What we deliver

• Model selection and sizing (open-source vs frontier)
• Prompt engineering playbooks and tooling
• Fine-tuning and adapters (LoRA/QLoRA) for domain language
• RAG and grounding pipelines with evaluators
• Safety, red-teaming, and guardrail configuration
• Latency/cost optimization and observability dashboards

Why it works

Pragmatic guardrails, evals, and CI for prompts and data let you ship LLM features with confidence before scaling usage.

Customer & employee assistants

Grounded, safe responses with real-time knowledge sources.

Content & knowledge workflows

Summarization, redaction, translation, and enrichment at scale.

Developer & ops copilots

Code review aids, runbook agents, and automated SOP drafting.

Data & analytics

SQL/text-to-DSL helpers with guardrails and lineage tracking.

Need the right LLM stack?

We balance model choice, safety, latency, and cost—then ship with evals and monitoring.

Discovery & data mapping

Map tasks, data sources, compliance, and latency/cost constraints.

Model & grounding design

Select base model, retrieval strategy, safety layers, and observability plan.

Fine-tuning & evals

Apply LoRA/QLoRA, build eval harnesses, and red-team critical workflows.

Delivery & integration

Wire APIs/SDKs, CI for prompts, and connect monitoring dashboards.

Launch & optimize

Roll out safely with rate limits, eval gates, and continuous cost/quality tuning.