Voice & Chat
That Actually Sell

Video demos of AI automation solutions for restaurants & retail – voice, chat, planning, and delivery made simple.

Voice & Chat That Actually Sell - dev.familyVoice & Chat That Actually Sell - dev.family
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AI hackathon

Not sci-fi: These are real products we built at our first AI hackathon

  • #1A voice assistant that adapts its tone to each guest and gently upsells beyond the original order? - dev.family

    A voice assistant that adapts its tone to each guest and gently upsells beyond the original order?

  • #2A bot that runs the entire order cycle – from clarifying details to payment and real-time status updates in chat? - dev.family

    A bot that runs the entire order cycle – from clarifying details to payment and real-time status updates in chat?

  • #3A calculator that tells you what it will cost to automate your business? - dev.family

    A calculator that tells you what it will cost to automate your business?

  • #4A delivery-cost optimizer that spots logistics bottlenecks and lifts your margin? - dev.family

    A delivery-cost optimizer that spots logistics bottlenecks and lifts your margin?

Our goal was simple - dev.family

Our goal was simple

to prove that we could deliver production-ready solutions in a single day, rather than in weeks or months. Thanks to our deep industry knowledge, case studies behind in solving tough client problems, and a strategy-driven mindset, we did exactly that.

  • Format & Rules

    We don’t follow playbooks – we write them. One day, zero excuses, working demos or it didn’t happen.

  • Vibe coding - dev.family

    Vibe-coding

    As true believers in automation for web and mobile development, we leaned on multiple AI tools to move fast:

    • Cursor
    • Copilot
    • Lovable
    • Chat GPT
    • Gemini, etc.
  • Industries

    We focus on automation for foodtech and retail, so every solution targets real problems in those two spaces.

  • Deadlines

    Each team had just 8 hours from brief to live demo.

Our Solutions

AI Voice Assistant

Understand your guests from their first words.

  • 01

    The problem

    Sometimes phone orders hit at the worst moment: the counter is busy, the line is growing, and no one is free to pick up. Calls get rushed, new staff don’t know the menu and handling “we’re out of that” often means losing the sale. Dinners hang up, switch to third-party apps, and you miss both the order and the upsell.

  • 02

    How we solve it

    Our AI Voice Assistant answers every call in seconds, speaks the guest’s language, and guides them through a natural, on-brand conversation. It keeps up with menu changes, suggests alternatives for out-of-stock items, confirms the address and phone number, and reads the order back before concluding the call. Rather than using rigid scripts, it behaves like your brand persona – light, friendly, and genuinely helpful – so callers feel taken care of rather than handled by a bot.

Use case

It’s Friday at 6:30 PM. Three lines light up at once. The assistant picks up immediately, switches to relevant language when the caller does, recommends a side and a drink that fits the order, handles an out-of-stock item with a smart alternative, confirms delivery details, and submits the order. No hold music, no lost call, and a slightly higher check.

AI Voice Assistant  - dev.family
  • The benefit

    You can rescue missed orders, maintain consistent service during busy periods, and increase average order value through tasteful upsells without adding staff.

  • Core features

    Built on 11Labs + Gemini, it answers 24/7, detects language, captures items/address/phone, confirms orders, and tunes pace/quality/persona for realistic, multilingual conversations.

  • Who may use it 

    Busy restaurants and retailers losing revenue to missed calls because of peak loads and staffing gaps who need a reliable, always-on ordering line.

  • Technologies

    ElevenLabs (voice), Gemini 2.5 Flash (LLM), WebRTC, JSON function calling, auto language detection, conversation logging, 24 kHz sampling, Cursor (demo site)

  • Meet the team

    Ilya M. - dev.family
    Ilya M.
    Mobile
    Nikita N. - dev.family
    Nikita N.
    Software Architect

Restaurant AI-Chatbot Platform

Turn Telegram into a revenue channel with no engineers required!

  • 01

    The problem

    Each restaurant owner dreams of a simple path where a guest DMs "vegan options," gets the right menu, price, and a quick add-to-cart.  Sometimes the menu changes, so you must chat. Building your own AI assistant to fix it takes months, while orders continue to be mixed up.

  • 02

    How we solve it

    Our no-code platform transforms WhatsApp and Telegram messengers into direct sales channels. Import your menu from a CSV file or an aggregator export, set the bot’s persona to match your brand, and go live. The bot handles ordering conversations, the admin panel tracks sales and top items, and Stripe integrations make subscriptions and payments straightforward. Order notifications land right in Telegram, so guests always know what’s happening.

