Choosing a Default Model for Customer Support

Table of Contents

1. Why Model Selection Matters for Customer Support
2. Understanding Your Support Workload
3. Key Evaluation Criteria for AI Models
4. Building Your Repeatable Selection Framework
5. Testing and Validation Protocols
6. Cost and Performance Trade-offs
7. Migration and Rollout Strategy
8. Monitoring and Re-evaluation
9. Implementation Timeline
10. Next Steps and Long-term Planning

---

Why Model Selection Matters for Customer Support

Choosing a default model for customer support is not a one-time decision. It’s a repeatable operational process that your engineering team will execute multiple times between now and 2027 as new models release, capabilities improve, and your support volume scales.

The stakes are real. A poorly chosen model costs you money in three ways: wasted compute spend on oversized models, slower response times that frustrate customers, and support tickets that require human escalation because the model lacked reasoning depth or domain knowledge. A well-chosen model reduces your support burden by 40–60%, cuts response latency by 70–80%, and lets your human team focus on genuinely complex issues that require judgment, empathy, or policy decisions.

Most teams treat model selection as a technical choice—“let’s try GPT-4 versus Claude versus Llama”—without a structured framework. This leads to inconsistent decisions, repeated mistakes, and friction between engineering and operations. Instead, you need a decision framework that your team can run in 4–6 weeks, document, and re-run quarterly or when a major new model releases.

This guide walks you through that framework. It’s built for engineering leaders, fractional CTOs, and operations teams at seed-to-Series-B startups and mid-market companies who are building or scaling AI-powered customer support. It assumes you already have a support system in place and are now optimising the AI layer.

---

Understanding Your Support Workload

Before you evaluate a single model, you need to understand what your support team actually does and where AI adds value.

Map Your Support Channels and Ticket Types

Start by auditing your last 90 days of support tickets. Categorise them by:

• Channel: email, chat, phone, social media, in-app messaging
• Category: billing, technical troubleshooting, feature requests, account issues, documentation questions, bugs, policy clarifications
• Resolution time: how long from first response to resolution
• Escalation rate: what percentage require human handoff
• Sentiment: customer frustration level (low, medium, high)

This audit tells you three critical things:

1. Where AI can win: Billing questions, documentation lookups, and account issues are typically 60–70% of your volume and highly automatable. Technical troubleshooting is harder but still 40–50% automatable if your model understands your product deeply.

2. Your baseline metrics: If you’re currently handling 500 tickets per month with an average resolution time of 8 hours and a 25% escalation rate, you have a concrete target. A good AI model should reduce that escalation rate to 10–15% and drop resolution time to 2–4 hours for the tickets it handles.

3. Your support team’s bottleneck: Is it volume (too many tickets), complexity (tickets are hard to resolve), or latency (customers wait too long)? This shapes which model capabilities matter most.

As you think through your support architecture, consider how customer service models vary in their use of automation, channels, and response standards. Your AI model choice will influence whether you can operate a lean, high-touch model or need to scale to a hub-and-spoke structure.

Quantify Your Support Economics

Next, calculate the cost of your current support operation:

• Labour cost: (number of support staff) × (fully loaded salary + benefits) ÷ (tickets handled per person per month)
• Tool cost: helpdesk software, knowledge base, CRM, analytics
• Overhead: training, QA, management time
• Opportunity cost: time your engineering team spends on support issues instead of product development

If you have 3 support staff at £50k per year each, plus £5k per month in tools, handling 1,500 tickets per month, your cost per ticket is roughly £110. If you can reduce escalation from 25% to 10% and halve resolution time, you’re looking at £30–40k per month in labour savings.

This isn’t about replacing humans—it’s about leverage. A £2–3k monthly spend on API calls to a capable model, plus engineering time to integrate it, typically pays for itself within 6–8 weeks.

