Gen AI USe case Estimation

✅ How to Build Estimation for a GenAI Use Case

Step 1: Identify the Use Case Scope

• What business problem are you solving? (Customer support, document processing, fraud detection, etc.)

• Who are the end users? (Internal ops, customers, auditors)

• What is the expected volume? (e.g., 1M queries/month)

• What data sources are involved? (Structured, unstructured, third-party)

Example:

• Use case: AI copilot for customer support

• Volume: 50,000 tickets/month

• Channels: Web, mobile, call center

• Data: CRM + knowledge base + historical tickets

Step 2: Break the Work Into Components

1. Data Preparation

   • Cleaning, structuring, labeling

   • Data integration (internal + external)

2. Model Selection

   • Pre-trained LLM or fine-tuning

   • Classical ML for structured tasks

3. RAG / Knowledge Layer

   • Vector DB setup, indexing

   • Search quality / retrieval metrics

4. Platform & Orchestration

   • API layer, prompt management

   • Guardrails, monitoring, logging

5. Integration

   • Connect AI platform to CRM, ERP, ops systems

6. Compliance & Security

   • Access controls, logging, PII masking, audit

7. Testing & Validation

   • Accuracy, latency, throughput

8. Deployment & Operations

   • CI/CD pipelines, monitoring, SLOs, cost management

Step 3: Estimate Effort per Component

• Map each component to roles & effort (days / FTEs)

• Example for BFSI GenAI pilot (~1M queries/month):

Component	Team	FTEs	Duration (Weeks)
Data preparation	Data engineers	2	4
Model selection & evaluation	ML engineers	2	3
RAG / Knowledge DB	ML + Infra	2	2
API & orchestration	DevOps + backend	2	3
Integration	Backend + SME	2	3
Compliance & Security	Security + Risk	1	2
Testing & validation	QA + SME	2	2
Deployment & monitoring	DevOps	1	1–2

Estimated timeline: ~10–12 weeks for pilot, scalable rollout 3–6 months depending on # of use cases.

Step 4: Estimate Cost

1. People Cost

• Sum FTEs × rate × duration

• Example:

  • 10 FTEs × ₹2L/month × 3 months → ₹6 Cr (for pilot)

2. Infrastructure / Cloud / AI Cost

• Compute for model inference & training

• Storage for data & vector DB

• Example:

  • Managed LLM API: ₹10–20 L/month

  • Cloud hosting + vector DB: ₹5–10 L/month

3. Licenses / 3rd Party

• API licenses, commercial models, enterprise vector DB

4. Contingency

• 10–15% buffer for uncertainty

Total Pilot Cost Example: ₹8–10 Cr

Scale Cost: Multiply by # of use cases or expected usage.

Step 5: Use Metrics for Executive Confidence

• Business KPIs: TAT reduction, productivity, risk mitigation

• Technical KPIs: Latency, throughput, uptime

• Cost KPIs: Cost per inference, ROI %

Example Statement:

“For a customer support GenAI pilot with 50k tickets/month, we estimate 10–12 weeks to pilot, ~₹8–10 Cr cost, handling 1M daily inferences at 99.95% uptime with 20–30% productivity improvement.”

Step 6: Timeline Phasing

• Phase 1 – Discovery & Data Prep: 3–4 weeks

• Phase 2 – Model Evaluation & RAG Setup: 3 weeks

• Phase 3 – Integration & Orchestration: 3–4 weeks

• Phase 4 – Testing, Security, Deployment: 2–3 weeks

• Phase 5 – Scale Rollout: 3–6 months (add new use cases iteratively)

✅ Executive Answer Template (Interview / Presales)

“I estimate GenAI use cases by breaking them into data, model, RAG, orchestration, integration, compliance, testing, and deployment. For each component, we map team effort, infrastructure, and timeline.For a typical BFSI pilot with ~1M daily inferences, it usually takes 10–12 weeks to pilot, involving 8–10 FTEs and a cost of ₹8–10 Cr, including cloud and managed AI services.The rollout to multiple use cases is phased over 3–6 months, with measurable productivity, cost, and regulatory KPIs tracked throughout.”

📝 GENAI USE CASE ESTIMATION TEMPLATE

Component	Team / Role	FTEs	Duration (Weeks)	Monthly Cost per FTE (₹L)	Total Cost (₹L)	Notes
Data Preparation	Data Engineers	2	4	2	16	Cleaning, labeling, integration
Model Selection & Evaluation	ML Engineers	2	3	2	12	Choose model / LLM / fine-tuning
RAG / Knowledge DB	ML + Infra	2	2	2	8	Vector DB, indexing, search quality
API & Orchestration	Backend + DevOps	2	3	2	12	Prompt routing, guardrails, API layer
Integration	Backend + SME	2	3	2	12	Connect to CRM / ERP / Ops
Compliance & Security	Security + Risk	1	2	2	4	IAM, logging, audit-ready
Testing & Validation	QA + SME	2	2	2	8	Accuracy, latency, throughput
Deployment & Monitoring	DevOps	1	2	2	4	CI/CD, monitoring, cost tracking
Subtotal – People					76	Sum of above
Cloud / AI Infrastructure	Cloud / LLM / Vector DB	-	-	-	15	Monthly cost for pilot
3rd Party Licenses	Vendors / APIs	-	-	-	5	Optional
Contingency (10%)	-	-	-	-	9.6	Safety buffer
Total Estimated Cost (₹L)					~105–110	Pilot-level estimate

⏱ Timeline Estimation (Weeks)

Phase	Duration	Description
Discovery & Data Prep	3–4	Understand use case, collect & clean data
Model Selection & RAG Setup	3	Choose model(s), setup vector DB, initial testing
Integration & Orchestration	3–4	Connect to backend / CRM / Ops systems
Testing, Security, Deployment	2–3	Validate accuracy, latency, regulatory compliance
Scale Rollout	3–6 months	Add multiple use cases, optimize cost, monitor adoption

Total pilot: ~10–12 weeks

Full rollout (multi-use case): 3–6 months

💰 Costing Notes

• People cost: FTE × duration × monthly cost

• Cloud / AI infrastructure: Include compute for training, inference, storage, vector DB

• Licenses / APIs: LLM, HF, vector DB, connectors

• Contingency: 10–15% buffer for unknowns

📊 KPI / Metrics to Track

KPI	Target
Inferences / Day	1–2M
Latency	<300–800 ms
Uptime	99.95%+
Productivity Gain	20–30%
Cost Reduction	30–50% (FinOps model)
Regulatory Compliance	100% audit-ready