Purpose-built AI assistants and custom GPTs trained on your data, tone and internal processes.
A private assistant that already knows your playbooks, tone of voice and internal terminology — deployed where your team already works.
A focused technical and operational audit
A working prototype against real inputs
Human approval points and reliable fallbacks
Documentation your team can own
A measured rollout, not a big-bang launch
A private assistant trained on your SOPs that new hires can ask instead of interrupting a teammate
Drafting first-pass responses to RFPs and proposals in your existing tone and structure
Summarizing long internal threads and documents into a decision-ready brief
No. A custom GPT built inside a consumer chat tool is a good starting point, but a proper internal assistant is integrated with your actual data sources, has access controls, and is built for repeatable business use rather than one-off queries.
That's defined during scoping — some assistants are built for one team, others are rolled out company-wide with role-based access to what each person can see and do.
Every customer-facing system launches with defined guardrails and an escalation path, and runs in a supervised mode until we have real production data on its accuracy — not just evaluation-set numbers.
Almost always the former. We design the system, prompts and guardrails around an existing frontier model, since that's faster and more reliable for most business use cases. Custom fine-tuning only happens when the evidence says a general model can't hit the accuracy bar.
Tell us where work gets stuck. We’ll come back with a sharper view of what to automate, what to keep human, and what to leave alone.