Define Your Support SLAs

Before you choose a model, be explicit about your service-level agreements:

• First response time: 15 minutes? 1 hour? 4 hours?
• Resolution time: 24 hours? 48 hours?
• Availability: 24/7? Business hours only?
• Quality threshold: What error rate is acceptable? (Most teams aim for 95–98% correct AI responses.)

These SLAs constrain your model choice. If you need sub-minute first response and 95%+ accuracy, you’ll need a larger, more capable model and probably a cached context strategy. If you can tolerate 5-minute responses and 90% accuracy, you have more flexibility.

---

Key Evaluation Criteria for AI Models

When you’re ready to evaluate models, use these seven criteria. They’re ordered by importance for most support workloads.

1. Domain Knowledge and Context Window

The model needs to understand your product, your customers, and your policies. This is not about raw intelligence—it’s about whether the model can hold enough context to reason about your specific domain.

What to test:

• Load your product documentation, FAQs, and recent support conversations into the model’s context window. Can it retrieve and reason about the right information?
• Ask it 20 real support questions from your ticket backlog. What’s the accuracy rate?
• How much of your documentation fits in the context window? (Claude 3.5 Sonnet has 200k tokens; GPT-4o has 128k; Llama 3.1 405B has 128k.)
• Can you use retrieval-augmented generation (RAG) to supplement the context window with your knowledge base?

For most support workloads, a mid-size model (70B–405B parameters for open-source; Claude 3.5 Sonnet or GPT-4o for closed-source) with a large context window (100k+ tokens) outperforms a larger model with a smaller window. The reason: context matters more than raw model size for domain-specific tasks.

2. Latency and Cost Per Token

Latency is the time from when a customer submits a question to when they see a response. Cost per token is the API price.

What to test:

• Measure end-to-end latency (including your own code, database calls, and model inference) for 100 real support queries. What’s the p50 and p99?
• Calculate the cost per query: (prompt tokens + completion tokens) × (model cost per token). For a typical support query (500 prompt tokens, 200 completion tokens), what’s the total cost?
• Is latency acceptable for your SLA? Most support teams aim for first response within 30–60 seconds.
• Can you batch queries or use caching to reduce token costs?

A useful rule of thumb: if your support volume is 1,000 queries per month, and each query costs £0.01 in tokens, that’s £10/month in model costs. If it’s 10,000 queries per month at £0.001 per token, that’s £10/month. Smaller, faster models (like Llama 3.1 8B or GPT-4o Mini) often have better cost-per-token than larger models, though they may sacrifice some accuracy.

3. Instruction Following and Tool Use

Your support model won’t just answer questions—it will need to take actions: look up a customer’s account, check an order status, apply a discount, escalate to a human, log a ticket.

What to test:

• Can the model reliably follow a structured prompt format? (Most modern models handle this well.)
• Does it support function calling / tool use? (Nearly all do, but implementation varies.)
• Can it chain multiple tools together? (e.g., “Look up the customer, check their order history, then apply a refund.”)
• How does it handle ambiguous or conflicting instructions?

For support, you need a model that can follow a consistent format, call your APIs reliably, and gracefully escalate when it’s unsure. Claude 3.5 Sonnet and GPT-4o both excel here. Llama 3.1 405B is strong but less battle-tested in production support workloads.

4. Hallucination Rate and Confidence Calibration

Hallucination—confidently stating false information—is the biggest risk in customer support. If your model tells a customer their refund was processed when it wasn’t, you have a problem.

What to test:

• Run 50 support queries where the correct answer is “I don’t know” or “I need to escalate.” How often does the model admit uncertainty versus making something up?
• For 50 queries with a correct answer in your knowledge base, how often does the model cite the right source?
• Test edge cases: outdated policies, contradictory information, missing data.
• Measure the false-positive rate: how often does the model confidently give wrong information?

Most teams aim for a hallucination rate below 2–3%. If your model hallucinates 5–10% of the time, you need either a more capable model, better prompting, or a mandatory human review step for high-risk queries (refunds, billing, policy decisions).

