Time back, every day
Automate intake, data entry, reconciliation, and recurring reports. Keep your team focused on decisions and customers.
BUSINESS AUTOMATION & CUSTOM SOFTWARE
Less busywork.
More business.
Turn repetitive work into precise, dependable processes. We build automation that gives your team time back, keeps running costs low, and puts you in control.
- Clear scope & fixed pricing
- Built-in checks
- Cloud or local
- 01
Receive
An invoice, request, or event starts the process.
- 02
Validate
Rules check the data. AI handles ambiguity.
- 03
Approve
Exceptions go to the right person.
- 04
Complete
Update your systems and record the result.
WHAT WE DO
Start with the work that slows you down.
One repetitive process is enough to start. We map the opportunity, agree on the economics, and build a system around your business.
01
Business automation
Connect your tools, remove repeated data entry, and keep approvals, reporting, and operations moving.
Give your team time back02
Software & automated delivery
Custom applications and automation, supported by our work on agent-managed development with clear budgets and acceptance criteria.
Build with less overhead03
Connected hardware
Bring devices, firmware, and operator interfaces into the same process as the rest of your business.
Connect physical operationsAUTOMATION THAT EARNS ITS PLACE
Reliable work. A lighter cost base.
A useful automation starts with a measurable business case: time spent, cost per task, and errors that cause rework.
Low, predictable costs
Fixed pricing for an agreed scope. We size infrastructure, usage limits, and support separately so you can see the full running cost.
Precision built into the process
Use explicit rules for calculations and decisions that must be repeatable. Add AI only where interpretation is useful.
Exceptions handled deliberately
Validate inputs, test outputs, and route uncertain cases for review. Logs, alerts, and recovery paths make failures visible.
BEYOND A CHAT WINDOW
AI interprets. Software enforces. People approve.
An LLM can propose an answer. A dependable workflow also needs permissions, validation, tests, and a clear path when something goes wrong.
CHOOSE THE RIGHT APPROACH
Use rules for certainty. AI for interpretation.
We build deterministic automation and automation with LLMs. The right balance depends on how much your inputs vary, what mistakes cost, and how often the process runs.
| What matters | The starting pointLLM on its ownBroad flexibility, variable results | We build thisDeterministic automationDefined rules, repeatable execution | We build thisAutomation + a focused LLMControlled workflows that handle variation |
|---|---|---|---|
| Getting started | Quick to prototype with a prompt. | More setup: map the process and implement its rules. | Map the workflow, then evaluate a model for specific tasks. |
| Execution speed | Waits for inference; long prompts and retries add latency. | Usually fastest for well-defined tasks; external systems can still limit speed. | Fast rule-based steps, with model calls only where interpretation is needed. |
| Cost | Low entry cost; often cheaper than manual work. Usage and review costs can grow. | Upfront engineering; typically low cost per repeat run, plus maintenance. | Engineering plus selective inference. Smaller capable models and usage limits help control spend. |
| Flexibility | Handles varied language and unfamiliar requests, with uneven accuracy. | Works within defined rules. New cases often require code changes. | Handles changing documents and requests within an agreed scope. |
| Predictability | Outputs can vary, including confident mistakes. | Repeatable behavior for the same inputs and system state; bugs remain possible. | Predictable control flow with validated model outputs and an exception path. Model answers still vary. |
| Security | Untrusted content can inject instructions. A prompt alone cannot enforce permissions. | No LLM prompt-injection surface. Still needs secure code, access controls, and input validation. | Prompt injection remains possible. Enforce tool permissions and action rules outside the model. |
| Data & privacy | Depends on hosting, provider terms, retention, and connected tools. | Can run inside your environment with controlled data access. | Self-hosted open-weight models can keep inference in-house. Compliance also depends on access, logging, retention, and operations. |
LLM on its own
Broad flexibility, variable results
- Getting started
- Quick to prototype with a prompt.
- Execution speed
- Waits for inference; long prompts and retries add latency.
- Cost
- Low entry cost; often cheaper than manual work. Usage and review costs can grow.
- Flexibility
- Handles varied language and unfamiliar requests, with uneven accuracy.
- Predictability
- Outputs can vary, including confident mistakes.
- Security
- Untrusted content can inject instructions. A prompt alone cannot enforce permissions.
- Data & privacy
- Depends on hosting, provider terms, retention, and connected tools.
Deterministic automation
Defined rules, repeatable execution
- Getting started
- More setup: map the process and implement its rules.
