Applied AI Engineering

Grounded, isolated, and affordable to run

The hard part of enterprise AI is not the demo. It is answers that cite their sources, tenants that cannot see each other, agents that stay inside the boundaries you set, and a bill you can explain to a CFO. We build the platform underneath the model: retrieval, isolation, tooling, cost control, and the operations that keep it running.

A senior engineer leads every engagement, from the first architecture review through deployment and hand-off, and the result runs in your tenant, on infrastructure you own.

What we do

Enterprise RAG deployments into regulated tenants, with grounded, cited answers and private connectivity to the data they draw on.
Multi-tenant agent platforms with per-tenant isolation enforced in the data layer, not the prompt, and MCP tool servers for the systems the agents act on.
Caging autonomous agents inside an enterprise tenant: identity, network, and permission boundaries so an agent can only do what it was given.
LLM cost engineering. Caching, model routing, budgets and spend caps, partition pruning in the data layer, and per-tenant cost attribution.
Azure OpenAI provisioned throughput (PTU) sizing and procurement, so you buy the capacity you need and not more.
Real-time voice agents on Azure, including the AI Receptionist that answers our own phone line.

What you get

An architecture that runs in your tenant, documented as infrastructure-as-code.
Isolation and guardrails you can show to a security reviewer, with the evidence.
Spend you can see: attribution per tenant and feature, and caps that hold.
Run-books and knowledge transfer so your team can operate and extend it.

Proof

Making an AI platform’s economics visible: LLM spend went from roughly 0% recorded to fully attributed per tenant, and a lakehouse bill dropped about 75%.
Rescuing the intellectual property of an AI platform: a stalled commercial launch turned out to be one expired credential away from live.
From the blog: Cutting an agent platform’s LLM spend by 79%, Spend caps at every layer, and partition pruning as a requirement, Caging an autonomous AI agent inside an enterprise tenant, and Fail-safe tools for a voice AI receptionist.

How we start

An AI Cost & Architecture Review: where your LLM spend goes, what it buys, how your tenants are isolated, and what to change. Scoped and quoted before work starts; it ends in a prioritized plan you own.

Building or running AI for real users? Tell us about your platform or call (662) 626-0732.

Questions we hear

Can it run inside our own cloud tenant?

Yes, and that is how we prefer to build. The models, data, and tools live in your own cloud tenant, behind your private networking and identity. You keep the data, the keys, and the ability to run it without us.

How do you keep one customer's data away from another's?

Isolation is enforced below the prompt: the data layer refuses queries that are not scoped to a tenant, database row-level security can back that up as a second layer, and the platform fails closed when a tenant cannot be identified.

Which models do you use?

Whichever fit the job and your compliance needs. We have built on Azure OpenAI, Anthropic, and Google models, size Azure OpenAI provisioned throughput (PTU) where it fits, and use model routing so routine requests go to cheaper models. We avoid designs that lock you to one vendor.

How do you control spend?

Budgets and spend caps at every layer (per collection, per tenant, per organization), response caching, model routing, and cost attribution so every dollar is recorded against the tenant or feature that spent it. You see the bill before it surprises you.