LLM & AI Systems

This service helps when you are:

Shipping assistants or copilots that sound right but contradict docs, policies, or customer-specific facts.
Burning tokens on huge context windows and prompt tweaks without fixing retrieval, chunking, or data quality.
Unable to explain why an answer went wrong—or to regress retrieval changes with a real eval harness.
Blocked by risk, compliance, or security from expanding AI to contracts, SLAs, or internal runbooks without citations and audit trails.

RAG & grounding: Ingestion contracts, structure-aware chunking, hybrid retrieval, re-ranking, metadata and entitlement-aware partitions.
Evaluation & quality: Golden and adversarial sets, citation checks, guardrails (must-cite / must-refuse / must-escalate), pre-production gates.
Observability: Traces for retrieval, chunk IDs, scores, model versions—so incidents are debuggable, not philosophical debates about prompts.
Cost & latency discipline: Context budgets, caching where safe, SLOs for p95 latency and tokens—aligned with how you actually use Azure OpenAI or comparable stacks.
Roadmap & governance: Repeatable playbooks for new corpora and sensitivity tiers so “the second RAG” does not repeat the first failure mode.