tuntas.ai / ENTERPRISE AI CONSULTING & SOLUTIONS

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Your enterprise's next leapBegins with AI

Enterprise-wide intelligence · Intelligent decisions · Human-AI collaboration

tuntas.ai starts with real business objectives, then connects AI models with enterprise data and day-to-day work so AI can move from answering questions to driving action across growth, service, production and decision-making.

Share one enterprise-upgrade priority with us. Our consultants will help identify the first AI scenario worth implementing.
tuntas.ai / AI CONSULTINGFrom one enterprise upgrade to transformation across the organization
FULL-SCENARIO AI SOLUTIONSDISCOVER · DESIGN · DELIVER

AI ACROSS THE ENTERPRISE

Ten enterprise scenariosbring AI into every moment of real work

From acquisition, service and management to production, healthcare, education and security, we begin with real workflows. Existing systems, AI capabilities and team collaboration are shaped into an operational loop that can launch and keep improving. Select a scenario to view its system prototype and delivery capabilities.

THE tuntas.ai AI CAPABILITY ARCHITECTURE

One shared intelligence foundation, configured for every enterprise

Shared capabilities for conversation, knowledge, documents, vision, decisions and AI assistance can be enabled through SaaS or a dedicated enterprise environment. They are then configured around your data, existing systems, work processes, access rules and business measures, with private or hybrid deployment available for sensitive scenarios.

TEN ENTERPRISE SCENARIOS
MarketingServiceManagementIndustryHealthcareRetail & hospitalityFinanceEducationAgricultureSecurity
VOICE

Conversational & voice AI

Customer outreach · real-time guidance · multilingual service · conversation quality review

KNOWLEDGE

Enterprise knowledge

Knowledge base · permission-based answers · traceable sources

DOCUMENT

Document & content intelligence

Extract · classify · compare · draft · review

VISION

Vision & multimodal AI

Visual inspection · event detection · image and document understanding · evidence review

DECISION

Prediction & decision intelligence

Demand forecasts · risk assessment · scheduling optimization · recommended next steps

ASSISTANT

AI assistants & automation

Task coordination · cross-team action · human confirmation · system updates

SHAREDShared AI capabilitiesA common set of models, knowledge, voice, vision and assistants that can support many scenariosCUSTOMIZEAI configured for your enterpriseChoose the capabilities that fit your data, systems, work processes, roles and business measuresDEPLOYMENTFlexible deployment and governanceUse SaaS, a dedicated enterprise environment, private deployment or a hybrid approachDELIVERYtuntas.ai consulting and implementationMove from clear upgrade priorities and a focused pilot to wider adoption and ongoing operations, while your enterprise stays in control

CLIENT FIELD NOTES

How AI enters real business

Four project records show where day-to-day work was getting stuck, how tuntas.ai introduced AI into each process and what changed afterward—from customer service and regional retail to dealership growth and warehouse operations.

AI service project at a China Mobile city retail center
CMCHINA MOBILETELECOM RETAIL SERVICE
THE UPGRADED WORKFLOWArrival → AI identifies the need → approved guidance → staff confirmation → summary and task → accountable follow-up → result recorded
CLIENT PRACTICE · CHINA MOBILE CITY RETAIL CENTER

Service that continues beyond the counter: an intelligent upgrade for a China Mobile city retail center

PROJECT CONTEXT

The project covered reception, counter service and post-visit follow-up. Plan enquiries, device sales, complaints and business-customer needs often crossed several employees and existing systems.

BEFORE THE UPGRADE

At busy times, repeated plan and policy questions occupied counter staff, complex policies were explained inconsistently, and unfinished requests lacked a clear owner and due date after customers left.

HOW THE SOLUTION WAS IMPLEMENTED

tuntas.ai connected approved service knowledge to an employee assistant that identifies customer needs, retrieves the right policy and drafts a service summary. Once confirmed by staff, unfinished items become tasks or tickets, while business-customer needs are assigned through CRM.

AI CAPABILITIES BUILT
Retail-service knowledge assistantConversation summaries and quality reviewAutomatic tasks for unfinished requestsBusiness-customer need identification and routing
IMPLEMENTATION OUTCOMES
A structured summary is ready as each enquiry endsService recordOpen items carry a named owner and due dateFollow-upRetail and follow-up teams work from the same customer recordCross-team work
Haier regional retail AI project
HaierHAIERAPPLIANCE RETAIL & SERVICE
THE UPGRADED WORKFLOWMulti-channel enquiry → unified customer profile → home and budget needs → product-bundle draft → adviser approval → delivery and installation → after-sales record
CLIENT PRACTICE · HAIER REGIONAL RETAIL OPERATION

From customer needs to completed installation: connecting the full customer journey for a Haier regional retail operation

PROJECT CONTEXT

A Haier regional retail operation manages direct stores, dealer outlets, ecommerce service, live-stream channels and leads from its owned customer channels. A sale requires room, size, budget and product-bundle details before inventory, delivery, installation and service begin.

BEFORE THE UPGRADE

The same customer appeared separately across channels, advisers rebuilt product-bundle proposals manually, and sales promises, delivery, installation and after-sales records remained disconnected.

HOW THE SOLUTION WAS IMPLEMENTED

tuntas.ai merged leads from every channel into one customer profile, extracted room, budget and category preferences, and drafted product-bundle proposals from approved product information. Once confirmed by an adviser, each plan continued into inventory, delivery, installation and after-sales milestones.

