Decide already.

PromptQL is an AI analyst that captures your team's shared context, connects to data wherever it lives and writes code to get you answers.

TRUSTED AT SCALE BY

Cisco
McD
Instacart
Swiggy
Lightspeed
Cisco
McD
Instacart
Swiggy
Lightspeed
Cisco
McD
Instacart
Swiggy
Lightspeed

THE PROBLEM

The knowledge your team needs to make decisions is scattered across everything.

PEOPLE'S HEADS

“Cost per order” means appeasement + refund only. Never blended. Nobody wrote that down.

OLD THREADS

Raj figured out partial-month data skews results six months ago. The insight died in Slack.

TRIBAL KNOWLEDGE

West Coast geo mix shifted after the Q3 contracts. Only ops knows.

Every AI tool hits the same wall: it doesn't know what your team knows. PromptQL fixes that.

THE SOLUTION

Shared context to scale accuracy

Someone asks a question. A colleague spots a nuance and jumps in. Together they guide PromptQL to the accurate answer and PromptQL remembers how.

Your collective knowledge finally has a home, so the next person (or AI agent) who asks doesn't have to figure it out again.

WHY TEAMS CHOOSE PROMPTQL

Built for the way real teams actually work

Zero data prep

No pipelines, no cleaning, no schemas. PromptQL connects to your databases, MCPs, APIs, spreadsheets, internal tools and figures out the shape of your data on its own. Just point it somewhere and ask.

Multiplayer learning

Anyone on your team can teach PromptQL. Correct an assumption, add a nuance, point out how things actually work here. It stitches individual knowledge into shared intelligence and never forgets.

Self-driving problem solver

PromptQL doesn't just query, it reasons. It writes code, runs it, checks the result and iterates until the answer makes sense. Like having an analyst who never gets stuck and always shows their work.

Security first

PromptQL connects to your sources without copying data. Every execution runs in a sandbox. Row and column-level access controls mean users only ever see what they're authorized to see. Private by default.

SEE IT IN ACTION

Watch shared context compound in real time

CA
Chloe A. (VP of Ops)
Chloe asks PromptQL a complex business question that requires deep business understanding.
we expected cost per order to drop 12 cents after the new fulfillment contracts. I'm not seeing it. Pull July 2025 vs Jan 2026 MTD. break down by partner mix and geo mix. appeasements and refunds only.
PromptQL
PromptQL
▸ 4 steps
Joining fulfillment data across 3 sources
Segmenting by partner mix and geo mix
7¢ showing up. 2¢ from geo mix shift. 3¢ unexplained vs the 12¢ claim.
CA
Chloe A. (VP of Ops)
@PromptQL for future reference: "cost per order" always means appeasement + refund cost, per order, segmented by partner and geo independently. Never blended.
✨ Wiki updated
Chloe taught PromptQL. Learning locked for everyone.
Cost per order — appeasement + refund cost, per order. Segmented by partner mix and geo mix independently. Never blended.
Taught by @chloe.a · VP of Operations
CA
Chloe A. (VP of Ops)
Chloe adds her team to collaborate on the analysis.
@sarah.m@james.l@raj.t See above. 7¢ of expected 12¢ showing up. What explains the 5¢?
@sarah.m, @james.l, @raj.t joined the thread
RT
Raj T. (PM, Fulfillment)
Raj brings more tribal knowledge into the analysis.
hmm, could be partial month skewing. @PromptQL compare partial vs full-month cost per order for Jan 2026 across the last 3 years.
PromptQL
PromptQL
▸ 3 steps
Analyzing partial month vs full month impact
Comparing return processing timing patterns
Jan partial-month (days 1–14) averages ~$0.02 higher than full-month across 2023–2025.
RT
Raj T. (PM, Fulfillment)
That's another 2¢. @PromptQL always normalize partial-month comparisons against the same calendar window, not full-month averages.
✨ Wiki updated
PromptQL absorbs tribal learning from another teammate.
Partial-month rule — compare against the same calendar window (days 1–14 vs days 1–14). Never compare partial to full-month averages.
Taught by @raj.t · Operations
PromptQL
PromptQL
▸ 2 steps
Recomputing with calendar-window normalization
11¢ of 12¢ now accounted for. Remaining 1¢ within noise.
CA
Chloe A. (VP of Ops)
@james.l update the board deck. 7¢ real, 5¢ explainable variance.
Thread resolved · 2 wiki updates · 4 participants. Next time anyone asks about cost-per-order, the context is already there.