Generative AI & Copilots
Custom copilots and generative-AI features embedded into your products and operations — grounded in your data, designed for adoption, and engineered to ship.
What this means for your business
A copilot is only useful if people trust it and use it. The hard part isn't calling an LLM — it's grounding it in your data, designing an experience that earns trust, and engineering the evaluation and guardrails that keep quality high in the real world. We take GenAI features from a promising demo to a dependable product capability your users rely on every day.
GenAI your users actually trust and use
Copilot Engineering
Domain assistants embedded in your product and workflows.
Prompt & Eval Pipelines
Systematic prompting with measurable quality.
RAG vs Fine-tuning
The right grounding strategy for accuracy and cost.
Multimodal & Content
Text, image, code and document generation.
AI UX Design
Interfaces that build trust and drive adoption.
Safety & Guardrails
Filtering, citation and graceful fallback by design.
From idea to shipped AI feature
Frame
Define the user, the job and the success metric.
Prototype
Prove value fast with a grounded prototype.
Harden
Add evaluations, guardrails and observability.
Ship
Launch, measure adoption and iterate.
Frequently asked questions
RAG or fine-tuning — which do we need?
Usually RAG first: it grounds answers in your current data with citations and is cheaper to maintain. Fine-tuning helps for tone, format or narrow tasks. We often combine both and recommend the right mix for your use case.
How do you stop the AI making things up?
We ground responses in your data, require citations, add evaluation suites that score faithfulness, and design graceful fallbacks for low-confidence answers — so the copilot says 'I'm not sure' instead of inventing.
Will our data be used to train public models?
No. We architect so your data stays yours — using enterprise endpoints with no-training guarantees, or models hosted in your own cloud, depending on your governance needs.
How quickly can we see something working?
Typically a grounded prototype in weeks. We frame a single high-value use case, prove value fast, then harden it for production.