5. Multilingual and Tone Consistency

If your customers are global, your model needs to handle multiple languages. Even within English, you need consistent tone: friendly, professional, empathetic.

What to test:

• If you support multiple languages, test the model on real queries in each language. Is the quality equivalent?
• Write 10 support responses in your brand voice. Show them to your team. Does the model match that tone?
• Test edge cases: sarcasm, frustration, cultural references.

Most modern models handle tone reasonably well with good prompting. Multilingual support is where they vary more. Claude and GPT-4o are strong across ~100 languages. Llama 3.1 is good but less comprehensive.

6. Moderation and Safety

You need guardrails against abuse, jailbreaks, and off-topic requests.

What to test:

• Does the model refuse to answer off-topic questions? (It should politely redirect to support channels.)
• How does it handle abusive input from customers?
• Can you add custom moderation rules? (e.g., “Don’t discuss competitor products.”)

Most production support systems add a moderation layer on top of the model. OpenAI’s moderation API, Anthropic’s constitutional AI, and custom rule-based filters all work.

7. Vendor Lock-in and Availability

Finally, consider operational risk. What happens if your chosen model becomes unavailable, pricing changes dramatically, or you need to migrate?

What to test:

• Is the model available via multiple providers? (e.g., Claude is available via Anthropic’s API and AWS Bedrock.)
• If it’s open-source, can you self-host it if needed?
• What’s the vendor’s track record on pricing stability and uptime?
• Can you run a fallback model if your primary model is down?

For most teams, using a closed-source model from a stable vendor (OpenAI, Anthropic, Google) is fine. Build a fallback strategy: if GPT-4o is unavailable, fall back to Claude 3.5 Sonnet or Llama 3.1 via Bedrock.

For a deeper dive into how to structure your support model selection, review selecting the right customer support platform, which outlines a seven-pillar framework that complements the technical criteria above.

---

Building Your Repeatable Selection Framework

Now that you understand what to evaluate, here’s the framework you’ll run every time a new model releases or your support workload changes significantly.

Phase 1: Scoping (1 week)

Goal: Define what you’re optimizing for.

1. Audit your current support metrics (as described earlier).
2. List your constraints: SLAs, budget, regulatory requirements (data residency, compliance).
3. Identify candidate models: Which models released in the last 6 months? What’s your shortlist?
4. Define success criteria: What improvement matters most? (Faster responses? Lower cost? Higher accuracy?)

Output: A one-page brief with current metrics, constraints, and success criteria.

Phase 2: Benchmarking (2–3 weeks)

Goal: Test each candidate model on your real workload.

1. Prepare test data: Extract 100–200 real support tickets from your last 90 days. Anonymise customer data.
2. Build a test harness: A simple script that sends each ticket to each candidate model and logs latency, cost, and output.
3. Run the benchmark: For each model, measure:
   • Latency (p50, p95, p99)
   • Cost per query
   • Accuracy (does the response match what your support team would say?)
   • Escalation rate (how often does it say “I need to escalate”?)
4. Score each model: Use a simple rubric. Example:
   • Accuracy: 40% of score
   • Latency: 20%
   • Cost: 20%
   • Escalation rate: 20%

Output: A comparison table with scores for each model. Example:

Model	Accuracy	Latency (p95)	Cost/Query	Escalation	Total Score
GPT-4o	94%	2.1s	£0.008	12%	92/100
Claude 3.5 Sonnet	96%	1.8s	£0.010	10%	94/100
Llama 3.1 405B	88%	1.2s	£0.003	18%	85/100

Phase 3: Validation (1–2 weeks)

Goal: Test the top 2–3 models in production with real customers.

1. Set up A/B testing: Route 10–20% of incoming support tickets to each candidate model. Log all interactions.
2. Monitor for 1 week: Track accuracy, customer satisfaction, escalation rate, latency.
3. Collect feedback: Ask your support team which model they’d rather work with. Which one escalates the right issues?
4. Calculate true cost: Factor in engineering time to integrate each model, ongoing monitoring, and support overhead.

