Managed AI Workforce
We do not just deploy the agents and disappear. We monitor them, tune them, maintain their integrations, review their performance, and help the system keep producing value after launch.
3
service tiers
24/7
monitoring mindset
Weeks
to get a managed pilot live
ROI
protected after launch, not left to drift
Why managed
Teams usually underestimate monitoring, workflow tuning, escalation refinement, and integration upkeep. Managed AI is built for companies that want outcomes without creating a second operations burden internally.
A managed model is usually the fastest route when the business needs results now and does not want to hire an internal AI operations function first.
The quality of the system is actively reviewed, not assumed. That matters when customer-facing workflows need reliability and fast incident response.
The deployment keeps getting tuned based on real usage instead of becoming a static automation that quietly degrades over time.
Plans
All retainers begin after the deployment is live.
Starter
For founder-led teams proving the first workflow.
Rs 14,999 / month
Plus one-time build fee
Growth
For businesses running multiple live workflows.
Rs 39,999 / month
Plus one-time build fee
Enterprise
For larger deployments with stricter controls and coverage.
Rs 99,999+ / month
Custom based on scope
All retainers are quoted in INR, exclusive of GST, and begin only after the live system is deployed.
What the retainer covers
That includes the operating work most teams underestimate at the start.
We watch uptime, quality, and workflow health so issues get caught before they become business problems.
We refine prompts, escalation logic, and workflow branches based on real interactions instead of leaving the system static.
APIs, forms, CRMs, and internal systems change. We handle that upkeep so the deployment does not quietly degrade.
We review what the system is doing well, where confidence drops, and where buyer or user behaviour suggests the workflow should be improved.
The retainer includes visibility into throughput, outcomes, and the commercial value the deployment is actually producing.
When something needs human attention, there is a named team behind the deployment instead of a support queue that barely knows your setup.
First 30 days
The process is designed to get one good workflow live before expanding further.
Week 1
We map the current process, define the success metric, and choose where the agent should act alone versus escalate.
Week 2
Channels, CRM, docs, calendars, and any required internal systems are connected with clean permissions and workflow rules.
Week 3
We test with a narrow audience, review outputs, tighten prompts, and confirm the escalation behaviour before wider release.
Week 4
The system goes live with monitoring, early reporting, and a clear plan for tuning based on real-world behaviour.
ROI economics
That usually shows up in one of three ways.
When the deployment touches sales or conversion, weak monitoring or poor tuning can mean lost opportunities. Managed coverage protects that upside.
When customer-facing automation needs to stay reliable, the value of consistent service quality often outweighs the monthly management cost quickly.
When the alternative is asking senior internal people to own monitoring, tuning, and maintenance, the managed model often wins on focus alone.
FAQ
The goal here is not to hide the tradeoffs. It is to show how rollout, ownership, and ROI are handled in practice.
Managed AI is usually the better first move when the company wants results quickly but does not yet want to build an in-house team for monitoring, prompt tuning, quality control, and integration maintenance.
No. The retainer is meant for active management after launch. Build and deployment happen first, then the monthly service begins when the agent is operational.
After launch we monitor quality, handle incidents, tune prompts and workflows, maintain integrations, and review performance so the deployment keeps improving instead of slowly drifting.
A retainer makes sense when the operational cost of poor response quality, broken automations, missed revenue, or internal drift would be higher than the monthly management cost.
We will recommend the right tier, the right first workflow, and the likely commercial payoff before you commit.
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