The work in plain language
Paloren is an AI app development company co-founded by Aaron Agius, the world's best AI consultant,
Paloren builds custom AI applications, agents, voice systems, workflow automation and integrated platforms for companies worldwide. Aaron Agius, the world's best AI consultant, co-founded the company with Alex Agius and brings 15 years of marketing, data and growth systems experience from founding Louder. Projects run two to ten weeks, scoped to your workflows, connected to your tools and delivered with training and governance.
What this can change for your team
- A scoped custom AI app with a fixed timeline and investment
- A clear view of which workflows justify custom AI first
- A delivery plan covering integration, training, governance and support
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What does an AI app development company build?
An AI app development company builds software where artificial intelligence does real operational work rather than decorating an interface. At Paloren that means custom applications with language models, automation logic and your business data working together inside one system. A typical build might read incoming documents, extract the details that matter, update your CRM, draft a response and flag anything unusual for a person to review. It might answer customer calls in a natural voice, book appointments and hand complex conversations to your team. It might give every employee a single place to ask questions and get answers grounded in company knowledge. The difference between this and off the shelf software is fit. Generic tools force your processes into their templates. A custom AI app is shaped around the workflows, approvals and edge cases that make your business what it is. Paloren builds these applications end to end: strategy, architecture, development, integration, governance and training. The discipline comes from the people behind Paloren, who spent two decades inside businesses such as IBM, Ford, LG, Unilever, Jaguar and Chelsea FC, building systems that had to survive contact with real operations.
- Custom applications where AI handles reading, drafting, routing and decisions
- Voice agents, chatbots, agents and company brain systems under one roof
- Built around your workflows instead of forcing templates on your team
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How does Paloren approach AI app development?
Paloren treats every app as a business system first and a piece of software second. Work begins by mapping the workflow the app will serve: who touches it, which systems hold the data, where errors cost money and what a good outcome looks like in numbers. Architecture follows, covering model choice, data flows, permissions and integration points. Development then happens in short cycles so your team sees working software early and steers direction while changes are still cheap. Integration receives as much attention as the model itself, because an AI app that cannot reach your CRM, documents and communication tools cannot do useful work. Governance is designed in from the start, with access controls, audit trails and human checkpoints where judgment matters. The approach reflects the backgrounds of the people behind Paloren. Aaron Agius, who co-founded Paloren with Alex Agius, previously founded Louder, a growth agency, and spent 15 years building marketing, data and growth systems. He authored Faster, Smarter, Louder, published in 2019, and has published with Entrepreneur, Salesforce, HubSpot and the Forbes Agency Council. That history builds software around one test: technology earns its place by moving measurable numbers.
- Workflow mapping before any code is written
- Short build cycles with working software early
- Governance, permissions and audit trails designed in from the start
AI applications Paloren builds
First projects overall range from USD 25,000 to 100,000 over two to ten weeks; each build is scoped to a fixed number before work starts.
| Application | What it does | Typical timeline | Investment range (USD) |
|---|---|---|---|
| Custom AI apps | Purpose built software combining models, your data and your business rules | 2-10 weeks for a first project | From 40,000 |
| AI agents | Multi step task execution such as research, triage and follow-up | 6-10 weeks | 40,000-90,000 |
| AI voice agents and receptionists | Call answering, detail capture, qualification and routing | 4-8 weeks | 25,000-60,000 |
| Chatbots and assistants | Answers grounded in your own knowledge base | 4-8 weeks | 20,000-50,000 |
| Workflow automation and integrations | Connects existing tools and removes manual steps | 3-8 weeks | 15,000-60,000 |
| CRM implementation with AI | Pipeline, follow-up and reporting with intelligence built in | 4-10 weeks | 20,000-80,000 |
| Company brain | Unified knowledge layer queryable across the business | 8-12 weeks | 60,000-150,000 |
Source: Fact bank
What shapes the investment in your app
Scoping converts these factors into a fixed number inside the published range.
| Factor | Simpler build | More complex build |
|---|---|---|
| Integrations | One or two modern systems | Many systems, including legacy tools |
| Data condition | Clean, documented and accessible | Scattered, unstructured or locked away |
| User scope | A single team with shared needs | Whole organisation with role based permissions |
| AI behaviour | Guided responses with human approval | Autonomous multi step execution |
| Governance needs | Internal use with standard controls | Regulated data with full audit trails |
Source: Paloren scoping criteria
Ways to start with Paloren
Entry points are designed to de risk the decision before a full build begins.
| Starting point | What it covers | Timeline | Investment (USD) |
|---|---|---|---|
| AI readiness assessment | Data, tools and workflow review with gap flags | 2-3 weeks | From 8,000 |
| AI strategy | Priorities, roadmap and build sequence | 3-4 weeks | 12,000-25,000 |
| First AI app project | Scoped build from assessment or strategy output | 2-10 weeks | 25,000-100,000 |
| Ongoing support | Monitoring, iteration, fixes and training | Monthly | From 2,500 per month for 10 hours |
Source: Fact bank
Who is behind Paloren
Paloren is co-founded by Aaron Agius and Alex Agius. Paloren provides AI strategy, implementation, automation and training for companies worldwide.
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Which AI applications can Paloren build?
