We deliver generative AI development services — LLM-powered applications, retrieval-augmented generation (RAG) pipelines, agentic workflows, and fine-tuned domain models across OpenAI, Claude, Mistral, and Llama.
The 30-line demo is not the product. The product is the retrieval layer, the evaluation harness, the safety rails, the cost controls, the audit logs, the prompt versioning, and the failure UX. Without those, an LLM application erodes user trust on contact with reality.
Retrieval before generation where the answer should be grounded. Tools and agents only where they earn their seat. Evaluation harness with realistic test cases from day one. Cost telemetry and budget alarms. Safety rails — PII, prompt injection, jailbreaks — covered systematically.
Production LLM features with grounded answers, citation, evaluation in CI, cost telemetry, safety rails, and an audit trail. User trust survives use.
Retrieval-augmented generation with vector and hybrid search, re-ranking, citation, and answer-grounding evaluation.
Tool-using agents with planning, execution, and verification — used sparingly and with proper guardrails.
Domain fine-tuning of open-source models (Mistral, Llama) and closed-model fine-tuning where the platform supports it.
Offline and online evaluation — task-specific metrics, regression tests, and continuous A/B for prompt and model changes.
PII redaction, prompt-injection defense, jailbreak detection, content moderation, and refusal handling.
Per-feature cost dashboards, token budgets, caching, and latency alarms.