People learned it, but do not use it at work
We practice with the work your team does every day and leave behind a workflow they can use right away.
FROM USING AI TO RUNNING ON AI
AI that fits your work. And actually gets used.
We do not stop at training or a demo. We start with one repetitive workflow, let your team try it, connect it to company systems and help keep it running.
01 / SET DIRECTIONALIGN
02 / PREPARE PEOPLEENABLE
03 / BUILD SYSTEMSBUILD
04 / RUN OUTCOMESOPERATE
WHY AI STALLS
You adopted AI. Did the work stay the same?
Another tool will not change the work on its own. The workflow, company data, human decisions and operating owner need to change together.
There is a demo, but no one owns operations
Permissions, security, internal integrations and post-launch ownership are designed from the start.
It is hard to explain whether AI helped
We look beyond usage counts to time saved, quality and the change in customer experience.
START WHERE YOU ARE
Start where you need help. One team stays to the end.
Start with an assessment, training or a working system. We meet you at the current stage and stay until people use it and the results are clear.
ALIGN
Choose one workflow to change first. Define its owner, goal, data and security boundaries together.
DIAGNOSE → PRIORITIZE → ROADMAPENABLE
Practice with real work by role. The team leaves with workflows and rules they can use right away.
LEARN → PRACTICE → ADOPTBUILD
Make AI handle repetitive document, service, sales and operations work, then connect it to the tools already in use.
PROVE → INTEGRATE → DEPLOYOPERATE
Watch quality and cost, fix failures and expand only the workflows that prove useful.
MEASURE → GOVERN → IMPROVE
TRY ONE WORKFLOW FIRST
Before a large project, try one workflow first.
In two weeks, we build a working screen with real data. Your team can try it and decide whether the next investment makes sense.
Pick one job to change
Start with work that takes too long, creates costly errors or has a clear upside.
Make something people can try
Use real data and give the owner a way to review and correct the result.
Let the result decide
Expand what works, fix what falls short and stop what does not fit. Do not begin with a large bet.
BUILT AND RUNNING
See what we built, not another explanation.
Company knowledge, sales, learning, legal, voice and multilingual work—ideas turned into products and systems that people actually use.
AGENTDECK BY GENDISTRICT · FLAGSHIP PRODUCT
Delegate work to AI. Keep people in control.
AgentDeck runs and tracks long-running work across Claude, GPT and Gemini in one place, with human approval available at the moments that matter.
- Projects, live status and results across multiple agents in one view
- Run on Windows or macOS; review and approve from your phone
- Login details and working files stay on your computer
AgentDeckConnected
Sessions8
agent-deckStabilize mobile reconnection
CodexWorkinginvestmentFinal pre-release verification
ClaudeNeeds approvalevidenceboxValidate source-grounded search
GeminiThinking
AGENTDECK / ENTERPRISE · ORGANIZATION LAYER
Put AgentDeck inside team policy and accountability.
The organization layer for AgentDeck. Manage organizations, seats, policy, audit, usage and devices while administrators remain unable to read members’ conversations.
- Role-based seats, invitations and company AI provider connections
- Organization ceilings for models, sandbox, approvals and MCP
- Audit metadata and usage without conversation content
AgentDeck / EnterprisePOLICY ACTIVE
ACTIVE SEATS48+ 6 pending
MANAGED DEVICES4398% current
AUDIT EVENTS1,284metadata only
Production agent policyENFORCED
ENTERPRISE AI
MAIDEPOT
A company AI system that connects internal information and tools to handle research, writing and customer work.
CONVERSATIONAL SALES
ZapSales
A sales tool that answers WhatsApp from the real catalogue and hands serious buyers to a person with full context.
EDTECH · EVIDENCE-BASED LEARNING
Twinsei
A classroom tool that turns existing teacher material into AI practice and shows which of thirty learners needs help now.
HEALTHTECH · DATA PLATFORM
GlowMap
A price map built by checking 2,000 websites and matching treatment conditions so people can compare like with like.
REAL-TIME AI · SPEECH / TRANSLATION
LiveSync
A live interpreter that turns one speaker into synchronized captions in more than 100 languages.
LEGALTECH · GROUNDED AI
EvidenceBox
A matter-review tool that searches calls, files and messages and returns every AI answer to the exact page or moment in the original.
WORKFLOW AI · REPORTING
Meeting Report
A tool that hears multilingual consultations and drafts reports without losing names, numbers or follow-up actions.
COMMUNITY · MATCHING
LingoSpark
A safe one-to-one exchange that finds mutual learning matches across 14 languages.
TRAVEL · ROUTE INTELLIGENCE
PilgrimWay
A pilgrimage product that organizes more than 800 sites into routes people can actually travel, with context and guidance.