- Execution speed
- Usually fastest for well-defined tasks; external systems can still limit speed.
- Cost
- Upfront engineering; typically low cost per repeat run, plus maintenance.
- Flexibility
- Works within defined rules. New cases often require code changes.
- Predictability
- Repeatable behavior for the same inputs and system state; bugs remain possible.
- Security
- No LLM prompt-injection surface. Still needs secure code, access controls, and input validation.
- Data & privacy
- Can run inside your environment with controlled data access.
Automation + a focused LLM
Controlled workflows that handle variation
- Getting started
- Map the workflow, then evaluate a model for specific tasks.
- Execution speed
- Fast rule-based steps, with model calls only where interpretation is needed.
- Cost
- Engineering plus selective inference. Smaller capable models and usage limits help control spend.
- Flexibility
- Handles changing documents and requests within an agreed scope.
- Predictability
- Predictable control flow with validated model outputs and an exception path. Model answers still vary.
- Security
- Prompt injection remains possible. Enforce tool permissions and action rules outside the model.
- Data & privacy
- Self-hosted open-weight models can keep inference in-house. Compliance also depends on access, logging, retention, and operations.
IN PRACTICE · INVOICE PROCESSING
A small model reads. Your rules decide what happens.
Extract fields from a changing invoice format, check totals and supplier records in code, then send mismatches for review. The model interprets the document; it cannot authorize a payment.
For stable inputs, automation alone may be the best fit. Add the lowest-cost model that meets tested quality requirements when language or document variation makes rules alone impractical.
HOW WE BUILD
From request to release, one managed pipeline.
Our internal R&D focuses on automating software development: coordinating agents, validating their work, and reducing delivery overhead. This is the process we are building toward.
- 01
Define
Turn the request into requirements and acceptance tests.
- 02
Plan
Agents break down tasks and choose suitable tools and models.
- 03
Build
Agents implement changes in isolated environments.
- 04
Verify
Run tests and review results against the agreed criteria.
- 05
Release
Approve deployment, monitor results, and keep a rollback path.
Match each task to the lowest-cost capable agent.
Route routine work to smaller models or deterministic tools. Escalate only when complexity or failed checks require it. Keep execution within defined permissions and budgets, with a traceable record of the work.
Make correctness part of the build.
Our engineering research connects clear software contracts with automatic validation and repeatable checks. The aim is less repetitive coding, earlier error detection, and changes that can be verified before release.
YOUR INFRASTRUCTURE. YOUR BOUNDARIES.
Keep sensitive work close.
Local and private deployment options for businesses where data exposure matters, including finance, aerospace, and confidential R&D.
Run inside your environment
Deploy on your own hardware or private infrastructure, with local models where suitable. Define which systems can be reached and which data, if any, can leave.
Controls matched to your risk
Scope access, retention, audit trails, and approval gates around your requirements. We assess model quality, hardware needs, and operating costs before choosing the deployment.
CUSTOM APPLICATIONS & WEBSITES
The right interface for the work.
Make your services easier to find and your software easier to use. Every screen should help someone take the next step.
Fast, useful software
Customer portals, internal tools, and web applications that make everyday tasks easier. Performance and maintainability are part of the build.
Easy to use, on any screen
Clear navigation, readable interfaces, and accessible interactions help people complete their work with less friction.
AI search visibility (GEO)
Help search and AI systems understand your business through clear answers, crawlable pages, and accurate structured data. Generative Engine Optimization builds on solid search foundations; placement and citations cannot be guaranteed.
Connected to your business
Integrate your CRM, ERP, and existing tools through APIs and focused workflows. Add localized content and useful analytics where they serve your customers.
HARDWARE
From hardware to human interface
One team for the hardware, firmware and interfaces your product needs.
Hardware & HMI
We deliver embedded systems development and human-machine interfaces that bridge the gap between your hardware and the people who use it.
From firmware to the touchscreen your operators use, one team owns the whole stack. That means fewer handoffs, faster iteration and a product that behaves as one coherent system, not a pile of parts.
HMIs are built around the people on the factory floor: clear overviews, responsive controls and data that surfaces when it matters, so your team works with the machine instead of around it.
OUR WORK
Projects we are proud of
Neural networks reading gas meters, social platforms, IoT fleets: this shipped work shows the range we deliver to clients worldwide.
Project illustrations show the subject of our work, not actual product screens or hardware.
Climbo
WebWhite-label AI review management platform for local businesses: agencies resell it under their own brand to grow MRR.
