- Element
- 02
- Period
- 2
- Group
- Noble Gases
AI/LLM Integration and Strategy
If it only works in the demo, it isn't working.
We build AI systems that actually run in production — not proof-of-concepts that impress in a presentation and fall apart at the edge cases you didn't think to test.
RAG architecture designed for your actual data distribution, agentic pipelines with real error budgets, hallucination mitigation that goes beyond temperature tuning — the real engineering behind AI systems that stay reliable when the demo conditions disappear. In healthcare and regulated environments, that means data handling designed to support HIPAA technical safeguard requirements, explainability documentation, and eval frameworks your compliance team can use as supporting evidence for reliability reviews.
We also handle the operational side: model routing, cost optimization, context window management for production workloads, and the observability infrastructure that tells you when your AI system has quietly started going wrong.
Engagement Process
-
Use Case and Feasibility Scoping
Most AI projects fail because they start with a technology and work backward to a use case. We start with the specific problem: what does it need to do, what does failure look like, what are the latency and cost constraints, and what data is actually available in production conditions. This scoping determines whether an LLM is the right tool at all — and if so, which approach has a realistic path to production.
-
Data and Infrastructure Assessment
RAG architectures are only as good as the data they retrieve from. We assess your existing data for quality, coverage, and consistency — the things that don't show up until you're running real queries at production scale. In regulated environments, we map PHI handling requirements, data residency constraints, and the audit logging your compliance team will require before anything goes near a production record.
-
System Design and Proof of Concept
We design the full system before building any of it: retrieval pipeline, context window strategy, prompt architecture, model routing logic, error handling, and fallback paths. The proof of concept is built to test the specific failure modes most likely to occur in your data distribution — not the happy-path demo that every AI vendor leads with.
-
Production Hardening and Observability
Getting a model to work in a notebook is 10% of the problem. We build the eval framework that proves reliability to your compliance team, the observability infrastructure that tells you when your AI system has quietly started degrading, and the cost modeling that ensures your unit economics survive actual usage patterns.
Outcomes
- Technical design and roadmap for a production-ready AI system, with documented error budgets and identified failure modes — full build delivery available as a follow-on engagement
- Eval framework suitable for demonstrating reliability to compliance and executive stakeholders
- Systems designed to support your HIPAA compliance program — data handling pipelines, audit logging, and access controls documented for compliance review. We design for compliance requirements; we do not provide legal or certification services.
- Model routing and cost optimization strategy validated against production workload projections
- Observability infrastructure — latency, accuracy, cost, and drift monitoring
- Documented context window strategy for long-form and multi-turn applications
Right for You If
- Companies with a compelling AI use case and no clear path from demo to production
- Healthcare and regulated-industry teams navigating PHI handling for LLM inputs
- Engineering teams that have built AI features that work in testing but degrade in production
- Executives who need to answer compliance team questions about AI reliability and explainability
- Teams whose AI cost model doesn't survive realistic usage projections
- Organizations evaluating build vs. buy for AI features and needing an independent technical view
Scope and Pricing
AI strategy engagements typically begin with a 1-week scoping assessment that produces a clear build recommendation, cost model, and technical risk assessment. Proof-of-concept engagements run 2–4 weeks. Production hardening and ongoing optimization are available as a retainer. Initial scoping assessments are priced as a fixed-scope engagement. Contact us for a scoping call.
Compounds Well With
These services are frequently engaged together for maximum yield.