Data Context
- Hard cold start because of fragmented data.
- Dashboard hell.
- Multiple answers to the same question.
- Experts become the support desk.
200 dashboards. Still no number anyone trusts.
Product Context
- Engineers wait on decisions because the product context lives with a few people.
- Experiments start from scratch because past learnings aren’t captured.
- We keep reopening decisions because the reasoning gets lost.
- Unclear acceptance criteria turn reviews into another requirements discussion.
- Work stalls at handoffs because decisions and next steps get lost.
We write code in hours and wait days for a decision.
Customer Context
- Delivery depends on the few engineers who know each customer.
- New engineers need the customer’s history explained before they can contribute.
- Customer constraints and commitments get lost in handoffs, causing rework.
- AI work needs constant correction because it lacks customer context.
If one engineer quits, we lose the account.
Teach it once. Everyone gets the skill.
PromptQL learns by being corrected. It starts from the context already scattered across Slack, docs, tickets, CRM, and warehouse tables, and on every task it shows its work — the sources it pulled, the assumptions it made.
Correct it once, or pull in the teammate who knows. That becomes shared context: a reusable skill (“exclude test accounts from revenue”), team knowledge, or a semantic-model change.
data
skills
tickets
crmThe fastest way to maintain context is to
stop asking people to maintain context.
Nobody wants to update the wiki. Everyone wants their work done.
PromptQL turns that to its advantage: the person who hits missing context has the reason to fix it, right in the flow. Their correction becomes cited, scoped knowledge the next session can use.
Context compounds from real work instead of decaying in a wiki nobody opens. Every task is a chance to teach the system.
Real wiki-contribution stats from our own use of PromptQL, across a team of 70.
Bootstraps shared context in 60 seconds
- Knowledge
- Skills
- Semantic layer
Suggests context updates as people work
- Capture new context
- Link context
- Prevent context rot
- Capture ambiguity and conflicts
Wikipedia-like operating model
- Easy for non-technical and technical users
- Citations to real work
- Revision history, audit trails & editorial controls
- Notifications on changes, page creations and deletions
AcmeCorp
AcmeCorp is a strategic enterprise account with shared finance, product, and customer-success coverage. Revenue for board reporting is sourced from netsuite.arr_monthly rather than analytics.revenue_legacy.[1]
Current renewal planning combines ARR health with churn signals from the Acme workspace, including inactive-user trends and recent risk signals surfaced in customer threads.[2]
Customer-facing pages can be shared directly with Acme collaborators, while internal playbooks and escalation notes stay scoped to PromptQL teams.
Govern with scopes
- Easily handle external, confidential & personal use-cases
- Scopes hold end to end: retrieval, creation & update
- Proven with external users (eg: customers), finance & HR teams
- Granular view & edit control
- Bulk operations to make rapid changes
The vibe shift
We made AI a team sport.
Fewer meetings. Fewer status updates. Faster decisions. The work & discussion about the work happen in the same place now — multiplayer AI threads.