AI Engineering
AI workloads that behave like production systems.
The hard part of AI in production is rarely the model. It is capacity, cost, latency, observability and data boundaries — the same platform problems as always, with less forgiving economics.
The problem
AI projects stall on infrastructure rather than models. GPU capacity is unpredictable, inference costs are invisible until the invoice lands, model endpoints have no observability, and nobody agreed what data may leave the building.
What we deliver
- AI platform engineering: model serving, GPU capacity planning, inference autoscaling
- Vector database and retrieval infrastructure sized for real query load
- LLM cost observability and budget control per team and per feature
- Secure AI landing zones with data-boundary and access controls
- AI application delivery — retrieval pipelines and agent workflows, staffed through our specialist network
- AI readiness assessment and adoption advisory for engineering leadership
- running the platform layer AI now depends on
- 10+ yrs
- devices operated on distributed data infrastructure
- 100k+
Numbers shown are from the founder’s prior roles, not SevenM engagements.
Technologies
- AWS Bedrock
- Kubernetes
- Terraform
- Prometheus
- Grafana
- PostgreSQL
Questions
- Is this just cloud consulting with AI in the title?
- The infrastructure half is deliberately continuous with our platform work — GPU scheduling, serving, cost control and observability are platform engineering problems, and pretending otherwise is how AI projects fail. Application-layer delivery is staffed through our specialist network.
- Can you help us choose between hosted models and self-hosting?
- Yes, and the answer usually turns on data boundaries and sustained throughput rather than headline token pricing. We model both against your actual usage before recommending either.
- We have no AI workloads yet. Is this premature?
- Not necessarily. The expensive mistakes are made early — data boundaries, identity, and where inference is allowed to run. Getting those right first is much cheaper than retrofitting them later.