Unified Voice Command Interface

For

Operations leads at mid-sized logistics companies

Solves

Staff spend excessive time switching between disparate software systems to answer simple questions or perform routine tasks.

Delivers

Structured data summaries, executed task confirmations, interaction logs.

Built in

10 days of creation time, MVP in 2 days

Investment

$5,000 for the MVP, $15,000 for the full product

Run it

Inside your business, or as part of your offer to clients

For operations leads at mid-sized logistics companies, turn spoken questions and commands into direct data queries and task executions across multiple software systems.

1. Parse natural language voice commands.
2. Route queries to specific software APIs.
3. Execute routine administrative tasks.
4. Summarise complex data sets.
5. Maintain user context across sessions.
6. Provide feedback on command accuracy.

What goes in, what comes out

The workflow

1. In
   Start with

   Voice recordings, user authentication tokens, API credentials.

2. 1

   Define intent

3. 2

   Authenticate user

4. 3

   Route to system

5. 4

   Retrieve data

6. 5

   Synthesise response

7. 6

   Present output

8. 7

   Log interaction

9. Out
   Finish with

   Structured data summaries, executed task confirmations, interaction logs.

AI does the heavy lifting, people stay in charge

Use large language models to interpret intent and generate the correct API calls. Validate the output against business logic rules. A system administrator reviews the routing logic and security permissions before deployment.

What your team sees

Key screens: Command centre, Integration hub, Audit log. The main screen is a command centre displaying a list of active voice sessions, recent queries, and system status indicators. It allows users to see the history of interactions and the current state of connected systems.

Accounts and administration

User roles, API key management, session history, permission levels, audit trails.

Integrations and data access

ERP systems, CRM platforms, Email servers.

We build with our own AI software development factory, so most implementations take days to a few weeks of creation time, not months. You see working software at every step, and exact timing depends on availability.

1. 1

   Scoping call

   Day 1

   Thirty minutes on your process, your data and how you want to run it: for your own team, or for your clients. You get a fixed scope and price for the MVP.

2. 2

   MVP

   2 days

   One buyer segment, one recurring use case. Manual review in the loop. Built by our AI software factory.

3. 3

   Paid pilot

   3 days

   Accounts, roles, review states, audit trail and the first integration, hardened for two to three paying pilot customers.

4. 4

   Full product

   5 days

   Self-serve onboarding, billing, monitoring and the wider integration set.

5. 5

   Run and improve

   Monthly

   We host, monitor and improve it for a fixed monthly fee, or hand it over to your team. How the retainer works.

Why we start with an MVP

An MVP, or minimum viable product, is the smallest version that your users can actually work with. It is not a cheap version of the full solution. It is a test, built to answer the questions that decide whether the rest is worth building.

1. Pick the riskiest assumption. Here: will operations leads at mid-sized logistics companies use it to solve "staff spend excessive time switching between disparate software systems to answer simple questions or perform routine tasks"?
2. Build only what tests it. One team, one use case, a few core modules. People do the rest by hand for now.
3. Run a paid pilot. Measure the reduction in time spent on routine queries and the number of context switches required to complete a task.
4. Measure, then decide. Track average query resolution time and user adoption rate per week. Then expand, change course or stop, with evidence instead of opinions.

MVP scope for this solution. One logistics client, one use case for inventory checking, two core integrations (ERP and Warehouse Mgmt), manual review of API calls.

After the MVP. Automated execution of multi-step workflows, predictive analytics based on voice queries, custom voice training for industry jargon.

What the build depends on. Stable internet connection, compatible microphone hardware.

A planning range to start the conversation, not a quote. You pay per phase, so you can stop after the MVP.

Running costs per month

A rough indication of monthly hosting and AI model costs once it is live, not tested. Real costs depend on usage, file sizes and the models chosen.

Your brand, or this one

Run it under your own brand, or start from this concept style.

Selling it to your own clients: the go-to-market playbook

Pricing to test

Hypothesis: 50 dollars per user per month.

Message to test

Speak your data, not your apps.

Where to find buyers

LinkedIn, industry conferences, tech review sites.

Lead magnet

Free 30-day trial of the command centre.

The first 30 days

1. Week 1: System architecture and API mapping.
2. Week 2: Voice model training and integration testing.
3. Week 3: User acceptance testing and security audit.
4. Week 4: Pilot launch and feedback collection.

Paid pilot

Measure the reduction in time spent on routine queries and the number of context switches required to complete a task.

Success metrics

Average query resolution time, user adoption rate per week.

Retention and expansion

Continuous updates based on new voice commands and system changes.

Why clients would pick it

The difficulty of mapping complex internal data structures to natural language and the need for ongoing maintenance of API integrations.

Alternatives and positioning

Native mobile apps, chatbots, manual data entry, hiring support staff.

Main delivery costs

Engineering time for integration, cloud hosting for voice processing.

Strict role-based access control, no access to sensitive financial data without explicit approval, blocking of commands that could delete data.