New · AI Assistant

    Ask your inventory anything. Get a decision back.

    You can now control the system with a conversational AI Assistant and query your data in plain language, across Forecasting, Buy Plan and Replenishment. Ask about a store, a category, a SKU or a budget, get a recommendation from your own data, and let the assistant carry it out. And with MCP support, your own AI tools can connect too.

    • Control the system by conversationCreate a push, draft a purchase order or change the plan just by asking. The assistant prepares the action and carries it out once you confirm.
    • Query your data in plain languageAsk about any store, category, SKU or month the way you'd ask a colleague. No reports to build, no filters to set.
    • See the reasoningEvery answer comes with the numbers behind it, and you can open the full breakdown before deciding.
    MCP supportedModel Context Protocolbuffers.ai supports the Model Context Protocol (MCP), so the AI assistants and agents your team already uses can query your data and work with the system too.

    See it in each module

    AI Assistant
    • Question: The inventory in store 005 is low under category Teenagers. Answer: Based on my analysis, I suggest creating a push of 300 units to store 005. Would you like me to create it, or do you want to see the breakdown first?
    • Question: Why did the December forecast for SKU 4471 jump 40%? Answer: Last December's promotion repeats this year, and LightGBM, this SKU's champion with a 14% backtest WAPE, carries that lift. Do you want to see the backtest comparison, or forecast December without the promotion?
    • Question: Q2 plan for Women's Outerwear is 8% over budget. Answer: I can bring it back inside budget by trimming April by $42K and moving $28K from May into June, when demand peaks. Should I apply it, or do you want the month-by-month view first?
    • Question: Why didn't store 012 get the new denim collection this week? Answer: The warehouse held 1,140 units against 1,600 units of store demand, so the stock went to the 18 stores with the highest sales potential. Store 012 ranked 23rd. Should I reserve part of Thursday's delivery for it?
    • Question: How much kids' footwear should we order from the supplier next month? Answer: Forecast demand is 4,200 pairs. After stock on hand and open purchase orders, and rounded to the supplier's MOQ, I recommend ordering 2,850 pairs. Shall I draft the PO, or show the split by size first?
    • Question: How much budget is left for Accessories in March? Answer: $84K of the $310K March budget is still open. Sales are running 6% ahead of plan, so I'd commit $50K now and hold the rest for April. Want me to update the plan?
    • Question: Which stores will run out of sunscreen before the weekend? Answer: Four stores hit zero by Friday: 003, 007, 015 and 021. Tomorrow's orders already cover three of them; store 015 needs 60 more units. Want me to add them to tomorrow's order?
    • Question: Which forecasts should I trust least right now? Answer: 12 SKUs have a WAPE above 35%, mostly new items with under 8 weeks of sales. I can switch them to a Cold-Start Forecast based on similar products. Want me to do that?
    • Question: What if we cut next season's budget by 10%? Answer: The 12-month plan rebalances to $3.6M. Footwear and Home absorb most of the cut, and the November peak stays fully funded. Shall I save it as a scenario, or compare it with the current plan?
    Example conversations across all three modules
    / Pricing

    Know your cost in under 30 seconds

    Enter your store count and SKU catalog size for an instant monthly and yearly estimate — no forms, no sales call required.

    Pricing Calculator
    Platform Overview

    What is buffers.ai?

    The Platform

    buffers.ai is a real-time AI platform that automates demand forecasting, purchase planning, and inventory replenishment for retail and manufacturing enterprises, built from three focused modules: Forecasting for AI demand prediction, Replenishment for AI inventory replenishment, and Buy Plan for budget-driven, periodic purchase planning. It is used by global retail and manufacturing enterprises including H&M, Bath & Body Works, Victoria's Secret, P&G, and Toshiba.

    Why It Matters

    Inaccurate demand forecasts and slow replenishment cause two costly outcomes at once: stockouts that lose sales, and excess inventory that ties up working capital. buffers.aiaddresses both by improving forecast accuracy per SKU and automating the resulting purchase and replenishment decisions, so safety stock reflects real, current demand rather than a static rule.

    How It Works

    Forecasting backtests five algorithms (TimesFM, LightGBM, Prophet, Exponential Smoothing, and Moving Average) over a rolling six-month window, selects the closest-fitting champion model per SKU, and converts that forecast into a supply plan accounting for lead time, safety stock, minimum order quantities, budget, and open commitments. Replenishment then manages day-to-day store and channel inventory: restocking, assortment, new product introduction, promotional push, omnichannel balancing, and KPI reporting. For products with no sales history — or for franchisees and multi-banner retailers working to a periodic buying budget — Buy Plan generates category-and-month purchase quantities directly from that budget, independent of the forecasting pipeline above.