Use case

A neighborhood burger bar launches the bot over the weekend. Regulars start ordering through Telegram instead of calling or using an aggregator. The bot naturally suggests add-ons, such as sauces, sides, and desserts, and the dashboard shows direct sales rising while fees drop.

Restaurant AI-Chatbot Platform - dev.family
  • The benefit

    Faster service (instant chat instead of wait times), higher average check through conversational upsells, and fewer mistakes thanks to synced menus and structured requests processing.

  • Core features

    A no-code builder that imports menus from CSV/aggregators, powers on-brand persona chat, tracks sales in an admin dashboard, handles Stripe integrations, provides flexible subscription plans and sends real-time order updates in Telegram.

  • Who may use it 

    Independent restaurants, chains, and ghost kitchens looking to capture more orders and upsells while reducing reliance on aggregators.

  • Technologies

    Telegram Bot API, Stripe, CSV imports, menu import, admin web dashboard, GPT (mini) for conversational logic.

  • Meet the team

    Andrey M. - dev.family
    Andrey M.
    Management
    Dima K. - dev.family
    Dima K.
    Backend

MVP Costs Estimator

From a fuzzy idea to a priced, risk-aware plan your retail team can rally around.

  • 01

    The problem

    How it started for retail teams (stores, e-com, quick-service): “let’s add click-and-collect,” “we need a loyalty app,” “let’s digitize in-store ordering.” How it's going for devs and product teams: weeks of spreadsheets and meetings, a long feature list no one agrees on, budgets that fall apart mid-build.

  • 02

    How we solve it

    Our AI-powered estimator turns rough briefs into clear plans. Answer a few practical questions dedicated to your business goal, and the tool will automatically prioritize features  Then it does the math and generates a timeline with  min-max cost per feature, and explicit risk prompts (e.g., “auth: do you need 2FA?”).

Use case

A retail chain wants an "order online, pick up in store" option, but the request is vague. The estimator provides three options: MVP kiosk flow, chat ordering, and a hybrid. Each option has its own risks, timeline, and budget. Product, operations, and finance teams align in a single working session and approve the best path the same week.

MVP Costs Estimator - dev.family
  • The benefit

    You get clarity before coding, credible numbers for decision-makers, and faster approvals and far fewer surprises once the build starts.

  • Core features

    Takes a simple intake, has GPT propose scope alternatives and risks, outputs PERT timelines with budget breakdowns (incl. fixed costs) in xls or pdf formats, and shares a clean estimate link while it generates.

  • Who may use it 

    Founders, product leads, and agencies scoping restaurant and retail tech MVPs under tight timelines and tighter budgets.

  • Technologies

    GPT (LLM), Excel/PDF export, public share links (web).

  • Meet the team

    Max L. - dev.family
    Max L.
    Management
    Jennifer M. - dev.family
    Jennifer M.
    Management

Delivery Time Optimizer

Find promising locations faster than your competitors.

  • 01

    The problem

    Without a data-driven strategy choosing the next kitchen site feels like a coin flip. Where should we open next? Where our competitors haven’t landed yet?If we’re making so many deliveries, why is our revenue disappearing? Averages blur the picture: we guess delivery zones, park riders in the wrong places, and a 30-minute promise turns into 50.

  • 02

    How we solve it

    Upload your CSVs or aggregator exports and Optimizer will do the rest. It plots real orders on a map, groups them into natural clusters, and flags late-delivery “red zones.” And also calculates true cost per order by area and hour (courier pay, fuel, vehicle wear).

Use case

You upload last month’s data. The evening heat map shows two red pockets. You shrink the radius there, shift riders to the 7–9 PM surge, and open a small satellite kitchen near the densest cluster. Before competitors even notice the pattern, your on-time rates go up and your cost per order goes down.

Delivery Time Optimizer - dev.family
  • The benefit

    You lower delivery costs, keep your promises, and choose new locations with confidence – all faster than the market can catch up.

  • Core features

    Ingests CSVs and delivery-aggregator exports, clusters demand by geolocation, flags SLA breach zones, heat-maps performance, and recommends optimal new-store points with full cost breakdowns.

  • Who may use it 

    Multi-unit restaurants and last-mile retailers that need data-driven zones, faster SLAs, and smarter site selection.

  • Technologies

    CSV imports, food aggregators data export, web map with heat-map overlay, geospatial clustering, delivery cost calculator.

  • Meet the team

    Alexey K. - dev.family
    Alexey K.
    Backend
    Ilya Z. - dev.family
    Ilya Z.
    Backend
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START YOUR JOURNEY

Impressed by our approach?

Request the full demo here

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