AI Integration

LLM Implementation & Deployment

Selecting a large language model is the easy part. Deploying it in a way that is cost-controlled, governance-aligned, and operationally sustainable requires decisions most organizations make too late. Usually after the vendor contract is signed and the pilot is already in production.

What you get

  • LLM selected for your use case, not the vendor with the best pitch deck
  • Deployment architecture aligned to your security and compliance requirements
  • Cost controls and usage governance before the model goes into production
  • Prompt engineering with documented quality controls your team can maintain
  • Monitoring and drift detection configured before the first production query

What This Covers

Specific capabilities and deliverables within this engagement.

Model Selection & Evaluation

  • Use-case-driven model evaluation (not benchmark-driven)
  • Build vs. buy vs. API analysis for your cost and control requirements
  • Vendor contract and lock-in risk assessment
  • Performance testing against your actual data and acceptance criteria

Deployment Architecture

  • Self-hosted vs. managed deployment decision framework
  • Infrastructure sizing for your query volume and latency requirements
  • Security boundary design (data isolation, PII controls)
  • Multi-model routing for cost optimization

Prompt Engineering & Governance

  • Prompt library design with version control
  • Output validation and quality control checkpoints
  • Human review loop design for high-stakes outputs
  • Prompt injection and adversarial input mitigation

Ongoing Operations

  • Usage cost monitoring and alerting
  • Model performance drift detection
  • Quarterly model evaluation for newer alternatives
  • Governance documentation for compliance review

Engagement flow

How the work progresses

Each step produces concrete decisions, artifacts, and sequencing guidance your team can use immediately.

1

Use Case & Requirements Scoping

Define the specific use case, quality requirements, cost constraints, and governance boundaries before evaluating any models.

2

Model Evaluation & Selection

Evaluate candidate models against your actual data and acceptance criteria, not vendor benchmarks or demo outputs.

3

Deployment & Integration

Deploy with governance controls, observability hooks, and cost monitoring configured from day one.

4

Operations Handoff

Document the deployment, configure monitoring, and establish the review cadence your team will own.

Best fit signals

This work is most valuable when the need is clear but structure, ownership, and sequencing are not yet defined.

You are evaluating LLMs for a specific use case and need an independent assessment, not a vendor pitch
Your LLM is already in production but lacks cost controls, monitoring, or governance documentation
You need LLM output in a format your downstream systems can actually consume
Your compliance team needs documentation on how the model is deployed and what data it can access

Ready to Get Started?

Book a strategy call to discuss your requirements and whether this engagement is the right fit.