    Product Modules

    Our Solutions

    Three products, each licensed on its own: Forecasting for what to buy from suppliers, Buy Plan for spending a set buying budget, and Replenishment for what to send to each store

    / AI Demand Forecasting

    Forecasting

    The right forecasting model for every SKU

    Five forecasting models compete on each product's own history, the most accurate one forecasts it, and the result becomes a purchase plan for your central warehouse.

    • Champion model selected per SKU
    • Promotion lift measured from past events
    • Forecasts for brand-new products
    • Purchase quantities within lead time, MOQ and budget
    Learn more
    / Monthly Buy Plan

    Buy Plan

    Turn a buying budget into a 12-month plan

    For franchisees and multi-banner retailers held to a periodic budget: how much to buy in each category, each month, in units and dollars.

    • Category-by-month plan in units and dollars
    • Change the budget and the whole plan rescales
    • Promotions, holidays and new stores built in
    • Works with no sales history
    Learn more
    / AI Inventory Replenishment

    Replenishment

    The right stock in every store, every day

    Decides what each store needs from the central warehouse and when to send it, recalculating every product's target stock level daily.

    • Daily dynamic buffer per product and store
    • Assortment, product launches and new stores
    • Holidays, promotions and merchandising pushes
    • Online, store and wholesale allocation
    Learn more
    Customers

    Trusted by Industry Leaders

    How buffers.ai provides automated supply chain replenishment and assortment tools for global retail and manufacturing enterprises including H&M, P&G, Toshiba, Bath & Body Works, Victoria's Secret, and Strauss.

    Toshiba logo
    Strauss logo
    Miniso logo
    H&M logo
    Victoria's Secret logo
    Bath & Body Works logo
    Delta logo
    Urbanica logo
    P&G logo
    Top Ten logo
    Fix logo
    COS logo
    Sano logo
    Panta Rei logo
    Dorben Group logo
    Israel Electric Corporation logo
    Futura Retail logo
    About Us

    A boutique partner, not just a platform

    buffers.ai started in 2015 as a consulting practice. For years we worked inside the inventory and demand-planning operations of retailers and manufacturers, from luxury and fast fashion to grocery, consumer goods and electronics manufacturing.

    In every engagement, stockouts and excess inventory turned out to be a process problem rather than a data problem. So we built a call-to-action system. It doesn't produce reports; it tells you the next order to place, every day, across forecasting, buy planning and store replenishment.

    The software grew up, but the way we work with clients stayed the same.

    Boutique care, end to end
    We work as a boutique, and every client gets our personal attention. The people who integrate your system are the same people who support it afterwards. No hand-offs, and nobody new to explain things to.
    A WhatsApp message, not a ticket
    If something needs attention, message us on WhatsApp and you'll be talking to someone who knows your business. There's no ticket queue and no waiting for an SLA.
    An integration team that comes to you
    Every ERP, data feed and team works a little differently. Our integration team doesn't wait for a perfect spec. We come to you, learn how your operation actually runs, and design a fit that works for it.
    Experience from ten verticals
    We treat our clients as friends. That means bringing you more than software: the supply-management lessons we've learned in fashion, grocery, footwear, auto parts, manufacturing and more, applied to your business.
    2015
    Founded
    10+ years
    Consulting heritage before the product existed
    10 verticals
    Luxury, fast fashion, footwear, grocery, supermarkets, consumer goods, car parts, truck parts, and electronics and textile manufacturing
    A note from our founder

    “I have one basic rule: a positive ROI has to work for both sides. If an organization isn't getting a strong, clearly positive return from buffers.ai, we aren't doing our job.

    That's why we stay close to our clients and remain personally reachable. Their results are the only measure of our work that I count.”

    Pini Usha, Founder, buffers.ai
    Proof Points

    Results, Backed by Case Studies

    Metrics reported by buffers.ai customers, sourced from published case studies

    Bath & Body Works

    Delta Israel Brands Ltd. — deployed across European stores and online channels

    ~70%
    Replenishment process automated
    2 mo
    Initial deployment time
    2
    Markets live (Israel, Germany)
    ~90%
    Target automation
    “Among the systems currently in use, buffers.ai has proven to be the strongest performer in supporting our operational needs.”
    — Diana Grohe, Commercial Manager, Bath & Body Works
    Read the Bath & Body Works case study

    RAI

    Grocery retailer managing 6,000–7,000 SKUs

    95%
    Replenishment process optimized
    2–3 hrs
    Saved per store manager, daily
    ~5%
    Stockout rate reduction (8–10% → ~4.5%)
    1–2 mo
    Expected full ROI
    Read the RAI case study