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.
Annual run cost + unmonitored downtime loss
You keep / year
$588.0KAnnual compute / inference bill
$108.0K trimmedUnmonitored downtime loss / year
$480.0K avoidedRight-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
/ the depth
Beyond the basics —
the full toolbox
6 capabilities, each backed by a real toolbox — manual to autonomous. A taste below; the full library runs deep.
techniques · 12 disciplines
Ship
from notebook to production, safely
Serve
predictions at the latency and cost you need
Monitor & drift
catch a model going wrong before customers do
Track & register
every experiment, model and feature versioned
Govern
reproducible, auditable, compliant
AIOps
ops intelligence that runs the platform for you
/ your industry
The hard problems
that actually pay
Every sector has a handful of problems that are genuinely hard — and genuinely worth it. Pick your industry for a taste; the full set lives on the industries hub.
Manufacturing
why it’s hard — Defects are rare events on fast lines; models run at the edge in real time, with near-zero tolerance for a missed fault.
$50B/yr lost to unplanned downtime (Deloitte); AI-in-manufacturing ~35% CAGR to 2030 (Grand View).
On-edge model serving & OTA updates at line speed
Deploy, version and update inspection models on machines at the edge — offline-tolerant, rolled back in seconds if a version misbehaves.
The failure is a sound the cloud is too slow to hear — and the line never stops for a redeploy.
Drift monitoring on defect-vision models per line & SKU
Watch each line's inspection model for the moment new lighting, materials or a new SKU quietly erodes catch rate.
The model that hit 99% at launch degrades one shift-change at a time, unnoticed until a recall.
evidenceInspectors miss 20–30% of defects; AI vision hits 95–99% (iFactory) — but only while it stays calibrated
Continuous training for predictive-maintenance models
Feed fresh failure labels back into RUL models automatically so predictions track the asset as it ages, not as it was.
A maintenance model trained on last year's wear predicts last year's failures.
/ 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.