Generative AI & LLM Solutions
Production GenAI on the right model.
Custom copilots, RAG grounding, fine-tuning and multimodal generation — engineered securely, and governed by default.
Custom copilots, RAG grounding, fine-tuning and multimodal generation — engineered securely, and governed by default.
Copilots, assistants and content engines — accurate, governed and cost-optimized for enterprise workloads.
Domain copilots for staff and customers, built on your data and workflows.
Accurate, cited answers retrieved from your own enterprise knowledge.
LoRA/QLoRA fine-tuning and small language models for cost and control.
Text, image and code generation within a single governed pipeline.
Route each request to the right model for cost, latency and quality.
Safety filters, evaluation and governance built in by default.
We ground generative models on your data with RAG and GraphRAG, so copilots return accurate, cited answers instead of hallucinations.
Copilots, content tools and search interfaces receive the request from staff or customers.
Prompt management, model routing, guardrails and semantic caching — one governed entry point for every request.
RAG and GraphRAG retrieve relevant, cited context from vector and graph stores before the model responds.
Frontier APIs, open models and fine-tuned SLMs — selected per request for the best cost, latency and quality balance.
Automated evals score every response before it reaches the user — with citations attached and audit trails kept.
Retrieval-grounded generation with guardrails and citation-backed responses.
A fixed-scope path to production — not an open-ended research project.
Use-case definition, data review and the right model strategy for cost and quality.
A grounded, evaluated proof of concept answering real requests from your data.
Hardened, monitored and handed over — with guardrails and evals running continuously.
A small model tuned on brand data, self-hosted with model routing — the full story is below. See the bank copilot and other engagements in our case studies.
High-volume content generation on frontier APIs was expensive and off-brand.
Fine-tuned a small language model (LoRA) on brand data, self-hosted with model routing to balance quality and cost.
On-brand content at a fraction of the cost, generated at scale.
Let's scope an architecture review and proof of concept for your use case.