coding tools, AWS, DevOps, Kubernetes, Amazon Connect Customer and Agentic CX Designer.
Students build every implementation themselves, record evidence on GitHub, review each other’s work and defend it, guided by U.S.‑based Lead Architects. They graduate with proof of work.
Tell us where your AI is stuck.
One conversation with an engineer. No sales script.