Ship & govern AI reliably in production
The pipelines, monitoring and governance that turn models and agents into dependable production systems — and keep them performing.
a model is a product, not a project
/ the difference
Models decay.
We keep them sharp.
Ship a model and walk away and it decays silently — data shifts, accuracy erodes, trust goes with it. We watch it continuously and retrain before it costs you a decision.
/ how we run it
One loop, running 24/7
Build, test, deploy, monitor, retrain — automated end to end. The moment monitoring sees drift, the loop retrains and ships a fix, with full lineage and instant rollback.
/ the payoff
More accuracy.
Lower bill.
Teams assume reliability costs more. Run a model as a product and the opposite happens — the same loop that keeps it accurate also keeps it cheap.
accuracy
held sharp ↑
run cost
≈ 35% lower ↓
Monitoring catches waste, right-sizing and autoscaling cut spend, and automated retraining holds accuracy — you stop trading one for the other.
/ the cost of running it well
Cheaper and
more reliable
Set your compute spend and risk — see what FinOps plus monitoring saves in a year.
Estimated annual saving
run-cost saved / year
downtime cost avoided / year
Right-sized and watched, this stack saves ≈ $588.0K a year.
/ what we watch
Every signal, in real time
Accuracy, drift, latency and cost — watched continuously, so the loop catches a problem before it reaches a decision or shows up on the bill.
ACCURACY
✓ in range
DATA DRIFT
✓ stable
LATENCY
✓ p95 ok
RUN COST
✓ optimised
/ what we run
Every model,
every kind
One operating discipline across the whole AI estate — classic models, LLMs and agents alike.
Classic ML models
Forecasts, scores and classifiers — versioned, monitored, retrained on drift.
LLMs & GenAI
LLMOps: prompt versioning, eval suites, guardrails and tracing.
AI agents
AgentOps: scoped tool permissions, run logs and observability on every action.
Cost & FinOps
Run-cost monitoring, right-sizing and autoscaling — accuracy up, bill down.
Governance
Lineage, approvals and audit trails wired in, not bolted on.
Models you already run
We retrofit and wrap what's deployed — no rip-and-replace.
/ the proof
Reliability you
can bank on
What continuous operations buy you, in numbers:
faster model deploys
AMDIM engagements
service uptime
AMDIM SLAs
lower AI run cost
FinOps tuning
ML teams
ship in days, not quarters
Risk
lineage and approvals on every model
Finance
AI run-cost under control
/ why it pays
Outcomes you can measure
faster deploys
CI/CD for models
Automated training, testing and deployment — ship safely, roll back instantly.
service uptime
Drift & performance monitoring
Catch data drift and decay before they cost you a decision.
lower run cost
Governance & cost control
Lineage, approvals and FinOps so AI stays compliant and affordable at scale.
AMDIM engagements · AMDIM SLAs · FinOps tuning — verified, not invented.
/ what we deliver
End-to-end, not half-built.
/ before you commit
Fair questions
Yes — LLMOps and AgentOps are core: prompt versioning, eval suites, guardrails, tracing and cost monitoring for GenAI and agents.
See where you stand in 5 minutes.
Take the MLOps Maturity Scorecard — a tailored score and the fastest path to impact.