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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.

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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

Related services

API DevelopmentCustom AI IntegrationAI Strategy

Ready to Get Started?

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

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Key takeaways

Last updated

  • Retrieval augmented generation grounds model output in your own documents and records. It is the correct default for knowledge and support use cases, and it is far cheaper to maintain than fine-tuning.

  • Fine-tuning is for teaching format, tone, or a narrow classification behavior. It is the wrong tool for adding knowledge, because the knowledge goes stale and retraining is more expensive than updating an index.

  • Retrieval must respect the permissions of the person asking. An index built without permission awareness will surface documents to users who could not otherwise open them.

  • Production LLM systems need a regression test set. Without one, a prompt or model version change silently alters behavior and the first report comes from a customer.

Frequently Asked Questions

Common questions about LLM Integration

Retrieval augmented generation retrieves relevant passages from your own content and passes them to the model as context, so answers are grounded in your material and can cite it. Use it for support, knowledge base, and policy question use cases, which is most enterprise LLM demand.
Use retrieval to add knowledge and fine-tuning to shape format, tone, or a narrow classification behavior. Fine-tuning to inject facts is expensive to keep current, since every content change means retraining, while a retrieval index updates when the document does.
Ground answers in retrieved source passages, require citations, set an explicit path for declining to answer when retrieval finds nothing relevant, evaluate output against a regression set, and route low-confidence or high-stakes cases to a human. Grounding plus a decline path removes most of the exposure.
Match the model tier to the task rather than standardizing on one. Route simple classification and extraction to small fast models and reserve frontier models for reasoning-heavy work. Keep provider choice behind your own interface so it can change without application rewrites.
Route by task complexity, cache repeated and deterministic queries, trim retrieved context to what is actually used, set per-caller quotas, and monitor cost per transaction rather than total monthly spend. Cost per transaction is the number that reveals a regression.
Yes. Open weight models served with vLLM or similar runtimes handle most enterprise workloads on self-hosted GPUs. It is the right pattern when data residency, regulatory constraints, or sustained high volume make provider APIs unsuitable.

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