AI and Ops studio helping brands grow while staying small

Telemyr

Founded by Rafael , a CTO and COO whose last venture took investment from of an LVMH , backed venture, and Fermin , an AI scientist with a background in robotics. We offer brands a better way to scale operations than hiring.

We use the latest tech and processes to scale operations profitably, so you can focus on product, brand and community.

WHERE WE COME FROM

What we can do for you

Five services on one source of truth. We connect your systems, build the apps your team runs on, and automate the work in between.

  1. [01]S&OPOne plan the whole business works from.
  2. [02]Data AnalyticsNumbers everyone agrees on.
  3. [03]AutomationLess manual work, fewer mistakes.
  4. [04]AI EnablementAI that gives your team time back.
  5. [05]LogisticsStock in the right place, at the right cost.

*Figures on mockups are dummy data for demo purposes

01 / 05

S&OP

One plan the whole business works from.

We set up a planning rhythm your team can keep. Sales, supply and buying work from the same forecast. When something changes, everyone knows what to do next.

S&OP weekly meeting: net revenue month to date 6.2% ahead of plan, revenue by channel, units against plan pacing, an AI summary and the products with the least stock cover.
S&OP purchase orders: a 3,000 unit order for the Organic Cotton Tee in transit, split into two deliveries, above the list of other open orders.

*Figures on mockups are dummy data for demo purposes

  • Demand Planning

    A forecast per product, colour and size, built from sales and the season plan.

  • Supply Planning

    A stock roll-forward per SKU, with cover targets, lead times and minimum orders.

  • Purchasing

    Suggested buys become purchase orders, tracked from the supplier to the warehouse.

  • Collection planning

    Season targets by category, product and colour, approved by the team.

  • Weekly meeting

    Pacing, projections and stock risk on one page, with an AI summary.

  • Scenarios

    Try a new market or warehouse without touching the live plan.

02 / 05

Data Analytics

Numbers everyone agrees on.

We bring your data into one place, in tools your team already uses. Everyone sees the same revenue, stock and margin. Reports are built for the meetings where decisions get made.

Point at a system to follow its data. Point at an app to see what feeds it.

Every system, one source of truth.

We connect the systems you already run on to one data warehouse, refreshed through the day. Every app reads from it, so a number means the same thing everywhere.

  • Online store

    Orders, products, variants, customers, refunds and abandoned checkouts.

    Feeds every app

  • Paid search

    Spend, clicks and conversions, and paid search in the channel reports.

    Feeds Analytics and the Assistant

  • Paid social

    Spend, return on ad spend and placements, by campaign, market and product.

    Feeds Analytics and the Assistant

  • Web analytics

    Traffic, sources, markets and the conversion funnel.

    Feeds Analytics

  • Warehouse

    Stock by location, with incoming, reserved and damaged units.

    Feeds every app

  • Wholesale

    Wholesale orders, the draft order pipeline and the order book.

    Feeds S&OP, Analytics and Retailer ATS

  • Budget

    The budget and forecast workbook from the finance team.

    Feeds S&OP and Analytics

  • Accounting

    Invoices and costs, so margins use the real numbers.

    Feeds Analytics and the Assistant
Analytics trending products, with the Organic Cotton Tee's own page: 1,284 units in 30 days, 10.4 weeks of cover, size mix, conversion and its paid social ads.
Analytics marketing: paid social spend, return on ad spend, cost per acquisition, weekly spend against attributed revenue and the top paid search campaigns.

*Figures on mockups are dummy data for demo purposes

  • Building a single source of truth

    Every system lands in one warehouse. Every figure is defined once, in writing.

  • Analytics and business insights

    Revenue, profit, markets, products and marketing, for every team.

  • Sales and commercial support

    Accounts, pipeline and the order book next to sell-through and stock.

  • Profit and loss

    Daily, weekly or monthly P&L against the budget.

  • Marketing

    Ad spend and returns, linked to the products they sell.

  • Find anything

    Jump to any page, metric, market or product from the command bar.

03 / 05

Automation

Less manual work, fewer mistakes.

We follow the work first, then simplify it. Systems take the repetitive steps. Your people keep the decisions.

Automations: the morning sync of five systems, one retried after a rate limit, with every check passed.
Automations: a demand forecasting model scored against actuals and retrained, with its accuracy and versions.

*Figures on mockups are dummy data for demo purposes

  • System integration

    Scheduled syncs between your store, warehouse, wholesale and finance, retried and checked.

  • AI powered process automation

    Agents carry out routine work, like splitting an order across two warehouses.

  • Machine Learning forecasting models

    Models forecast demand, are scored against actuals every month and retrained.

  • Approvals

    People approve what matters in one click, with the reasons shown.

  • Monitoring

    Every run is logged. Problems are flagged before anyone notices.

  • Time saved

    Hours saved per team, measured every week.

04 / 05

AI Enablement

AI that gives your team time back.

We start with a real task, not a tool. We test AI on everyday work and measure the time it saves. What works stays, in your accounts, for your team to run.

