Every merchant question, answered against live data
A specialty sports retailer plugged Solya into Slack so the team could ask anything — and get back a sourced answer, not another report request.
Outcome
< 90s avg. response
Network
12 stores · multi-brand
Measured outcomes
< 90s
Average question-to-answer time
−80%
Analytics team request volume
100%
Answers reproducible — same definition, same numbers
What's wired up
Systems connected
Data warehouse
Single source of truth
POS feed
Sales · live API
Slack
Threads · sourced answers
Before · After
Before
Analytics queue, days of wait
Operations, marketing and store leads filed dozens of ad-hoc data requests every week. Simple questions like "how did this SKU perform last weekend?" were taking days, and the analytics team had become the bottleneck.
After
Ask in Slack, answered in 90 seconds
Anyone asks a question in natural language. Solya parses it against the team's semantic layer (pinned definitions of margin, sell-through, units), runs the query, and posts back the answer with the source SQL and a confidence note.
The challenge
Operations, marketing and store leads were filing dozens of ad-hoc data requests every week. The analytics team became a bottleneck: simple questions ("how did this SKU perform last weekend?") were taking days to answer.
What we changed
Solya was connected to the data warehouse, POS feed and the team's existing semantic layer via the data layer. A Slack app let anyone ask a question in natural language and get an answer back with the underlying query, the rows it pulled, and a confidence note.
How decisions get made
Each question is parsed against the semantic layer first — definitions of margin, sell-through, units, returns are pinned so two people asking the same question get the same answer. Anything ambiguous gets a clarifying question instead of a wrong number.
Where it lands
Answers stay inside Slack threads with the source query attached, so anyone can audit them later. The analytics team's queue dropped from dozens to a handful — and those that remain are real strategic questions, not lookups.
What changed
- Average question-to-answer time under 90 seconds
- Analytics team requests reduced by ~80%
- Every answer is reproducible — same definition, same query, same numbers
Related: see how continuous replenishment and AI agents on markdown and transfers push the same model from Q&A into actual execution.
More use cases
A seasonal buy plan, signed off in one review
A 14-store apparel buyer had to cut next season's open-to-buy by 12% — and built a sharper plan on two years of live sell-through instead of intuition.
Move stock before markdown is the only option left
A 9-store streetwear brand turned twice-a-season panic transfers into a calm weekly lever — moving stock six weeks before markdown was the only answer left.
Allocation that finally knows the network
A 14-store apparel network still split each season with a rule written when it had 8 stores. Solya re-allocated on what every store had actually become.
From the blog
Anaplan for retail inventory: what it does for stock
Evaluating Anaplan for stock? It plans brilliantly and stops at the plan. Here's what Anaplan does for inventory — and the layer that picks up where it ends.
Blue Yonder for retail inventory: fit and shape
Blue Yonder automates stock decisions at enterprise scale. The real buyer question isn't whether it's capable — it's whether the suite is your shape.
RELEX for retail inventory: fit and shape
RELEX is a category-leading planning platform that runs stock at scale. The real buyer question is whether a unified platform is your shape.