Capability 03 / Enterprise AI
The demo always works. The problem is the eighteen months between the demo and something an enterprise can actually put in front of a customer — the governance, the evaluation, the access control, the serving that does not fall over. That gap is the whole job.
The uncomfortable part
Most stalled AI initiatives did not fail at the model. They failed because the data underneath was ungoverned, unlineaged and untrusted — so nobody could sign off on letting an agent touch it. There is no shortcut around that.
We land lineage, permissions and quality in Unity Catalog first, then build retrieval and agents on top of something defensible. If your data is not ready, we will tell you, and we will fix that before we sell you anything with the word “agent” in it.
What we build
Vector Search over your real documents and tables, with permissions enforced at retrieval time so a user only ever gets grounded on what they are allowed to see.
Single and multi-agent systems with tools wired to governed tables and functions, scoped so an agent can do its job and nothing more. Every action is logged and attributable.
MLflow evaluation with judges and datasets built from your own domain, run as a regression gate in CI. Quality becomes a number that either clears the bar or blocks the release.
Model Serving and Databricks Apps with the access control, rate limits and monitoring that a real user base demands. The part everyone skips in the demo, done properly.
Why evaluation first
“It seems better” is not a release criterion an enterprise can stand behind. Before we build the agent, we build the thing that scores it — so every change afterward is a number going up or down, not a vibe.
# A drop in grounding blocks the release. No exceptions.
import mlflow
from quasar.eval import Grounded, Correct, gate
results = mlflow.genai.evaluate(
data = "prod.ai.eval_golden",
predict = support_agent,
scorers = [Grounded(), Correct()],
)
gate(
results,
grounded = 0.95, # hard floor
correct = 0.90,
)
# grounded 0.972 - correct 0.911 - PASS - promoted @challengerStraight answers
We can, but we will do the governance first, because otherwise you are building on sand. Often that means the honest first engagement is data engineering, not AI. We would rather tell you that now than eighteen months and a failed pilot from now.
We build on your Databricks platform and use the foundation models available there, plus the right external model where it genuinely wins. The point is that governance, evaluation and serving live in one place you control — not scattered across accounts nobody audits.
Tools are scoped to least privilege, permissions are enforced at the data layer through Unity Catalog, and every action is logged and attributable. An agent inherits the access model you already trust — it does not get a private key to the whole warehouse.
Start here
Bring your current architecture, data volumes and the workloads that hurt. You leave with a target-state diagram on Databricks, the three changes that would cut the most cost or latency, and an honest answer on whether you need us at all.
No slide deck. No procurement process. One senior architect on the call.