Project details
Our contribution
We developed the original Climbo 1.0 and the new Climbo 2.0 platform now online. Our contribution spans both generations of the product, from the original implementation to the development of its successor. The platform gives agencies a branded software offering for managing their clients’ online reputation.
Optix
Web / SoftwareCloud management platform for modern opticians — CRM, agenda, automations, marketing and inventory in one tool.
Project details
Our contribution
We take on high-complexity engineering within the existing Optix platform: reflection-based code, legacy-code fixes, software architecture, backend development, and database work. Our contribution focuses on the foundations behind the product, resolving difficult technical problems and making the codebase easier to maintain and extend.
Primo Piano
Web / SoftwareSubstantial implementation work on the new CRM and order management system for Italian kitchen brand Primo Piano Cucine.
Project details
Our contribution
We implemented a substantial part of Primo Piano’s new internal CRM, including work on customer and order management. The project moved functionality from a legacy PHP system into a Java-based platform. Our role was hands-on implementation within the wider CRM rebuild, translating the business’s operational requirements into working software.
Gas meter neural network
AI / HardwareNeural-network recognition of legacy gas-meter readings for a British customer, connecting existing meters to remote monitoring.
Project details
Our contribution
We delivered most of the development for a British customer’s optical meter-reading project. The system uses a neural network to recognize the numbers displayed by legacy gas meters and makes those readings available remotely. The work connects image interpretation with a practical metering workflow, extending the usefulness of existing equipment.
Laiwa IoT devices
IoT / HardwareConnected IoT devices: hardware, firmware and the platform behind them, built as a complete custom software project.
Project details
Our contribution
We carried out most of the implementation across the connected-device projects shown here, covering hardware, firmware, and the supporting software platform. The work brings device communication and application software together so sensor data can move from physical equipment into a usable connected system.
#BeFirst Social
WebSocial platform for creators and communities, designed to grow engagement and retention.
Project details
Our contribution
We built most of the BeFirst Social platform, bringing its customer-facing web application and supporting backend together. The product combines social-profile management with a community for discovering people and sharing content. Our contribution covered the implementation that connects those user workflows into a working service.
Fast-Lab
Web / HardwareWater activity measurement software for a French laboratory, connecting laboratory instruments with a usable measurement interface.
Project details
Our contribution
We delivered most of the development for this laboratory measurement project, with software that connects water activity instruments to an operator interface. The work turns measurements from the instrument into information laboratory users can access and work with. Water activity describes the availability of water within a sample, rather than water-supply levels.
MeterLinq · Gas metering
IoT / SoftwareSmart gas-metering work with MeterLinq Srl, including an AMGas pilot in Italy and collaboration with Vodafone.
Project details
Our contribution
We worked on the concentrator interface using wireless M-Bus and DLMS/COSEM, with spread-spectrum backhaul using On-Ramp Wireless technology (later Ingenu/RPMA). Our work also included an open-source COSEM library.
TRUSTED BY
Trusted by forward-thinking companies
Startups and growing businesses count on us for dependable software across web development, IoT hardware and automation.
PRACTICAL ANSWERS
Before we build.
What should we automate first?
Start with a frequent, repeatable task: data entry, document intake, reconciliation, reporting, or approvals. We assess the time spent, exception rate, and integration effort before recommending a build.
How do you keep costs predictable?
We agree on a defined scope and fixed project price. Infrastructure, model usage, and support are scoped separately. Smaller capable models, usage limits, and deterministic software help keep ongoing costs low.
How is this different from using an LLM directly?
An LLM generates a response. A managed workflow adds explicit rules, validation, tests, access controls, and exception handling. AI handles interpretation where useful; software and approval gates constrain what happens next.
Can the system run locally?
Yes. We can design for your own hardware or private infrastructure, using local models where suitable. We assess hardware capacity, model quality, data boundaries, and operational requirements with you.
Do you still build websites, apps, and hardware?
Yes. We build custom applications, websites, connected devices, firmware, and interfaces, and integrate them with your business processes.
CONTACT
What could your team stop doing manually?
A useful conversation starts with the work. Email us about an automation, a software project, or a private deployment.
Start with one process.
Tell us what happens today, the tools involved, and where time or money gets lost. We’ll help define the scope, likely running costs, and a practical next step.
info@lobesoft.comOr open your email app using the address above.
Helpful details to include
- The task you want to simplify
- How often it happens and who handles it
- Your existing software and data sources
- Budget, timing, and privacy requirements