Output: A recommendation memo with validation results and final cost analysis.

Phase 4: Decision and Rollout (1 week)

Goal: Commit to a default model and plan the rollout.

1. Make the call: Based on benchmarking and validation, choose your default model.
2. Document the decision: Write a brief explaining why this model won, what trade-offs you accepted, and when you’ll re-evaluate.
3. Plan the rollout: How will you migrate from your current setup? (Gradual ramp-up? Hard cutover?)
4. Set review dates: When will you re-run this framework? (Quarterly? When a major new model releases?)

Output: A decision document and rollout plan.

---

Testing and Validation Protocols

Once you’ve chosen a model, you need ongoing validation to ensure it stays performant and doesn’t drift.

Automated Accuracy Testing

Every week, run a test suite of 50 real support tickets against your model. For each ticket:

1. Get the model’s response.
2. Compare it to the “golden” response (what your support team would say).
3. Score it: Correct, partially correct, or wrong.
4. Track the trend: Is accuracy stable or drifting?

Set a threshold: if accuracy drops below 90%, investigate why. It might be a model update, a shift in ticket types, or a gap in your knowledge base.

Latency Monitoring

Track response latency continuously. Set alerts:

• If p95 latency exceeds your SLA (e.g., 5 seconds), page the on-call engineer.
• If cost per query increases 20% month-over-month, investigate why.

Use your monitoring tool (Datadog, New Relic, etc.) to track these metrics in real time.

Customer Satisfaction Surveys

Every 50th customer who interacts with your AI support model gets a quick survey:

• “Was this response helpful?” (Yes / No)
• “Did we resolve your issue?” (Yes / Partially / No)
• “Would you have preferred to talk to a human?” (Yes / No)

Track these metrics monthly. If satisfaction drops below 85%, investigate.

Escalation Quality Review

When your model escalates a ticket to a human, log it. Weekly, review 10–20 escalations:

• Was the escalation appropriate? (Did the model correctly identify a hard case?)
• Could the model have handled it? (Was the escalation too conservative?)
• What pattern do you see? (Missing knowledge? Lack of context? Genuine hard cases?)

Use this to refine your prompts or knowledge base.

---

Cost and Performance Trade-offs

Choosing a default model is fundamentally about trade-offs. Here’s how to think about them.

The Accuracy-Cost Curve

Generally, larger models are more accurate but more expensive. Smaller models are cheaper but less accurate.

Example trade-offs:

• GPT-4o: 94% accuracy, £0.008/query, 2.1s latency
• GPT-4o Mini: 89% accuracy, £0.0005/query, 1.5s latency
• Llama 3.1 8B: 82% accuracy, £0.0001/query, 0.8s latency

If you have 10,000 support queries per month:

• GPT-4o: £80/month + engineering time = £500–1,000/month total
• GPT-4o Mini: £5/month + engineering time = £500–1,000/month total
• Llama 3.1 8B: £1/month + self-hosting costs = £200–500/month total

The engineering time is often the largest cost. A smaller model that requires more custom prompt engineering might cost more in total than a larger model that works out of the box.

When to Choose Each Model Class

Large, expensive models (GPT-4o, Claude 3.5 Sonnet):

• You have high accuracy requirements (95%+)
• Your support workload is complex (technical troubleshooting, policy decisions)
• You need strong tool use and chain-of-thought reasoning
• You can afford £1–5k/month in API costs
• You want minimal engineering time to get to production

Mid-size models (GPT-4o Mini, Claude 3 Haiku, Llama 3.1 70B):

• You have moderate accuracy requirements (90–94%)
• Your support workload is mostly FAQ and account lookups
• You need good latency and cost efficiency
• You’re willing to invest 2–4 weeks of engineering time
• You want to balance cost and capability

