1. Identify business needs (content creation, data analysis, internal tools).
2. Define evaluation criteria:
   • Capability (accuracy, reasoning, multilingual).
   • Cost (per‑token pricing & compute for self‑hosted).
   • Privacy & data‑security guarantees.
   • Ease of integration for a non‑technical team.
3. Score leading models on each criterion.
4. Weight criteria based on company priorities.
5. Recommend the model with the highest weighted score.

• State‑of‑the‑art multimodal (text, image, audio).
• Strong reasoning, coding, and creativity.
• Pricing: $5 / M input tokens, $15 / M output tokens.
• Enterprise contracts allow data‑logging opt‑out.
• Simple REST API, extensive SDKs, good docs.

• Highly aligned, safe‑first model; good for internal tools.
• Comparable performance to GPT‑4o on most benchmarks.
• Pricing: $3 / M input, $15 / M output.
• Data is not retained unless you explicitly opt‑in.
• API is similar to OpenAI’s; easy migration.

• Strong multilingual capabilities and integration with Google Workspace.
• Cheapest per‑token pricing, but limited data‑privacy controls.
• API still maturing; occasional latency spikes.

Adopt Anthropic Claude 3.5 Sonnet as the company‑wide standard model.

• Best balance of capability (9.0) and cost ($3 / M in).
• Strong privacy stance – no data retention by default.
• API parity with OpenAI makes onboarding easy for non‑technical staff.
• Enterprise support and SLAs available.

If a specific use‑case demands multimodal (image/audio) or the absolute highest performance, fallback to GPT‑4o for those projects.

1. Month 1: Pilot with 5 power users (internal docs, summarization).
2. Month 2‑3: Expand to all teams; build simple no‑code UI (Zapier‑style wrapper).
3. Month 4: Governance – set up data‑usage policies, audit logs.
4. Month 5‑6: Review usage, negotiate enterprise contract.

Assumed average usage: 2 M input tokens / month across the company.

Estimated monthly cost: ~$6,000 (Claude 3.5 pricing).

Additional budget: $3,000/quarter for tooling & training.

• Vendor lock‑in – negotiate flexible contract, keep abstraction layer.
• Cost overruns – monitor token usage, set hard limits, consider hybrid self‑hosted Llama for high‑volume workloads.
• Privacy breach – enforce opt‑out, conduct regular security audits.
• Model performance drift – schedule quarterly benchmark reviews.
• User adoption – provide low‑code UI, training sessions, internal champions.

We evaluated six leading LLMs against capability, cost, privacy, and ease‑of‑use. Anthropic Claude 3.5 Sonnet offers the optimal mix for a 60‑person organization seeking strong performance without sacrificing privacy or budget.

Next steps: approve pilot budget, assign a project lead, and start the Month 1 rollout.