AI doesn't stay reliable just because it shipped.
Models change. Data changes. Business rules change. Performance can drift quietly after launch. We monitor, evaluate, tune and govern production AI so teams know what it is doing and when it needs attention.
The problems begin after the demo is over
Silent drift
Accuracy or behaviour degrades gradually without triggering a traditional application error.
Model changes
A model provider changes behaviour or performance even though your application code did not change.
Business changes
Policies, processes or source information change while the AI continues using old assumptions.
No evidence
Someone asks what the AI did, why it did it, or what source it used — and there is no usable record.
What we manage
Monitoring
Track appropriate operational metrics such as answer quality, task success, containment, escalation rate, latency, failures, cost and usage — as relevant to the deployment.
Evals
Maintain evaluation suites and run them when relevant components change — model, prompts, tools, knowledge or workflows.
Tuning
Use real production failures and evaluation results to improve behaviour.
Knowledge maintenance
Keep relevant knowledge sources current where included in scope.
Incident response
Provide a defined escalation path when the system behaves unexpectedly.
Operational review
Regularly report usage, failures, escalations, evaluation results, changes made, known risks and recommended improvements.
Governance — and proving it
Important AI actions should be controlled, attributable and reviewable. Governance needs to exist in the system, not only in a policy document.
Guardrails
Define what the AI system is allowed and not allowed to do.
Human approvals
Introduce human checkpoints before consequential actions where appropriate.
Audit trail
Record relevant information around action, trigger, input, output, tool or system touched, approval, and identity or context.
Access control
Apply role-based permissions to AI capabilities and information.
Evaluation evidence
Maintain evidence that important behaviour has been tested.
Compliance readiness
Support implementation of technical controls relevant to applicable privacy, data and AI governance obligations — without claiming blanket legal compliance or certification.
What we ship, we keep running
Our live agents are not handed over and forgotten. They run with human-in-the-loop handoff, consent and session-data handling, and knowledge sources that have to stay current after launch — otherwise answers quietly go stale.
We also operate The ANT Space itself and the AI running inside KyndCare's homecare coordination. Operating our own AI is where we learned what actually degrades after go-live, and it's what we bring to systems somebody else delivered.
Tender Wizard e-tendering platform · Malaysia
Tender Pintar
It runs inside a platform we don't own, so its knowledge sources and escalation rules have to be kept current, not just launched.
Two governed AI companions · Malaysia
Emergence Mirror Labs
Consent, session-data handling and knowledge currency are standing obligations here — they don't end at launch.
ANT
The ANT Space
We operate the platform our clients' agents run on, which means we carry its uptime, versioning and regressions.
Homecare · AI
KyndCare
Homecare runs every day, so degradation surfaces as a service failure rather than a merely unhelpful answer.
Live
Already have AI in production? We'll take it over.
The system does not have to be one Proppify built. If another team or vendor delivered it and nobody is actively operating it, we can assess the environment and take over monitoring, evaluation, tuning and governance.
Assess
Understand the existing architecture, workflows, dependencies and operational risks.
Baseline
Establish current behaviour and evaluation benchmarks.
Operationalise
Set up monitoring, evaluation, reporting and governance.
Manage
Run the agreed operational service on an ongoing basis.
Starting points — not fixed plans
Final scope depends on system count, usage, operating hours, support expectations, governance requirements and technical complexity.
Monitor
From ₹1.5 lakh / month
For one or two AI systems requiring monitoring, evaluations and regular operational review.
Operate
From ₹3 lakh / month
For multiple systems or more active operational involvement including tuning and incident support.
Govern
From ₹5 lakh / month
For environments requiring more extensive governance, audit reporting, access reviews or operational controls.
Deployment is not the end of the AI lifecycle.
If you have AI in production and nobody is actively operating it, let's talk.