Small models (Llama 3.1 8B, Phi-3, Mistral 7B):

• You have tight cost constraints (< £100/month)
• Your support workload is well-defined and narrow (e.g., billing questions only)
• You’re comfortable self-hosting or using a cheap inference provider
• You have engineering bandwidth to optimise prompts and fine-tune if needed
• Latency is less critical

Hybrid Approaches

Many teams use a hybrid strategy:

1. Route by complexity: Simple queries (FAQ, account lookup) go to a small model. Complex queries (troubleshooting, policy decisions) go to a large model.
2. Use cascading fallback: Try a small model first. If it says “I’m not sure,” escalate to a larger model.
3. Fine-tune a small model: Start with a small open-source model and fine-tune it on your own support data. This can match the accuracy of a large model at 1/10th the cost.

For most support teams at seed-to-Series-B stage, a hybrid approach (80% small model, 20% large model) is optimal.

---

Migration and Rollout Strategy

Once you’ve chosen your default model, you need a safe way to roll it out without breaking your support experience.

Pre-rollout Checklist

Before you flip the switch:

1. Integration: Is your model integrated with your helpdesk, knowledge base, and customer database?
2. Monitoring: Are you tracking accuracy, latency, and cost in real time?
3. Fallback: What happens if the model is down? Do you have a fallback model or human queue?
4. Documentation: Have you updated your team on how the new model works and what to expect?
5. Training: Have you trained your support team to work with AI-assisted responses?

Rollout Phases

Phase 1: Canary (Days 1–3)

• Route 5% of incoming tickets to the new model.
• Monitor for errors, latency spikes, or unexpected escalations.
• Your team reviews every response manually.

Phase 2: Ramp (Days 4–10)

• Increase to 25% of traffic.
• Your team reviews a random sample of 20% of responses.
• Monitor accuracy, latency, and customer satisfaction.

Phase 3: Majority (Days 11–20)

• Move to 75% of traffic.
• Spot-check responses. Review all escalations.
• Monitor metrics closely.

Phase 4: Full Rollout (Day 21+)

• 100% of traffic on the new model.
• Continue monitoring. Review escalations weekly.
• Set a review date (e.g., 30 days post-launch) to assess overall performance.

Rollback Plan

If accuracy drops below 85% or latency exceeds 10 seconds, immediately roll back to your previous model. Don’t wait for the next scheduled review.

---

Monitoring and Re-evaluation

Choosing a default model isn’t a one-time decision. You need to re-evaluate regularly as new models release and your workload evolves.

Monthly Metrics Review

Every month, pull these metrics:

• Accuracy: % of responses your team rated as correct
• Latency: p50, p95, p99 response time
• Cost: Total spend on model API calls
• Escalation rate: % of tickets escalated to humans
• Customer satisfaction: % of customers who rated the response as helpful
• Error rate: % of responses that were factually wrong

If any metric drifts 10%+ from baseline, investigate.

Quarterly Model Review

Every quarter, revisit your model selection decision:

1. What new models released? Check Hugging Face, OpenAI’s model card, Anthropic’s announcements.
2. Did your workload change? More tickets? Different types of questions?
3. Did pricing change? Some vendors lower prices as models mature.
4. Are you hitting your SLAs? If not, can a different model help?

If you find a model that’s 10%+ better on your key metric (accuracy, cost, latency), run a mini-benchmark (1 week) to validate. If it wins, plan a rollout.

Annual Deep Dive

Once a year (or when your support volume doubles), run the full selection framework again. New models have likely emerged. Your team has learned what matters. Your metrics have matured. This is a good time to revisit the decision.

---

Implementation Timeline

Here’s a realistic timeline for choosing and deploying a default support model, assuming you’re starting from scratch.

