01Reliability
Agent diagnostics
Find why an agent fails—not just where. We isolate quality, latency, cost, retrieval, tool-use, and orchestration defects, then engineer measurable improvements.
Applied frontier AI / New York
Move AI from
promise to proof.
Prestilla Labs turns stalled AI initiatives into reliable systems—through scientific diagnosis, robust engineering, and operating models built to scale.
DIAGNOSE→DESIGN→ENGINEER→DEPLOY
Frontier capability. Production discipline.
THE PROBLEM / 01
The model is rarely
the whole problem.
AI programs stall at the seams: between a compelling demo and a dependable product, between model quality and business value, between governance policy and executable controls.
ΔPerformance gap
Lab behavior fails to survive production conditions.
∞Pilot loop
More experiments, no decision architecture.
⊘Control gap
Risk requirements remain disconnected from the system.
∴Ownership gap
No durable team owns outcomes end to end.
THE PRACTICE / 02
Six ways we move critical AI work forward.
02Delivery
PoC recovery
Turn perpetual pilots into production programs. We identify the missing product, data, platform, or ownership decisions and build a credible path to deployment.
03Models
Domain post-training
Adapt frontier models to specialized work with the right mix of supervised fine-tuning, preference optimization, synthetic data, and domain-grounded evaluation.
04Organization
AI operating model
Create the teams, interfaces, standards, and ownership model needed to ship repeatedly—not through heroics, but as an organizational capability.
05Assurance
Regulated AI deployment
Build systems that can survive scrutiny. We connect engineering controls to real regulatory, risk, privacy, security, and audit requirements from day one.
06Applications
Work-accelerating AI apps
Build focused AI applications that compress expert workflows, remove repetitive effort, and make high-quality work faster without forcing teams to abandon the systems they already use.
THE METHOD / 03
Evidence before
architecture.
We begin with the failure modes that matter, construct an evaluation system, and use the evidence to determine what should change—in the model, the product, the platform, or the organization.
Request a working session ↗ENGAGEMENT PROTOCOLREV 1.0
W01
OBSERVE
Instrument the real system and establish the baseline.
+W02
ISOLATE
Build a failure taxonomy and test causal hypotheses.
+W03
INTERVENE
Ship the highest-leverage model, product, and system changes.
+W04
TRANSFER
Leave behind tooling, controls, and an accountable team.
+OUTPUTA system your team can operate, measure, and defend.
REGULATED SYSTEMS / 04
Compliance becomes an engineering property.
Policies do not deploy software. We translate obligations into concrete architecture, evaluation thresholds, human oversight, release gates, monitoring, and audit evidence.
- Traceable model and data decisions
- Risk-tiered evaluation and release criteria
- Continuous monitoring with accountable escalation
- Evidence packages designed for review
FIELD NOTES / 05
Research for the
deployment gap.
View all field notes ↗PUBLICATION QUEUE / 000
Field notes are in preparation.
Original research and operating frameworks from Prestilla Labs will appear here.
Visit Insights ↗START HERE / 06
What is keeping your
AI system from working?
Bring us the messy version: the failing agent, the endless pilot, the blocked deployment, or the team design no one can resolve.
Start a diagnostic ↗