Build with AI — From Pilot to Production Playbook

Every company has AI pilots. Few have production AI features customers actually use daily. The gap between pilot and production is enormous: reliability, latency, cost, compliance, and continuous iteration.

Here's how we help teams close that gap.

The 5-stage AI integration journey

Stage 1 — Use case selection Pick a use case with:

• Clear business value
• Available data
• Manageable risk
• Reasonable scope (6–12 weeks to launch)

Stage 2 — Architecture Design the stack:

• LLM choice (GPT-4, Claude, open-source)
• RAG layer (if needed)
• Orchestration (LangChain, LlamaIndex, custom)
• Guardrails (prompt injection, PII, output filters)
• Monitoring (latency, cost, quality)

Stage 3 — Evaluation Build eval harnesses:

• 300–500 test cases
• Rubric scoring (accuracy, tone, safety)
• Automated runners
• Regression tracking

Stage 4 — Production deployment

• Latency targets (<2s for most use cases)
• Cost per action tracking
• Fallback strategies
• Human handoff design
• Error handling

Stage 5 — Continuous improvement

• Monitor drift
• Retrain or re-prompt
• Add capabilities
• Expand use cases

What we deliver to clients

• Production-ready AI features (chatbots, assistants, agents)
• Eval harnesses for continuous quality tracking
• Cost engineering to protect margins
• Guardrails for compliance
• Handoff design for reliability

AI integration patterns

RAG — Anchors LLMs to your data

Fine-tuning — Customizes model behavior

Prompt engineering — Optimizes instructions

Agents — Multi-step autonomous tasks

Hybrid — Combining patterns as needed

Common AI integration mistakes

• Shipping pilots without evals
• Ignoring cost per action
• No guardrails
• No human handoff
• Treating AI as deterministic
• No drift monitoring

What "done" looks like

• Feature live in production
• Evals running on every change
• Cost per action within budget
• Human handoff working
• Drift monitoring active
• Roadmap for iteration