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. We build the applications. We automate the manual work.

  1. [01]S&OPOne plan for the whole business.
  2. [02]Data AnalyticsNumbers everyone trusts.
  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 for the whole business.

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 trusts.

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

Every system, one source of truth.

We connect the systems you already have. Their data goes into one data warehouse. It updates through the day. Every app reads from it. 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.

  • 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

Short, practical notes on planning, data, automation and AI, from the work we do with growing brands.

(TOPICS)

(VIEW)

  1. [02]2 min read

    Why better operations create room for creativity

    The launch should take your attention. Chasing its stock should take less of it.

  2. [03]2 min read

    AI should give teams their time back

    Count the checking, correcting and chasing before calling something a time saving.

  3. [04]2 min read

    Turning disconnected data into a shared view

    Agree what the numbers mean before asking everyone to use the same report.

  4. [05]2 min read

    Building a demand planning rhythm

    A forecast becomes useful when the team knows when to change it and what to do next.

  5. [06]2 min read

    From manual workflow to useful automation

    Follow the work, simplify it, then decide which parts a system should handle.

  6. [07]2 min read

    Testing AI agents on everyday operations

    A practical trial for a small task, with a clear way to decide whether to keep it.

  7. [08]2 min read

    Comparing forecasting approaches for seasonal demand

    Give a new forecast a fair test against a simple starting point.

  8. [09]2 min read

    What makes a reporting tool useful?

    Try it in the meeting where someone has to make a decision.

  9. [10]2 min read

    What an AI study tells us about helping a team

    A reading note on Generative AI at Work, and a practical question for your own business.

  10. [11]2 min read

    Give AI the right reference material

    A reading note on retrieval, and why the source of an answer matters.

  11. [12]2 min read

    Test a forecast without giving it the future

    A reading note on time series evaluation, with a buying decision in mind.

  12. [13]4 min read

    32% less inventory, still growing

    When KPIs become the center of attention, it's easy to forget the strategy. And miss sales along the way.

  13. [14]4 min read

    A new brand of S&OP

    The method works. The problem is the time and work it takes to run it.

  14. [15]3 min read

    Agents inside integrations

    Most integrations only work when the data is perfect. An agent handles the part that is not.

  15. [16]3 min read

    5 ways we increase your gross margin

    Gross margin leaks in places nobody owns. These five turn up in almost every brand we work with.

  16. [17]3 min read

    How brands end up with four numbers for revenue

    We have all been to a meeting where no one agreed on the number. Our take.

  17. [18]3 min read

    Hire the function, not the person

    We love big companies with small teams. Here is how we make that happen.

  18. [19]3 min read

    How we build a single source of truth

    One place where the numbers agree, built in tools your own team can work in.

  19. [20]3 min read

    How we join your ops team

    We take real work, in your systems, on your cadence. Then we leave when it runs without us.

  20. [21]4 min read

    How we select 3PLs

    Rate card last, outcomes first.

  21. [22]3 min read

    How we work with enterprises

    You have the systems, the teams, and the governance. What we bring is speed, and things your people can own.

  22. [23]3 min read

    How we work with scale-ups

    Growth is stressing the planning, the systems, and the margins. This is the stage we built Telemyr for.

  23. [24]3 min read

    How we work with startups

    The habits you build at ten people decide what scaling costs you later. Getting them right early is cheap.

  24. [25]4 min read

    Modernising B2B operations

    B2B is the most predictable part of most brands. It is usually the least automated.

  25. [26]3 min read

    Modernising business controlling

    Controllers spend most of the month explaining what already happened. Very little of that work still needs a person.

  26. [27]3 min read

    Our approach

    Experienced operators, working with AI, embedded in your business.

  27. [28]3 min read

    Scenario planning without a two week wait

    Most scenario work arrives after the decision. By then it is a record of what you could have done.

  28. [29]3 min read

    Stop pushing AI tools to your team. Raise the ambition instead.

    Rollouts create users. Ambition creates inventors. Set targets the old way of working cannot reach and let the team find the way.

  29. [30]3 min read

    The 24hr rule

    Imagine no process in operations is allowed to take more than one working day. What would you have to change to get there?

  30. [31]3 min read

    The battle of the inventory

    Ecommerce and wholesale fight over the same stock in most brands. The allocation policy gets blamed. The buy is usually the problem.

  31. [32]3 min read

    What the first month looks like

    An engagement starts with a conversation rather than a proposal. Here is the sequence from that conversation to something working.

  32. [33]3 min read

    What we tell CEOs who ask about AI strategy

    There is no AI strategy. There is a business strategy, and better tools to execute it with.

  33. [34]3 min read

    When to build and when to buy

    The old rule was to buy anything that was not your core advantage. Building got cheap enough that the rule needs revisiting.

  34. [35]2 min read

    Where AI projects go wrong

    Most AI projects in operations fail for the same three reasons. Almost none of them are technical.

  35. [36]3 min read

    Where is the profit?

    Most brands have now spent money on AI and cannot point to the result. The gap is rarely the technology.

  36. [37]3 min read

    Who do we hire these days?

    Teams are getting smaller and flatter. The people worth hiring are the ones who can work close to the task again.

  37. [38]3 min read

    Why dashboards keep failing

    Everyone has bought one. Almost nobody uses it. The same four reasons show up in nearly every business we walk into.

  38. [39]3 min read

    Why we don't sell software

    The cost of building software falls every year. We think the next generation of services sells outcomes rather than tools.

  39. [40]3 min read

    Why we started Telemyr

    We kept meeting the same problems inside consumer brands and wanted to solve them differently.

  40. [41]3 min read

    Your best people are doing your worst work

    The person you would least like to lose is probably spending most of the week on assembly. That is a retention problem as much as a productivity one.

  41. [01]2 min read

    Growing a brand without growing the overhead

    Before the next hire, look at the work that keeps coming back.