Unitforce Technologies
Consulting Pvt Ltd
The unglamorous half of AI — versioning, serving, monitoring and rollback — owned by us so your team doesn't carry it.
Production deployment of your models and agents — containerised, versioned, and served on infrastructure you control. We own uptime, rollback and monitoring, so your team never has to.
Most AI work that fails to reach production does not fail on accuracy. It fails because nobody owned packaging, serving, versioning, monitoring or the path back when a new version turns out to be worse than the old one.
We take models and agents from whatever state they are in and make them deployable artefacts: containerised, reproducible, versioned in a registry, and served behind an interface the rest of your stack can call.
Deployment targets are yours — your cloud account, your VPC, your on-premise hardware where data residency requires it. The point is that the thing running in production belongs to you, and does not become something only we can operate.
Releases go out gradually. New versions are canaried against live traffic with the previous version still in place, and a rollback is a routine, rehearsed action rather than an incident.
Serving uptime and latency are the easy half. The half that catches teams out is silent degradation — input distributions drifting away from the training set, a dependency changing behaviour, quality falling while every dashboard stays green. Monitoring covers both.
This is also the stage where the Support commitment in our approach applies: monitoring, patches and SLA-backed fixes continue after go-live, because launch is not the end of the work.
Two outcomes are legitimate and we decide which one before the work starts: your team operates the system with us on support, or we operate it for you. What we will not do is leave the arrangement vague, because that is how a production system ends up with no owner at three in the morning.
Where your team takes it, handover is a deliverable rather than a farewell email — runbooks, the deployment pipeline, the monitoring dashboards and the rollback procedure, walked through with the people who will use them. Where we keep it, the same artefacts exist anyway, so the option to take it back is always open.
The practice of running machine learning in production: packaging models as deployable artefacts, versioning them, serving them reliably, monitoring for degradation, and being able to roll back safely when a new version underperforms.
Yes. Deployment targets your own cloud account, VPC or on-premise hardware, which is normally what data residency and security review require. The running system stays yours to operate.
Through canary releases: the new version takes a share of live traffic while the previous version stays in place, metrics are compared, and the change is either promoted or rolled back. Rollback is a routine operation, not an emergency.
By monitoring input distributions and output quality alongside uptime and latency, so drift is caught as a measured change rather than as a business complaint. Degradation is usually silent, which is exactly why it is monitored explicitly.
Agents that do the work, not just describe it — wired into the systems your team already uses.
Generative AI that answers from your facts, cites where they came from, and says nothing when it has no evidence.
An assistant inside the tool people already have open, grounded in what your company actually knows.
Whole processes automated end to end, with the exceptions routed to people instead of hidden from them.
Fraud flagged as it happens and credit scored at the point of decision — with reasons an auditor will accept.