AI CAPABILITIES BUILT
Cross-channel customer profilesHome requirements and product-bundle proposalsAdviser follow-up and quotation assistanceDelivery, installation and after-sales coordination
IMPLEMENTATION OUTCOMES
Store and online enquiries merge into one customer profileCustomer leadsRoom requirements become an adviser-ready bundle planProposal preparationDelivery, installation and after-sales milestones return to the customer recordDelivery & after-sales
AI sales project for a local automotive dealership
4SLOCAL AUTO DEALERSHIPAUTOMOTIVE DEALER OPERATIONS
THE UPGRADED WORKFLOWMulti-channel leads → match and remove duplicates → AI-assisted qualification → priority assignment → test-drive booking → purchase → service and next-purchase engagement
CLIENT PRACTICE · LOCAL AUTO DEALERSHIP

Bringing valuable leads forward: an AI-assisted sales upgrade for a local dealership

PROJECT CONTEXT

The dealership receives daily leads from automotive platforms, its website, short-form video, calls, visits and referrals. Each sale requires several follow-ups, a test drive and negotiation before the relationship continues into servicing, insurance renewal and the customer's next vehicle purchase.

BEFORE THE UPGRADE

Duplicate and invalid leads were mixed with high-intent buyers, response times varied by salesperson, and promised test drives, callbacks and customer preferences remained in private conversations rather than a shared record.

HOW THE SOLUTION WAS IMPLEMENTED

tuntas.ai matched and removed duplicate leads across channels, then used AI-assisted outreach to capture model, budget, timing and test-drive intent. High-intent buyers moved into a prioritized sales queue, while summaries, callback dates and appointments returned to CRM and supported the relationship after purchase.

AI CAPABILITIES BUILT
AI-assisted outreach and intent captureLead matching and duplicate removalSales tasks and test-drive bookingService, insurance-renewal and next-purchase engagement
IMPLEMENTATION OUTCOMES
High-intent customers enter the sales queue firstLead priorityTest-drive promises and callback plans are recorded in CRMSales follow-upServicing, insurance renewal and the next purchase continue from the same customer recordPost-purchase journey
Local warehouse AI operations project
WHLOCAL LOGISTICS WAREHOUSELOGISTICS & FIELD OPERATIONS
THE UPGRADED WORKFLOWVideo and sensor alerts → event grouping → risk level → staff confirmation → assigned work order → resolution and review → evidence archived
CLIENT PRACTICE · LOCAL LOGISTICS WAREHOUSE

From seeing an exception to closing it: an AI operations upgrade for a local warehouse

PROJECT CONTEXT

The project covered a regional distribution warehouse from receiving and sorting to storage, loading and dispatch. Cameras, smoke detectors, equipment sensors, WMS and manual inspections were already in place, but did not yet feed a single response process.

BEFORE THE UPGRADE

Control-room staff manually rotated through many camera feeds, several alerts could describe the same event, and confirmed problems were still assigned through group messages without a complete record of evidence, action and timing.

HOW THE SOLUTION WAS IMPLEMENTED

tuntas.ai grouped video events, sensor alerts and WMS records by time, location and task, then assigned a consistent risk level. After staff confirmation, the platform creates a work order for the responsible zone and shift and archives photos, results and elapsed time when the work is complete.

AI CAPABILITIES BUILT
Aisle blockage and unsafe-work detectionSmoke, fire and equipment-event reviewWork-order assignment and overdue remindersAutomatic field-evidence records
IMPLEMENTATION OUTCOMES
One exception brings video, sensor and WMS evidence togetherException reviewConfirmed issues become tasks for the right zone and shiftAssigned work orderResolution photos, outcome and elapsed time are archived togetherField evidence

CORE TEAM

Business vision, industry experience and implementation expertise

A cross-disciplinary team with experience in enterprise strategy, international growth, foundation models and intelligent-product implementation.

Peter leadership portrait

Peter

Founder · AI strategy

A Tsinghua University graduate and serial entrepreneur with experience in digital finance, digital marketing and enterprise growth. He connects opportunity assessment, pilot design and AI commercialization into a practical growth path.

Business strategy · Growth · New venture development
Sven leadership portrait

Sven

Global markets & growth

Brings international-market experience from the Alibaba ecosystem, spanning regional strategy, channel partnerships, brand building and localized growth. He turns headquarters strategy into practical market-entry and customer-acquisition programs.

International expansion · Market entry · Localization
Nemo leadership portrait

Nemo

Enterprise AI & foundation models

Former product director at China Mobile, responsible for foundation-model product planning, enterprise applications and large-scale implementation. His experience spans operator-grade knowledge assistants, intelligent customer service and workflow copilots, from architecture and data connection to rollout.

AI products · Platform architecture · Large-scale implementation
Leo leadership portrait

Leo

Intelligent products & implementation

Former intelligent-hardware lead at Dreame with end-to-end experience across product definition, supplier collaboration, hardware-software integration, validation and large-scale delivery.

Product strategy · Engineering collaboration · Operations
Ethan leadership portrait

Ethan

Tencent AI technology lead

An AI technology leader at Tencent focused on foundation-model application architecture, agent engineering and enterprise AI platforms. His work spans knowledge assistants, intelligent service and operational analytics, including evaluation, data connection, access governance and production deployment.

Model engineering · Agent systems · Enterprise platforms

Turn your next enterprise upgrade into an AI solution built for real work

Tell us which part of the business you want to improve, which systems your teams already use and what result you want to achieve. The tuntas.ai solution team will identify the right starting point, choose the AI capabilities it needs and map a practical implementation path—from one valuable pilot to wider adoption across your enterprise.

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