Perspectives on knowledge, evidence, and AI.
Writing from the Jackson Hart team on what changes when document-heavy work is built on intelligence you can trust — and what doesn’t.
External material we’re reading on this.
Real reporting and research, grouped by theme. The strongest, most relevant piece in each set is first — drag, swipe, or use the arrows to see the rest.
The citation problem
Generic models answer confidently without traceable sources. In high-stakes work, that gap is the liability Dash is built to close.
Your data, your infrastructure
Putting sensitive corpora into public AI exposes confidential, privileged, and regulated material — and regulators are now acting on it.
The knowledge-base problem
Once a corpus is large enough, the bottleneck stops being the information and becomes the ability to find and connect what you already have.
Governable AI for regulated work
Regulators and professional bodies increasingly expect firms to evidence oversight and accountability for every AI output.
Why generic GenAI doesn’t land
Most generic pilots show no return. Serious outcomes need AI built for the job and grounded in the organisation’s own corpus.
When AI is wrong, it’s expensive
Real sanctions, refunds, and reputational damage from ungrounded AI — the consequences Dash’s source-traceable answers prevent.
The agent accountability gap
As autonomous agents enter audit, legal, and advisory workflows, buyers need AI where every action traces back to source evidence.
