Wilab
Decision Intelligence Agent · Retail

Ask your retail data anything
Decide in minutes

Train the agent on your data, your business rules and your knowledge in 30 days.

Built for specialty chains · supermarkets · big-box
Book a Live Demo
Chat with your dataDemo · see it in action
> LIST_TABLES · SQL_QUERY ×6 · GET_HISTORICAL_KPIS
▮▮ Monthly sell-through — chain-wide
Current sell-through sits at 16.3%: 206.7K units sold against 1.06M in stock. The bottleneck isn't demand — it's inventory productivity: 742K surplus units.
Scenario comparison
scenarioSKUspot. salesproforma ST
A · Selective markdown on top 15% of surplus3,961$58.1M25.0%
B · Reactivate no-sales inventory3,052$8.1M17.5%
“By acting on just 15% of surplus inventory, proforma sell-through climbs toward 25% — with a potential upside of ~$58M MXN.”
Ask your data anything…
Real product conversation · anonymized data
Clients & data engineering heritage
See the problem it solves↓
“We used to wait for the weekly close just to react. Now our managers ask directly what categories need attention and make calls the same day.”
VP of Operations · 150-store retail chain
The problem

Thousands of SKUs per store: too much for any team or ERP/BI tool to keep up with

The problem isn't a lack of data or tools — it's that the combinations grow faster than any team can analyze. And every answer depends on an overloaded data team. Days of waiting for a single number.

Why not just use ChatGPT or Claude off the shelf?
  • ✕it doesn't know where to get the information and hallucinates numbers.
  • ✕it's insecure: you can't control which employee accesses what data.
  • ✕it becomes outdated in a few months and the knowledge stays with the person, not the company.
Validación In this post, Anthropic documents how their own team took their internal agent's accuracy from 21% to over 95% — with serious data engineering work behind it. It confirms that precision is custom to every use case and needs to be actively maintained — otherwise it degrades within months.

The model isn't the hard part: everything that needs to surround it is.

Business value

Real questions, answers with real numbers

Two real use cases from a footwear chain. Click each one to see the full agent response.

Inventory optimization
“What initial allocation maximizes turnover by product?”
  • ✓ Sell-through
  • ✓ Sales velocity
  • ✓ Utilization
  • ✓ Margin
Real answer — 5 scenarios compared▶ see full response
ST proforma 28.5% → 69.1% +$298K est. margin / 30 days
Commercial strategy
“Propose three segmentation strategies to maximize margin and market penetration.”
  • ✓ Volume
  • ✓ Profitability
  • ✓ Frequency
  • ✓ Growth
  • ✓ Channel
Real answer — 3 strategies with channel breakdown▶ see full response
DAMA: +$2.8M est. precio medio: +$1.15M est. excedentes: +$6.66M est.
Quantified recommendations to reduce stockouts, free up inventory, and speed up decisions. Questions that used to take days with your data team — answered in seconds.
SQLEvery answer includes the SQL that generated it — defensible in any board meeting.
The agent recommends and explains — you make the final call.
What makes us different

Why Wilab gets it done in 30 days — when others can't

The AI model is a commodity. The data engineering isn't.

Our edge isn't the model — it's turning your data, business rules, and domain knowledge into a reusable system. Since 2018 we've built real-time data pipelines for telecoms — where a wrong number costs millions.

Your data
Your business rules
Your team's domain knowledge
>
What we do
10 years of tools and processes
built to turn complex data into decisions
~1 mes>
Result
Decision Intelligence Agent configured for your business
Not a generic model: a reusable system that belongs to you. AI-provider agnostic.
6 paying clients live within the first 2 months

Try it with your own data

All we need is read access to your data sources and a technical contact during onboarding. No migrations. First use case live in ~30 days.

Start with the capacity you need. Scale as you grow

Pick a time for your demo Or reach out by email
You'll see it working live on real data You can talk to our current clients
Inventory optimization — full response
What initial allocation maximizes product turnover? Compare store vs. reserve assignment strategies and estimate impact on sell-through, velocity, utilization, and margin.
> LIST_TABLES · SQL_QUERY ×6
Store vs. reserve allocation — scenario comparison
scenariostore inv.reserve inv.est. sales 30d
Optimal turnover: 30d demand + 15% buffer287.8k757.7k198.7k
Store push: 90% store / 10% reserve940.9k104.5k198.5k
Current: existing distribution690.9k82.6k196.8k
Balanced: 70% / 30%731.8k313.6k196.3k
High reserve: 50% / 50%522.7k522.7k189.2k
Top categories by incremental margin — optimal vs. current
Operational priorities at the SKU–store level: which stores get inventory, which release to reserve, and which products to move first — exportable to CSV/XLSX to execute.
“The optimal strategy isn't filling stores — it's putting depth where velocity exists. With 30-day demand + a 15% buffer, estimated sell-through jumps from ~28.5% to ~69.1%, freeing up dead inventory and generating an estimated ~$298K in additional margin over 30 days.”
aggregated & anonymized data · footwear chain
Commercial strategy — full response
Propose three segmentation strategies to maximize margin and market penetration. Evaluate each by volume, profitability, frequency, growth potential, and channel.
> LIST_TABLES · SQL_QUERY ×8
Proposed segmentation scenarios
strategysegmentchannelincr. margin
1 · Double down on mid/high WOMEN's winnersSandal, Sneaker, Party & Flat · margin ~62.7%A + B+$2.8M
2 · Profitable penetration at mid-priceHigh-turn low/mid price · margin ~55.9%C+$1.15M
3 · Free up cash from surplus inventoryCoverage >180 days · 12% of stock, 15% disc.Outlet+$6.66M
Store channels: sales and margin — last 90 days
Recommendation: mixed strategy — winners for margin, mid-price for penetration, surplus to unlock cash. Built around channel architecture with surgical discount governance — targeted, not blanket.
“We have two clear engines: margin in mid/high WOMEN's and penetration at mid-price. The third lever is financial: turn surplus into cash without destroying margin.”
aggregated & anonymized data · footwear chain