.svg)
.svg)
Managed Services / AiOps
Keeping deployed models and AI systems accurate, compliant, and cost-effective
Models drift as real-world data shifts away from training data, prompt-based systems behave inconsistently as model versions change, and usage costs grow unpredictably without active management. AI Ops brings the same operational discipline to AI that other enterprise systems have always required.
Get started
TECHNICAL CAPABILITIES
The disciplines that keep models accurate and costs predictable
Performance monitoring & drift detection
Continuous monitoring of model and prompt-based system performance against defined accuracy and quality benchmarks, with drift detection to catch degradation over time.
Model optimization & cost management
Retraining, fine-tuning, or prompt adjustment based on monitored performance, with cost monitoring and optimization of model usage, token consumption, and infrastructure spend.
Governance & incident response
Governance and compliance monitoring to keep AI system behavior within defined guardrails, with incident response for failures or unexpected outputs.
OUR Approach
A structured approach to modernizing what matters
.webp)
01
Monitor
Continuous monitoring of model and prompt-based system performance against defined accuracy and quality benchmarks.
02
Detect drift
Drift detection to identify when model or system behavior degrades over time.
03
Retrain & adjust
Retraining, fine-tuning, or prompt adjustment based on monitored performance.
04
Manage cost
Cost monitoring and optimization of model usage, token consumption, and infrastructure spend.
05
Govern & respond
Governance and compliance monitoring to keep AI system behavior within defined guardrails, with incident response for failures or unexpected outputs.
Outcomes
The payoff of operational discipline
Accuracy that holds
AI systems that maintain accuracy and reliability after deployment, not just at launch.
Predictable costs
Predictable, managed AI operating costs.
Faster drift detection
Faster detection of model drift or degraded performance.
Governance that keeps pace
Governance and compliance maintained as models and usage evolve.
Tech stack
The technology behind the transformation
FAQ
Frequently asked questions
How fast can you start?
No. We build the substrate environments, reward models, eval harnesses, data pipelines, feedback loops and hand it to your training infrastructure. You run the GPUs. We run the engineering around them. That lane discipline is part of why we work as a partner, not a vendor.
Can the work be co-authored or made public?
No. We build the substrate environments, reward models, eval harnesses, data pipelines, feedback loops and hand it to your training infrastructure. You run the GPUs. We run the engineering around them. That lane discipline is part of why we work as a partner, not a vendor.
How do you handle confidentiality and data?
No. We build the substrate environments, reward models, eval harnesses, data pipelines, feedback loops and hand it to your training infrastructure. You run the GPUs. We run the engineering around them. That lane discipline is part of why we work as a partner, not a vendor.
Do you run the actual training?
No. We build the substrate environments, reward models, eval harnesses, data pipelines, feedback loops and hand it to your training infrastructure. You run the GPUs. We run the engineering around them. That lane discipline is part of why we work as a partner, not a vendor.
Ready to modernize your core tech without the risk?
Let’s Connect
