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Rohit Soni
Rohit Soni

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How to Audit an AI Implementation Partner Before Signing: A Technical Checklist (Bangalore 2026)

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85% of AI projects fail before production (Gartner). The failure mode is almost always operational, not algorithmic. And it almost always traces back to the wrong implementation partner.

Here's a technical due diligence framework for evaluating AI implementation firms — built for engineers and technical leads who are involved in vendor selection.

The lifecycle gap most evaluations miss 

What most evaluations test:    Model accuracy, portfolio, pricing
What actually determines ROI:  MLOps maturity, integration depth,
                                post-deployment ownership, compliance arch
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The model is roughly 20% of the work. The rest is everything that makes it run reliably in production.

Technical questions to ask at each stage

Data engineering capability

→ Can you work with our existing stack? [cloud warehouse / on-prem / streams]
→ How do you handle unstructured inputs? [clinical notes / scanned docs / logs]
→ What does your data quality audit process look like before model dev begins?
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Model development rigour

→ How do you handle class imbalance in our domain?
→ Custom architecture or fine-tuned foundation model — how do you decide?
→ What does your train/val/test split strategy look like for time-series data?
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MLOps and production readiness

→ Describe the monitoring setup for a model you deployed 18 months ago
→ Is retraining triggered by drift detection or scheduled? Who initiates it?
→ What does your rollback process look like if a new model underperforms?
→ How are models versioned and how are shadow deployments managed?
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Compliance and security architecture

→ Data storage location and residency controls [DPDP Act compliance]
→ Encryption at rest and in transit — what standards?
→ Access controls and audit trail for model inference logs
→ Regulatory clearances held: CDSCO / RBI / SEBI / FDA / CE
→ Model explainability approach for regulated use cases
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Post-deployment SLA

→ P1 incident definition and response time
→ Drift threshold that triggers a retraining alert
→ Who is the dedicated post-launch contact — team or individual?
→ What is included in managed services vs separately billed?
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The one question that reveals everything

"Describe the monitoring setup for a model you deployed 18 months ago. How is it still performing? What changed since go-live?"

A firm with genuine production experience answers this specifically. A firm that excels at pilots deflects or gives a generic answer.

The discovery sprint test

Before full implementation: commission a 2–4 week paid sprint (₹2–5L) using your real data. Evaluate:

[ ] Technical scoping document — specific or vague?
[ ] Data readiness assessment — honest about gaps?
[ ] Proposed architecture — cloud-native, modular, explainable?
[ ] Phased roadmap — milestone-based with clear exit criteria?
[ ] Retraining and monitoring plan — included from day one?
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A firm that refuses the sprint and pushes straight to full implementation contract is either overconfident or afraid of what your data will reveal.

Red flags summary 

[ ] Only pilot/POC case studies — no production deployments
[ ] Compliance questions answered with vague reassurances
[ ] "We'll hand off to your IT team" after deployment
[ ] Refuses paid discovery sprint
[ ] References all from the past 3–6 months only
[ ] Pivots domain questions to technology answers
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Full guide with vendor scorecard: blog link

What's on your AI vendor technical checklist? Drop it in the comments.

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