Week 1: Scoping

• Audit your support tickets (90 days of data)
• Define metrics and SLAs
• Identify candidate models
• Output: One-page brief

Weeks 2–4: Benchmarking

• Prepare test data (100–200 real tickets)
• Build test harness
• Run benchmarks on 3–5 candidate models
• Score and rank
• Output: Comparison table and initial recommendation

Weeks 4–5: Validation

• Set up A/B testing in production
• Route 10–20% of traffic to top 2 candidate models
• Collect feedback from support team
• Output: Validation memo with final recommendation

Week 6: Decision and Planning

• Commit to a default model
• Plan rollout phases
• Brief your team
• Output: Decision document and rollout plan

Weeks 7–8: Integration and Testing

• Integrate model with your helpdesk and knowledge base
• Set up monitoring and alerting
• Run internal testing
• Output: Integrated system, ready for canary rollout

Weeks 9–10: Canary and Ramp

• Deploy to 5% of traffic (3 days)
• Ramp to 25% (7 days)
• Monitor closely
• Output: Validated system, ready for wider rollout

Weeks 11–12: Full Rollout and Stabilisation

• Move to 75%, then 100% of traffic
• Continue monitoring
• Train your team
• Output: Production system, baseline metrics established

Total time: 12 weeks, or 3 months.

If you already have a support system in place and are just swapping models, you can compress this to 6–8 weeks. If you’re building from scratch, add 2–4 weeks for infrastructure setup.

---

Next Steps and Long-term Planning

Once you’ve deployed your default model, here’s how to think about the next 12–24 months.

Immediate Next Steps (Next 4 weeks)

1. Establish a baseline: Document your current metrics (accuracy, latency, cost, escalation rate, customer satisfaction).
2. Set up monitoring: Ensure you’re tracking the seven metrics mentioned earlier in real time.
3. Schedule reviews: Put recurring calendar blocks for monthly metrics review and quarterly model review.
4. Document your decision: Write a brief explaining why you chose this model, what you’re optimising for, and when you’ll re-evaluate.

If you need help setting up monitoring or integrating your model with your support infrastructure, consider working with a partner who specialises in AI operations. PADISO’s AI & Agents Automation service focuses on exactly this: building repeatable, monitored AI systems that work at scale. We’ve helped 50+ companies in Australia and beyond move from ad-hoc model experiments to production AI support systems with clear metrics and rollout plans.

3–6 Months: Optimisation

Once your system is stable, focus on optimisation:

• Fine-tune prompts: Use your escalation data to refine your system prompts.
• Expand your knowledge base: Add FAQs, product docs, and policy clarifications that the model should know.
• Experiment with routing: Try routing different ticket types to different models.
• Measure ROI: Calculate how much you’ve saved in support labour and how much you’ve spent on models. Is it positive?

6–12 Months: Scaling and Expansion

As your model matures:

• Expand to other channels: If you’ve deployed in email, try chat or in-app messaging.
• Automate more workflows: Move beyond answering questions to taking actions (issuing refunds, resetting passwords, creating tickets).
• Add multilingual support: If you’re global, extend to other languages.
• Integrate with your product: Embed support AI directly in your product UI.

For larger-scale transformations—moving from a centralised support model to an AI-assisted distributed model, or completely rebuilding your support infrastructure—PADISO’s Platform Design & Engineering service can help you architect a system that scales to millions of queries per month.

12–24 Months: Strategic Evolution

By this point, you’ll have 12+ months of production data. Use it to:

• Re-evaluate your model choice: Run the full selection framework again. Have new models emerged? Has your workload changed? Is your current model still optimal?
• Consider fine-tuning: If you have 100k+ support tickets, you might fine-tune a smaller model to match the accuracy of a larger model at 1/10th the cost.
• Plan for multi-model strategies: Instead of a single default model, you might run a fleet: small models for simple queries, large models for complex ones, specialised models for specific domains.
• Invest in knowledge management: Your knowledge base is now your competitive advantage. Invest in keeping it up-to-date and well-organised.

