Solya Catalyst

We embed with your teams.
We build what your edge requires.

Catalyst is Solya’s dedicated team: forward-deployed engineers and retail strategists on the ground, designing and shipping the AI decision systems the standard platform doesn’t cover.

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Forward-deployed engineers

Retail strategists

Fixed price per project

When Catalyst steps in

Catalyst steps in when standard workflows aren’t enough.

The platform covers the decisions every retailer shares. Catalyst builds the ones that are yours alone: the systems behind a durable competitive edge.

Solya Platform

Covered out of the box

  • Buying, restock, exchange, markdown & rebalancing plans
  • Analytics, cards & dashboards
  • AI Analyst Agent
  • Native connectors for the retail stack

Included in your license.

Solya Catalyst

Built for your edge

  • Signals & business rules nobody else has
  • Bridges to legacy and in-house systems
  • Workflows that cross teams and tools
  • Dedicated buying, planning or store apps
  • External data: weather, traffic, competitors

Fixed price per project.

The method

Identify. Design. Build.

Three steps, from the first workshop to a system in production. You know the scope, the timeline and the price before anything gets built.

01

Identify

Spot what’s worth building.

What we look for

  • Manual processes
  • Complex decisions
  • Operational frictions
  • Unexploited data sources
  • Performance levers
02

Design

Spec the system. Commit the price.

What we specify

  • Business workflows
  • Decision logic
  • Business rules
  • AI agents
  • Interfaces & applications
03

Build

Ship it. Hand over the keys.

What we ship

  • Automated workflows
  • Specialized AI agents
  • Business applications
  • Connectors & integrations
  • Custom developments
Missions

What Catalyst builds.

Four typical missions, told end to end: the problem, what we plug in, the workflow step by step, and what lands in your teams’ hands.

FinanceFashion network · 60 stores
See the full mission

Cash trapped in stock, finally visible

System built → Working-capital cockpit · Workflow + AI agent

−15%

target working capital tied up in stock

D+1

cash visibility, down from month-end

1

shared truth between finance and merchandising

The problem

The CFO discovered the cash impact of buying decisions at month-end, in an Excel rebuilt by hand from SAP exports. By then the orders were signed, the cash committed. The overdraft line was doing the steering.

What we plug in

  • Bank balances (EBICS feeds)
  • Supplier payment schedule (SAP)
  • Committed purchase orders (OMS)

The workflow, step by step

  1. 1

    Every night, the system pulls bank balances, the SAP payment schedule and open orders from the OMS.

  2. 2

    It projects the cash position week by week over 13 weeks, stock included.

  3. 3

    Every pending purchase order is priced in cash impact, at its actual payment date.

  4. 4

    If the projection crosses the safety threshold, the agent alerts finance in Teams, with the five orders that weigh most.

  5. 5

    Finance arbitrates in the cockpit: delay, split or approve. The decision flows back into the buying plan.

Business impact

€500K

of cash freed: the −15% target on ≈€3.5M of inventory at cost across 60 stores

What your teams get

  • A 13-week cash projection, refreshed nightly
  • A cash price tag on every pending order
  • Alerts before the threshold is crossed, not after
E-commerce & marketplacesSports retailer · 80 stores + 3 marketplaces
See the full mission

One stock, every channel

System built → Cross-channel arbitration engine · Decision workflow

+20%

target online availability

0

overselling incidents

3

marketplaces run as one stock pool

The problem

Amazon, Zalando and the Shopify store were each run from their own back office, fed from the same warehouse by copy-paste. Out of stock online while the same SKUs sat in 30 stores. Marketplace fees quietly ate the margin.

What we plug in

  • Amazon Seller Central & Zalando (fees, returns)
  • Shopify (online sales & stock)
  • Carrier rate cards (Excel)

The workflow, step by step

  1. 1

    Every hour, the system reads sales, returns and fees from each marketplace, plus Shopify and warehouse stock.

  2. 2

    It computes net margin per SKU and per channel, after commission, shipping and expected returns.

  3. 3

    It decides what to expose where: full depth on the profitable channel, capped where margin dies.

  4. 4

    Store stock above the sales-floor minimum becomes sellable online, automatically.

  5. 5

    Before any channel runs dry, exposure is rebalanced: no overselling, no manual lockouts.

Business impact

+€200K

of online revenue per year: the +20% availability target on ≈€2M of online business

What your teams get

  • A daily exposure plan per channel and SKU
  • Net margin per channel, visible at last
  • Store stock turned into online availability
MarketingFootwear network · 45 stores
See the full mission

Marketing budgets that follow demand

System built → Local budget allocation engine · Workflow + AI agent

−25%

target wasted ad spend

7 d

budget reallocation cycle, down from 90

45

store-level plans instead of one national

The problem

Media budget was set nationally, once a quarter, on Meta and Google Ads. A store in stockout got the same advertising pressure as one drowning in unsold sizes. Nobody linked the spend to what each store could sell.

