Your AI pilot worked in the demo. It never reached production.
Most don't. The model wasn't the problem — the integration, the data, the approvals and the edge cases were. We take stalled pilots, work out what's missing, and give you a practical path to production.
Four reasons it's sitting in a demo environment
The gap between a convincing AI demo and a production system is usually not the model itself.
It was never connected
The demo ran on sample data. Production needs your ERP, CRM, ticketing system or internal applications — and nobody scoped that work.
The data isn't ready
Inconsistent, undocumented and spread across systems. The model was fine. What you fed it wasn't.
Nobody would approve it
No audit trail. No human checkpoint. No clear answer to "what happens when it's wrong?" So IT, security or risk said no.
It handled the happy path
80% accuracy demos beautifully. Production is where the other 20% becomes someone's actual job.
A four-week production-readiness assessment
We assess what you built, what is missing, and what it would realistically take to move it into production.
If the pilot should not be finished, we'll say so. If it should, you leave with a scoped recovery plan.
Assess
Understand:
- what was built
- what it runs on
- current performance
- current architecture
- where it breaks
Diagnose
Identify production gaps across:
- integrations
- data
- accuracy
- security
- governance
- approval points
- infrastructure
- exception handling
Decide
Determine whether to:
- finish the existing pilot
- rebuild specific components
- rebuild the solution
- stop the project
The recommendation includes reasoning.
Plan
If worth finishing, provide:
- required technical work
- dependencies
- integrations
- risks
- estimated effort
- implementation sequence
- indicative delivery cost
What you get
Written assessment of the current pilot
Specific gaps between demo and production
Integration requirements
Data requirements
Accuracy and edge-case findings
Governance and approval model
Production architecture recommendations where relevant
Go / no-go recommendation with reasoning
Scoped path to production
Estimated engineering effort
Indicative implementation cost
If the right answer is “don't finish this,” you'll hear it during the assessment — not after another six months of spend.
Fixed-price engagement
Starting from ₹3 lakh
Final scope depends on the number of systems, integrations, workflows and environments being assessed.
One workflow · Senior engineering team · Fixed deliverables
The four weeks cover the assessment and production-readiness plan — not full AI implementation.
We know the distance from demo to live, because we've closed it
Three ANT agents are live across two client platforms in Malaysia, and KyndCare runs homecare coordination with AI through its daily operations. None of them shipped because a prototype worked — they shipped once integration, grounding, permissions, handoff and failure paths were actually dealt with.
That's the same distance a stalled pilot still has to travel. We assess it from the position of having walked it, not from a framework.
Tender Wizard e-tendering platform · Malaysia
Tender Pintar
The agent had to land inside a platform already carrying real users, which is exactly where most pilots stall — the model was never the hard part.
Two governed AI companions · Malaysia
Emergence Mirror Labs
Two companions taken from concept to public use, including the refusal and handoff behaviour a demo never has to prove.
ANT
The ANT Space
We built the layer a stalled pilot is usually missing — grounding, handoff, tenancy, tool execution and cost control — as a platform rather than per project.
Homecare · AI
KyndCare
AI that had to survive contact with daily operations — care coordination doesn't tolerate a system that's right most of the time.
Live
Send us the pilot. We'll tell you whether it's worth finishing.
One conversation. No obligation.