Building a Sustainable Process

The key insight is this: model selection is not a project, it’s a process. You’re not choosing a model once; you’re choosing it every time a new model releases or your workload changes significantly.

To make this sustainable:

1. Assign ownership: Someone (usually your VP Engineering or Head of Operations) owns the quarterly model review.
2. Automate metrics collection: Don’t manually pull reports. Build dashboards that update in real time.
3. Build decision templates: Use the framework in this guide as your template. Run it the same way every time.
4. Document decisions: Keep a log of every model you evaluated and why you chose what you chose. This history is invaluable.
5. Allocate budget: Set aside £2–5k per quarter for model evaluation and experimentation. This is not a cost; it’s an investment in staying current.

The Broader AI Readiness Picture

Choosing a default support model is one piece of a larger AI strategy. If you’re serious about AI, you should also be thinking about:

• AI readiness: Are your data, infrastructure, and team ready to scale AI across your business? PADISO’s AI Strategy & Readiness service helps founders and operators answer this question.
• Compliance and security: As you build more AI systems, you’ll need to ensure they’re secure and auditable. If you’re pursuing SOC 2 or ISO 27001 compliance, your AI systems need to fit into that framework.
• Fractional CTO support: If you don’t have a CTO, you need someone senior to guide these decisions and oversee implementation. PADISO’s CTO as a Service provides exactly this: fractional CTO leadership for seed-to-Series-B startups.

The teams that win with AI are the ones that treat it as a systematic, repeatable process—not a one-off experiment. This guide gives you the framework. The rest is execution.

---

Summary

Choosing a default model for customer support is a repeatable, structured process that your engineering team can run every 3–6 months as new models release. Here’s the summary:

The Framework:

1. Scope (1 week): Audit your support metrics, define constraints, identify candidates.
2. Benchmark (2–3 weeks): Test each model on your real workload. Score and rank.
3. Validate (1–2 weeks): A/B test the top candidates in production with real customers.
4. Decide and rollout (1 week): Commit to a model and plan a phased rollout.

Key Evaluation Criteria:

1. Domain knowledge and context window
2. Latency and cost per token
3. Instruction following and tool use
4. Hallucination rate and confidence calibration
5. Multilingual and tone consistency
6. Moderation and safety
7. Vendor lock-in and availability

Ongoing Management:

• Track seven metrics monthly: accuracy, latency, cost, escalation rate, customer satisfaction, error rate, escalation quality.
• Re-evaluate quarterly when new models release or your workload changes.
• Run the full framework annually.
• Treat model selection as a process, not a one-time project.

Expected Outcomes:

• 40–60% reduction in support volume (via automation)
• 70–80% reduction in response latency
• 30–50% reduction in support labour costs
• 95%+ customer satisfaction with AI-assisted responses
• A clear, documented decision framework your team can reuse

The timeline is 12 weeks from decision to production, or 6–8 weeks if you’re swapping models in an existing system.

Start with scoping this week. You’ll have a recommendation by the end of month 2 and a production system by month 3. By month 6, you’ll have the data to optimise. By month 12, you’ll be ready to re-evaluate and evolve.

This is how you build sustainable, scalable AI support. Not with hype, not with one-off experiments, but with a clear process, real metrics, and a commitment to continuous improvement.

For help building this process at your company—whether you need a fractional CTO to oversee the project, hands-on engineering support to integrate your model, or strategic advice on AI readiness—reach out to PADISO. We’ve helped 50+ companies in Australia and beyond move from ad-hoc AI experiments to production systems that scale.

For more context on how to structure your support operations broadly, review Nielsen Norman Group’s hub-and-spoke model for customer-service information, which complements the AI model selection framework with guidance on information architecture and support workflows.

You can also explore different customer support models and tiers to understand how AI fits into your broader support operating model, or review Deloitte’s perspective on customer support operating models for enterprise-scale thinking.

Start small, measure relentlessly, and iterate. That’s how you choose—and keep choosing—the right model for your support team.