Focused deployments covering a single use case with RAG architecture and one system integration typically run six to ten weeks. Implementations involving fine-tuning, multi-agent orchestration, private deployment, or multiple system integrations extend to fourteen to twenty-four weeks. Data readiness is the variable that most consistently determines whether a project finishes on schedule.
Why LLM Implementations Fail and How We Prevent It
The implementation decisions made in the first two weeks determine whether your LLM reaches production or gets shelved.
Understanding why LLM projects stall is the most operationally useful thing we can share before any engagement begins. Most failures trace back to three root causes that a structured implementation methodology addresses before they become problems.
1. Data unreadiness: models surface outdated or incomplete information when the underlying data architecture has not been audited first.
2. Missing governance architecture: LLM outputs in regulated industries require human-in-the-loop controls, confidence scoring, and output logging to remain auditable and compliant.
3. Integration failure: an LLM that operates outside your existing workflows will not be adopted. We address all three before a single model is deployed.
How Our LLM Delivery Process Solves These Problems
Every engagement follows a structured methodology built around your specific production requirements:
LLM readiness assessment covering data, compliance, and infrastructure
Use case scoping and model selection aligned to your specific requirements
Architecture design covering RAG, fine-tuning, or a hybrid approach
Integration build connecting the LLM to your existing systems and workflows
Governance and compliance layer implementation
Production deployment with monitoring, alerting, and cost controls
Post-launch optimization and model performance review cycles
