Four layers. One engineered system.
Production AI is not a model problem. It is four layers that have to work together, and a failure in any one of them shows up as a bad answer, a blown budget, or a system nobody trusts. We engineer all four, and we go deepest where performance is actually decided.
Models are the surface. Making them behave is the work.
Agents, copilots and retrieval systems are easy to demo and hard to depend on. The distance between the two is evaluation, grounding, guardrails, and the operational discipline to keep a system correct as the data and the models shift underneath it.
Retrieval and grounding
Vector and hybrid retrieval, chunking and embedding strategy, re-ranking, and the evaluation harness that shows answers are grounded in your data rather than merely plausible.
Agents and orchestration
Tool use, multi-step workflows, failure handling and end-to-end tracing, so when an agent does something unexpected you can see exactly which step produced it.
Evaluation and guardrails
Offline and online evals, regression suites that run on every change, policy and safety enforcement, and explicit thresholds that decide whether a release ships or stops.
MLOps and model lifecycle
Training, fine-tuning and serving pipelines, a model registry with real versioning, drift detection, and a rollback path that works while the system is under load.
AI raises the bar on data that was already behind
Enterprise data sits across clouds, applications, warehouses and documents, with unclear ownership and incomplete metadata. AI systems now retrieve, combine and act on that information while they run, so gaps that were tolerable in a quarterly dashboard become wrong answers in production.
Platforms and modernization
Lakehouse architecture on Databricks, Snowflake or native cloud services, and migrations that move the workloads and their economics rather than just copying tables across.
Pipelines and engineering
Batch and streaming ingestion, transformation and orchestration, with the tests and freshness checks that make a pipeline something you can actually build on.
Governance, lineage and quality
Catalogues and access control, PII handling and masking, end-to-end lineage from source to answer, and quality checks that fail loudly instead of degrading quietly.
AI-ready data products
Curated, documented, owned datasets shaped for retrieval and model consumption, not reshaped reporting tables with a vector index bolted on the side.
Accelerators alone do not make a system fast
Performance leaks across network latency, collective communication, GPU topology, workload placement and storage throughput. Adding accelerators does not close those gaps; a poorly designed distributed architecture gets less efficient as it scales, so the bill grows faster than the throughput.
GPU and HPC architecture
Cluster design and topology, scheduling on Kubernetes or Slurm, multi-tenancy and queueing, and capacity planning against the workloads you actually run.
Interconnect and the data path
NCCL, InfiniBand, EFA and RDMA tuning, plus the storage and network work that stops expensive accelerators sitting idle waiting for data to arrive.
Distributed training and inference
Parallelism strategy, collective-communication tuning, and serving architectures that hold their latency when concurrency rises rather than only when the demo is quiet.
Benchmarking and cost
Measured throughput and utilization on your own workloads, before and after, with the FinOps attribution that shows which change produced which saving.
The foundation everything above it inherits
Account structure, networking, identity and security posture decide what is possible in every layer above them. These are also the decisions that are most expensive to revisit once real workloads and real data are running on top of them.
Architecture and landing zones
Account and network design, multi-cloud and hybrid patterns, and environments defined as code so they can be rebuilt rather than archaeologically reconstructed.
Kubernetes and platform engineering
Cluster design, workload isolation, autoscaling and release tooling, shaped into an internal platform your engineers use willingly rather than route around.
Security and compliance posture
Identity, secrets, network controls, encryption and audit trails, designed so the evidence your risk and compliance functions need is produced as a by-product.
Observability and FinOps
Telemetry across every layer, cost attribution down to team and workload, and budget guardrails that catch a runaway before it becomes a quarterly surprise.
Strongest where it is hardest. Capable all the way up.
Most partners work the top layer and assume the rest. We go deepest in data and compute because that is where performance and cost are decided, and we carry the work through the layers above so nobody is left holding the integration.