Solution
Build vs Buy — AI Governance & Quality Engineering
The Decision Your Leadership Team Is Having Right Now
Should you build your AI governance capability in-house — or buy it? It's the right question. Most enterprises get the answer wrong — not because they're unsure, but because they're comparing the wrong things. Here's an honest framework.
01
₹4.85–6.85Cr
3-year total cost — Build internally
02
₹1.8–3.6Cr
3-year total cost — Qapitol managed service
03
12–18 months
Time to first value — Build internally
04
14 days
Time to first value — Qapitol managed service
05
1,400+
Regulatory obligations, updated with every circular
The challenge
What makes this hard
- The Talent Problem: An AI governance engineer who understands IRDAI circulars, adversarial ML, DPDP data lineage, and can build eval pipelines is being hired by Google and OpenAI at ₹80L+/year. Your AI team will build the architecture, then the person who built it will leave. You'll own something nobody else can maintain.
- The Regulation Velocity Problem: IRDAI publishes a new circular. EU AI Act enforcement phases advance. DPDP rules are updated. Every regulatory change requires a manual update to your internal system. That's a permanent maintenance burden — one that needs a specialist team to keep current, not a quarterly sprint.
- The 12-Month Gap Problem: Building a proper AI governance capability takes 12–18 months. Your IRDAI deadline is Q1 2026. The math doesn't work. You need coverage now — not at the end of an internal build programme that will slip twice before it's done.
- The Accountability Gap Problem: When your internally-built governance system misses a violation and a regulator asks why, who answers? Your internal team. With Qapitol, there's a contract, an SLA, and a named accountable party. The accountability — and the risk — shifts.
- The 'One Model' Problem: You build governance for your current GPT-4o deployment. Then you adopt Llama for cost reasons and add Gemini for a new product. Your internal governance build is suddenly outdated. Qapitol already supports all three — your engineers can focus on building products, not governance infrastructure.
What we deliver
The Qapitol approach
01
Qapitol Manages Governance Layer
SLA-backed, always current with regulatory updates, accountable delivery metrics monthly.
02
Your Team Owns Data & Model Architecture
Domain-specific decisions about AI builds and rationale remain internal.
03
Co-Owned Eval Pipeline
Qapitol provides QAVE; your team customizes test scenarios for specific model behavior and risk profile.
04
Internal Governance Champions Training
Knowledge transfer built into service contract; internal capability compounds over time without creating dependency.
Next step
Bring Build vs Buy — AI Governance & Quality Engineering to your stack
Scope it in one call — outcomes defined upfront, free assessment included.