What we plug in

  • Meta & Google Ads (spend, campaigns, geo)
  • Store footfall counters
  • Local weather (public API)

The workflow, step by step

  1. 1

    Each week, the system crosses ad spend per catchment area with store-level stock, sales and footfall.

  2. 2

    It scores every store: can the demand we’re paying for be served?

  3. 3

    Budget shifts from stocked-out catchments to overstocked ones, within rules marketing sets.

  4. 4

    Campaign budgets update in Meta and Google Ads through their APIs, no agency round-trip.

  5. 5

    A weekly report shows spend versus sellable stock, store by store.

Business impact

€100K

of media spend redirected yearly: a quarter of a ≈€400K local advertising budget

What your teams get

  • Store-level budgets pushed to the ad platforms
  • Weekly reallocation, automatic
  • A spend-versus-stock report, store by store
OperationsHome & garden network · 70 stores
See the full mission

The painful Monday workflows, automated

System built → Supplier follow-up agent · AI agent

−90%

manual follow-ups

2 d

earlier discrepancy detection

100%

orders tracked end to end

The problem

Every purchase order meant chasing the supplier by email for a confirmation, then checking the delivery against the order line by line. Two coordinators spent their mornings in Outlook. Discrepancies surfaced at the warehouse dock.

What we plug in

  • Supplier emails (Outlook inbox)
  • EDI order confirmations
  • Carrier tracking (GLS & DPD)

The workflow, step by step

  1. 1

    The agent reads incoming supplier emails and EDI messages, and matches each one to its purchase order.

  2. 2

    No confirmation after 48 hours? It chases the supplier itself: your templates, your tone.

  3. 3

    Confirmed dates and quantities are checked against the order, line by line.

  4. 4

    Carrier tracking updates the expected delivery date in real time.

  5. 5

    Clean orders flow through untouched. Only exceptions land in a human queue, pre-qualified.

Business impact

1500 h

of coordination given back yearly: two coordinators, every morning, all year long

What your teams get

  • Confirmations chased without a human
  • Discrepancies caught before the truck arrives
  • An exceptions queue instead of an inbox

Illustrative scenarios: figures are project targets, not published client results.

What we ship

Systems in production. Not slides.

Every Catalyst engagement ends with something running: used by your teams, measured on your KPIs.

Business workflows

Multi-step decision workflows specific to your organization: approvals, exceptions, hand-offs included.

Specialized AI agents

Agents trained on your decision logic, operating on your data, accountable to your rules.

Dedicated applications

Purpose-built interfaces for your buying, planning or store teams when generic screens slow them down.

Connectors & integrations

Bridges to legacy ERPs, in-house tools and partner systems the standard catalog doesn’t reach.

New data sources

Weather, traffic, competitor pricing, market signals, wired into your decision systems where they pay off.

Team enablement

Your teams learn to run, tune and extend the systems we build. Autonomy is part of the deliverable.

Why it works

Not consulting. Engineering, embedded.

Catalyst exists because recommendations don’t move margin. Systems do.

Built on the platform, not beside it

Everything Catalyst ships runs on your Solya environment. Data already connected, deployments measured in weeks, and no orphan tool to maintain.

Engineers in your operations, not slides

Forward-deployed engineers work with your teams on real decisions. The deliverable is a system in production, not a recommendation deck.

Per project, not per day

Every engagement is scoped, priced and committed before we start. You buy an outcome, not time.

FAQ

What retailers ask about Catalyst.

How is Catalyst different from a consulting firm?

Consultants leave recommendations. Catalyst leaves systems: workflows, agents and applications running in production on your Solya environment, used by your teams every day. The engagement ends when the system works, not when the deck is delivered.

Do we need the Solya platform to work with Catalyst?

Yes. Catalyst builds on top of the platform: that’s what makes it fast. Your data is already connected, the decision infrastructure already runs, so projects start from a working foundation instead of a blank page.

How is it priced?

Per project. Each engagement is scoped with you first (objective, system, timeline), then committed at a fixed price. No day rates, no drift.

Who does the work?

Solya’s own forward-deployed engineers and retail strategists, embedded with your teams. No subcontracting, and the founding team stays close to every engagement.

What does a typical project look like?

A working-capital cockpit for finance. A stock arbitration engine across marketplaces. An agent that chases supplier confirmations so your teams don’t. If your teams handle a decision by hand today, it’s a candidate. Most projects ship in weeks, not quarters.

Who owns and maintains what’s built?

Everything ships into your Solya environment, documented, and is maintained as part of your license. No shadow IT, no orphan codebase.

The playbook is shared.
Your edge is built.

Tell us where your network loses margin to manual decisions. We’ll tell you what we’d build, how long it takes, and what it costs.

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