We arrive with an architecture.
This is the reference stack we build for clients, and the one this company runs on. Every layer is delivered declaratively, documented, and handed over with the runbooks for operating it.
Four layers, in a fixed order
The order is the point: nothing above works reliably until the layer beneath it is decided. Most estates we inherit have layer three missing and layer one improvised.
Applied intelligence
The models that do the work, with routing, evaluation and guardrails around them.
- Private LLM serving
- Retrieval
- Voice & video
- Agents
- Evaluation harness
Data foundation
The lakehouse and the streaming spine, with semantics that are proven rather than assumed.
- Lakehouse
- Streaming backbone
- Contracts
- Metric layer
- Equivalence lane
Platform
The Kubernetes estate and everything a team needs before it can ship anything at all.
- Clusters
- GitOps delivery
- Observability
- Policy & secrets
- GPU scheduling
Fabric
On-premises hardware, cloud regions and serverless inference, addressed as one estate.
- On-prem bare metal
- GKE · EKS
- Serverless inference
- Zero-trust network
What we will argue for
These are not preferences. Each one exists because we have seen the alternative fail, and each one changes what we build.
One fabric, many providers
On-premises hardware, cloud regions and serverless inference are addressed through one abstraction. Adding a provider is a configuration record, not a rewrite — which is why your placement decisions stay reversible.
Declarative or it did not happen
Every layer is reconciled from a repository. A change is a reviewed commit, a rollback is a revert, and a manual cluster change is surfaced as drift rather than silently kept.
Proven equal, not assumed equal
Any migration we run — batch to streaming, warehouse to lakehouse, legacy to new — ships with an automated comparison lane that proves the two paths produce the same data before anything is switched.
Inference belongs where the trade-off lands
Latency, privacy and cost decide where a model runs, and that answer changes. We build the routing layer first, so moving a workload from owned hardware to burst capacity is a configuration change.
Identity, not location
Being on the tunnel does not mean being inside. Access is scoped per peer and per service, and the network policy inside the cluster bounds what a compromise can reach.
A probabilistic system needs a test suite
Models, prompts and retrieval are changed constantly, so every AI system we ship carries an evaluation harness built from your own traffic, and a regression gate that stops a quiet degradation from shipping.
The console we run our own estate from
Everything above is visible in one place: the targets, their health, the workloads, the synthetic workforce and what it costs. Clients and team members reach it across the tunnel.