A custom GPT is an AI assistant configured for one job — onboarding new staff, answering policy questions, drafting proposals in your format — with your documents as its knowledge and explicit rules on what it may and may not say. It is narrower than a general chatbot and considerably more reliable because of it.
Narrow beats general
Giving a whole company one general-purpose assistant produces mediocre results everywhere. Giving the sales team an assistant that knows the pricing rules, the objection library and the proposal format produces something they use daily. Specificity is what makes these tools stick.
The build is mostly knowledge curation and constraint design rather than model work: deciding what the assistant sees, what it must refuse, how it cites, and where it lives so people actually reach for it.
Process
How we deliver it
Process flow for Custom GPT solutions
01
Pick one job
Define the single task the assistant must do better than the status quo.
02
Curate knowledge
Assemble and clean the documents it will answer from.
03
Set constraints
Tone, refusal rules, citation requirements and escalation.
04
Deploy where they work
Web, Slack, Teams, WhatsApp or inside an existing tool.
05
Review and refine
Read real transcripts and fix the gaps they expose.
Deliverables
What you receive
A deployed assistant on your chosen channel
Curated, versioned knowledge base with an update process
Constraint and refusal configuration, documented
Usage analytics and transcript review workflow
Team training and an owner nominated to maintain it
Engagement shape
Three to six weeks per assistant. Additional assistants are faster once the knowledge pipeline exists.
Stack decisions follow the problem. This is where we usually start, not a fixed menu.
Frequently asked
Questions we get about this
Can it access our internal documents securely?
Yes, with access control carried through from your existing permissions, so a user only gets answers from documents they are already entitled to read. Getting this wrong is the most common serious failure in internal AI deployments, so we treat permissions as a design requirement rather than a later hardening step.
What stops it answering questions it should not?
Explicit refusal rules, a restricted knowledge scope, and logging. We test with deliberately out-of-scope and adversarial questions before launch, and review transcripts in the first weeks.
Talk it through before you commit
A discovery call is a working session on your constraint, not a sales pitch.