VENTURE INTELLIGENCE · LATAM
Selva
A venture-intelligence map for exploring Latin American startups, investors and funding rounds with original sources attached.
DOCUMENT SECURITY · HUMAN-ONLY ACCESS
SealSend
Document security that encrypts a confidential file for verified people while refusing to release decryption keys to AI.
REAL-TIME AI · SPEAKING COACH
LingoFlow
An AI coach for realistic speaking-test and foreign-language interview practice, with one clear improvement and a sentence to retry after every answer.
PERSONAL AI · FITNESS COACH
FitCoach
An AI fitness coach that reads workout, meal and body history plus today’s condition to suggest a realistic plan and the next action.
LEARN WITH REAL WORK
Training should change the next day at work.
Start with the work participants actually do. Build AI around it, then try it in a game-like simulation based on real workplace situations.
01 / WORKFLOW-SPECIFIC AX · LG KAM
Redesign real KAM work with AI
This was not a tour of general AI tools. We analyzed KAM work across LG Electronics, Chem, Energy Solution, Innotek and Magna, then built tools and agents around market sensing, RFQ strategy and internal and external negotiation. Participants finished by negotiating with an NPC grounded in a real sales situation.
- ACTUAL KAM WORKFLOW
- CUSTOM AI AGENT
- GAME SIMULATION
“The real-time conversation with an NPC felt the most realistic.”— LG KAM AX participant
LG KAM AXRepresentative program interface
Market sensingRFQ strategyNegotiation simulation
NPC
GLOBAL OEM PROCUREMENT LEADRespond to a price reduction request while protecting deal terms.
Use market evidence and internal approval boundaries. The counterparty reacts to the choices and evidence you present.
BUYER / NPCA competitor offered a lower price. Explain why your proposal is different.
KAM / YOUI will show three points based on total cost and supply stability, not unit price alone.
LIVE FEEDBACK · evidence · strategy · next actionMISSION IN PROGRESS
02 / FIELD-WORK AX · KOTRA
AI adoption built for overseas trade-office work
Instead of touring prompt features, the program started with recurring work: buyer meeting notes, market and regulation research, and CRM follow-up. Participants designed models and field agents, tested them in virtual missions, and co-built an EU market monitoring application with KOTRA as a working outcome.
- FIELD WORK INVENTORY
- MISSION-BASED PRACTICE
- EU MARKET MONITOR APP
KOTRA FIELD MISSIONRepresentative program interface
TODAY'S MISSIONPrepare a local buyer meeting and complete the follow-up report.
- 01Market and regulation researchCreate a sourced briefingDONE
- 02Buyer meeting simulationQuestion a local-role NPCLIVE
- 03CRM and follow-up reportExtract facts and next actionsNEXT
CO-BUILT APP · EU MARKET MONITOR
A monitoring application that turns changes in EU markets into trade-office work
03 / PUBLIC INNOVATION · CHILE
Turn real city problems into AI prototypes
At an AI hackathon organized by Universidad Autónoma and the Municipality of Huechuraba, students and municipal staff defined internal administration problems and developed software proposals suited to public work. GenDistrict supported building with MAIDEPOT.
- Problems defined by municipal staff
- Team AI service prototypes
- Designed for public-work context
It has to be safe for the company to use.
People make the final call
Define what AI may do and what a person must review or approve.
Protect company data first
Decide storage, access and external model use before the build begins.
Check the result in numbers
Measure changes in time, cost, quality and customer experience, not the number of pilots.
Use the AI that fits
Choose the right model for the work and make it possible to change later.
Keep looking after it
Watch quality after launch, recover from failures and keep improving.
BEFORE WE START
What teams usually ask when they first contact us.
Your plan does not need to be complete. Start with the question you have now.
What should we start with?
Look for work that repeats often, causes errors or creates a clear benefit when improved. If the workflow is already chosen, we can skip another assessment and start with training or a build.
Can we ask for training only?
Yes. We build the program around the work participants actually do, not a tour of features. The team leaves with workflows and rules it can use right away.
Can we try something small before a large project?
Yes. In two weeks, we turn one important workflow into a working screen with real data. The owner can try it and review quality, time and risk before deciding what comes next.
Can it connect to the systems we already use?
It can connect to CRM, ERP, collaboration tools, document stores and internal APIs. We can begin safely with read-only access or human approval.
Will company data be sent to an external AI?
Not by default. Before work begins, we agree on storage, access and exactly what may be sent to an external model. Private environments and de-identification can be used when needed.
Can you run it after launch?
Yes. GenDistrict can keep operating quality, cost, security and improvements, or prepare the source and procedures for your internal team.
START WITH ONE TASK
Tell us one task that takes too long today.
You do not need an AI plan or a requirements document. Tell us who does the work and how often, and we will first look at whether AI can help.