LLM & GenAI Consulting
Production GenAI. Not another demo.
LLM integration, RAG pipelines, AI agents, and customer-facing copilots — engineered with the eval framework, cost ceilings, and safety guardrails that production systems actually need.
Where we add value
The hard part of GenAI is everything except the prompt.
Eval harness, retrieval quality, hallucination control, cost ceilings, prompt-injection hardening, and the integration with your actual data and surfaces. That's the engineering. We do that part.
45 → 7 min
First-draft authoring time per landing page
0
Engineer hours on SEO meta + JSON-LD per page
Same-day
Locale variants shipped, down from same-sprint
Services
Eight GenAI engagements we ship.
From a simple RAG over your docs to multi-agent customer copilots — the shape that fits gets picked on the first call.
RAG pipelines
Retrieval-augmented generation against your docs, SKUs, policies, support tickets. Hybrid (vector + lexical) retrieval, reranking, citation, eval harness.
AI agents + tool use
Multi-step agents that call tools, query data, post to Slack, write to your OMS. Built on the Anthropic / OpenAI agent SDKs with proper guardrails.
Customer-facing copilots
Shopping assistants, support copilots, in-app advisors. Brand-voice tuning, hallucination guardrails, cost ceilings per session.
Internal AI tools
Merchandising copilots, content generation, ops automation, sales-lead drafters. The unsexy AI that pays back faster than the customer-facing kind — lower risk, quicker to ship, adopted by the team that asked for it.
LLM evaluation framework
Eval harness, regression tests, golden-set scoring, drift detection. Buyers ask 'how do you prove the model works' — this is the answer.
Fine-tuning + adapter training
When prompting plateaus, we fine-tune. LoRA adapters, distillation, or full fine-tunes on Anthropic / OpenAI / Llama / Mistral.
Cost + latency engineering
Prompt caching, model routing (cheap-model-first), output streaming, batch APIs, semantic caching. Production AI economics.
Anti-prompt-injection + safety
Adversarial test suites, jailbreak hardening, output filtering, per-user rate limits. The trust layer most teams skip.
Where we built
CuberiQ, engineered AI-native from day one.
First-draft authoring time cut from ~45 min to ~7 min per landing page — SEO meta and JSON-LD generated with no engineer per page
Read the case studyHow an engagement starts
Three steps to a partnership
Intake call
30 minutes. We listen, you talk. No deck.
Diagnostic
We audit the surface, name the bottleneck, propose a path.
Kickoff
Senior engineer in your standup by week two.