Assistant: the Monday brief written from the dashboards, each sentence checked against the chart.
Assistant: hours saved each week, tasks done with AI and weekly active users, by team.

*Figures on mockups are dummy data for demo purposes

  • LLM Deployment

    Assistants in your own cloud, on your data, with answers that cite their sources.

  • Workflow automations

    Drafts for customer emails and product copy. A person chooses what goes out.

  • Business Reporting

    The Monday brief writes itself, and every line is checked against the chart.

  • Permissions

    People only get answers from what they can already see.

  • Measured

    Time saved per task and per team, reported every week.

  • Your accounts

    Models, data and logs stay in your cloud and are never used for training.

05 / 05

Logistics

Stock in the right place, at the right cost.

We choose warehouse partners on outcomes, not rate cards. We find where margin leaks between order and delivery. Then we automate how stock is planned and moved.

Logistics: a carrier invoice checked line by line against the agreement, with two lines held back.
Logistics: the cost to serve an average order, from product cost to contribution margin, before and after the actions.

*Figures on mockups are dummy data for demo purposes

  • 3PL selection and network design

    Warehouses and carriers chosen on cost, speed and service, zone by zone.

  • Partner management

    A scorecard for every partner, and every invoice checked against the agreement.

  • Margin improvement

    The cost to serve from order to delivery, and the actions that close the gaps.

  • Inventory management + automation

    Stock that runs short raises its own purchase order.

  • Partner portal

    Retail partners see what is free to sell, in their own market and currency.

  • Shipments

    Small shipments to one receiver merged, and the right carrier on every lane.

Start with one service. The rest connect to the same data.

Talk to us

5 reasons brands love to work with us

  • 1

    Accountable

    We answer for the numbers the way an employee would. When something is wrong, you call us, and we fix it.

  • 2

    The cost

    Several disciplines, one preassembled, high-performance team. Combining operations and planning best practices with AI, ML, and software development.

  • 3

    Ownership

    In your cloud, on your accounts, with low-cost tools. Whether we’re around or not, it’s yours to keep and yours to run.

  • 4

    AI native

    We are at the frontier of AI capabilities and use cases, with partnerships with major labs like Anthropic and experience in R&D with Google DeepMind.

  • 5

    Operators

    Experience earned inside consumer brands, in the chairs you’re sitting in now. When we take a project, we consider ourselves part of the brand and fully invested in your business goals.

Case studies

Apps and data, built in partnership with growing brands.

Great sportswear, great operations.

Three apps on one source of truth.

Retailer ATS for retail partners, S&OP for planners and Analytics for the whole team, fed by eight data sources from Shopify to the accounts.

Client
Hastko, France
Industry
Women’s sportswear and underwear
What we built
Retailer ATS, S&OP, Analytics
Data
8 sources, one warehouse
Read the case study

  • Brain Dead

    Los Angeles, USA

  • C'est Normal

    Stockholm, Sweden

  • Saela

    USA

  • Spunge

    Los Angeles, USA

  • Tom Wood

    Oslo, Norway

  • Various

    USA

  • Vestre

    Oslo, Norway

  • Viking

    Oslo, Norway

Notes from the work

(ARTICLES)

  1. [01]Growing a brand without growing the overhead
  2. [02]Why better operations create room for creativity
  3. [03]AI should give teams their time back
  4. [04]Turning disconnected data into a shared view
  5. [05]Building a demand planning rhythm
  6. [06]From manual workflow to useful automation
  7. [07]Testing AI agents on everyday operations
  8. [08]Comparing forecasting approaches for seasonal demand
  9. [09]What makes a reporting tool useful?
  10. [10]What an AI study tells us about helping a team
  11. [11]Give AI the right reference material
  12. [12]Test a forecast without giving it the future
  13. [13]32% less inventory, still growing
  14. [14]A new brand of S&OP
  15. [15]Agents inside integrations
  16. [16]5 ways we increase your gross margin
  17. [17]How brands end up with four numbers for revenue
  18. [18]Hire the function, not the person
  19. [19]How we build a single source of truth
  20. [20]How we join your ops team
  21. [21]How we select 3PLs
  22. [22]How we work with enterprises
  23. [23]How we work with scale-ups
  24. [24]How we work with startups
  25. [25]Modernising B2B operations
  26. [26]Modernising business controlling
  27. [27]Our approach
  28. [28]Scenario planning without a two week wait
  29. [29]Stop pushing AI tools to your team. Raise the ambition instead.
  30. [30]The 24hr rule
  31. [31]The battle of the inventory
  32. [32]What the first month looks like
  33. [33]What we tell CEOs who ask about AI strategy
  34. [34]When to build and when to buy
  35. [35]Where AI projects go wrong
  36. [36]Where is the profit?
  37. [37]Who do we hire these days?
  38. [38]Why dashboards keep failing
  39. [39]Why we don't sell software
  40. [40]Why we started Telemyr
  41. [41]Your best people are doing your worst work