Solutions
One team across all of it, because an AI system rarely fails inside a single discipline. A retrieval problem becomes a permissions problem, and a permissions problem becomes an identity problem.
Retrieval, agent architecture, evaluation, and inference on your own infrastructure.
The systems people mean when they say AI: retrieval over a corpus that has real access rules, agents that carry a task to completion, and the evaluation harness that tells you whether either still works this week. Most of the engineering sits around the model rather than in it, which is why the choice of model is usually the last decision rather than the first.
Work carried across CRM, ERP, ticketing, and the internal systems your teams already use.
Work that used to move through a queue moves through a system instead. The difficulty is rarely the reasoning. It is deciding what an agent is permitted to do, making every action reversible or reviewable, and satisfying the people who own Salesforce or SAP that it will behave on a Tuesday afternoon when nobody is watching.
Pipelines, lineage, and access control on Databricks, Snowflake, and the warehouse you already run.
AI makes the state of a data estate impossible to ignore. Retrieval quality is a data problem before it is a model problem, and permissions that live in the warehouse have to survive the trip into an index. We build the pipelines, the lineage, and the access model that make a system defensible rather than merely impressive.
Foundations across AWS and Azure, designed around the constraints already in place.
Residency, tenancy, latency, and network egress decide more architectures than any technical preference does. We work inside the landing zones, identity providers, and change controls a client already has, because the alternative is a system that demonstrates well and cannot be promoted.
Device connectivity, edge runtime, and fleet operations on AWS IoT Greengrass and Cumulocity.
Inference close enough to the process to act on it, on hardware that is often older and more constrained than anyone would choose. Vision on a production line, telemetry from a fleet, local models that keep deciding when the link to the cloud drops. Fleet operations and over-the-air updates matter more here than model size does.
Identity, authorization, observability, evaluations, and the record an auditor will ask for.
Anything a model reads may have been written by someone hostile, so authority belongs at the tool boundary and never in the prompt. Underneath that sits the duller and more decisive work: who the system acts as, what it may reach, how quality is measured continuously, and what can be produced afterward when somebody asks what happened.
Tell us the outcome you are trying to create, what you have already attempted, and where the constraints are.
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