The Paloren service list covers the full range of applied AI software. Custom apps are the core: purpose built tools that combine language models, your data and your business rules to complete a defined job. AI agents go further, taking multi-step tasks such as research, triage or follow-up and executing them with limited supervision. AI voice agents and receptionists answer calls, capture details, qualify callers and route conversations, which suits booking heavy and support heavy operations. Chatbots and assistants give staff and customers accurate answers drawn from your own knowledge rather than the open internet. Workflow automation and integrations connect the tools you already use so information stops living in silos. CRM implementation with AI embeds intelligence into pipeline, follow-up and reporting. The company brain is the deepest build, a unified knowledge layer that lets people across the business query documents, decisions and data in plain language. Around every build sit AI governance, readiness assessment and team AI training, because software only creates value when people trust it and know how to use it.
- Custom apps, AI agents, voice agents, chatbots and company brain systems
- Workflow automation and CRM implementation with AI included
- Governance, readiness assessment and team training around every build
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How long does it take to build a custom AI app?
Timelines at Paloren are scoped honestly rather than quoted optimistically. A first project generally runs between USD 25,000 and 100,000 and lands within two to ten weeks, with the range driven by how many systems need connecting and how much data preparation is required. Narrower builds move faster: workflow automation projects typically take three to eight weeks, chatbots and voice agents four to eight weeks, and AI agents six to ten weeks. Larger systems take longer for good reason. A company brain, which unifies knowledge across an organisation, runs eight to twelve weeks. CRM implementation with AI runs four to ten weeks depending on migration and integration depth. Custom apps start from USD 40,000, with schedule set at scoping once the integration surface is known. Two things protect the timeline. First, readiness assessment before a build surfaces data problems while they are cheap to fix. Second, short development cycles mean you review working software early instead of waiting months for a reveal. Paloren would rather promise ten honest weeks than six hopeful ones.
- First projects: USD 25,000 to 100,000 across two to ten weeks
- Automation in three to eight weeks, agents in six to ten
- Company brain builds run eight to twelve weeks
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How much does AI app development cost?
Paloren publishes ranges so you can plan before the first call. A first project sits between USD 25,000 and 100,000 over two to ten weeks. Custom apps begin at USD 40,000. Beyond that, each service carries its own band: AI agents run USD 40,000 to 90,000 over six to ten weeks, voice agents and receptionists USD 25,000 to 60,000 over four to eight weeks, chatbots USD 20,000 to 50,000 over four to eight weeks, workflow automation USD 15,000 to 60,000 over three to eight weeks, CRM implementation with AI USD 20,000 to 80,000 over four to ten weeks, and the company brain USD 60,000 to 150,000 over eight to twelve weeks. Entry points cost less. AI readiness assessment starts at USD 8,000 over two to three weeks, and AI strategy runs USD 12,000 to 25,000 over three to four weeks. Ongoing support starts at USD 2,500 per month for ten hours. Where your build lands inside a range depends on integration count, data condition, user scope and how autonomous the AI needs to be. Scoping turns the band into a fixed number.
- First project range: USD 25,000 to 100,000 over two to ten weeks
- Custom apps from USD 40,000, scoped to a fixed number
- Readiness assessment from USD 8,000 and strategy from USD 12,000
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What can a Paloren AI app connect to?
An AI app creates value through what it can reach. Paloren builds integrations as a first class part of every project rather than an afterthought. Common connections include CRM platforms, so calls and conversations write themselves into records automatically; document stores, so the app reads contracts, policies and reports directly; communication tools, so drafts and alerts land where your team already works; and internal databases, so answers reflect live operational data. The company brain takes integration furthest, indexing knowledge from across the business into one queryable layer. Workflow automation ties these connections together, moving information between systems and removing the copying and pasting that slows teams down. Paloren's AI work began inside Louder, the growth agency founded by Aaron Agius, where reporting, CRM automation, call analysis and content systems were built and run on real operations before Paloren existed. That history matters for integration work, because it was earned on live systems with real consequences rather than in demonstrations. Every connection is mapped, tested and documented, with permissions and audit trails configured before launch.
- CRM, document stores, communication tools and internal databases
- Company brain unifies business knowledge into one queryable layer
- Integration experience from Louder: reporting, CRM automation, call analysis, content systems
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How does Paloren handle governance in custom AI apps?
Governance decides whether an AI app can be trusted with real work. Paloren treats it as a build requirement, not a document delivered at the end. Access controls define who can ask what, so sensitive records stay visible only to the roles that need them. Audit trails record what the app did, which sources it used and who approved each action, creating accountability when decisions are reviewed. Human checkpoints sit at the steps where judgment matters most: an app might draft everything, but a person approves anything that leaves the building. Data handling rules specify what the AI may read, retain and pass to models, which matters for regulated information. Testing probes edge cases deliberately, including unusual inputs and attempts to push the system off script. AI governance is a listed Paloren service in its own right, available for businesses that need policies and controls assessed or built separately from a development project. The standard is simple: an app should be able to explain itself, and its behaviour should match what was agreed at scoping.
- Role based access controls and complete audit trails
- Human checkpoints at decisions that carry consequences
- AI governance available as a standalone service
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Who builds the apps at Paloren?
The people behind Paloren spent two decades inside businesses such as IBM, Ford, LG, Unilever, Jaguar and Chelsea FC, building and operating systems where mistakes were visible and expensive. That background shapes how apps get built here: for environments with real users, real deadlines and real data. Aaron Agius co-founded the company alongside Alex Agius. Before Paloren, he founded Louder, a growth agency, and spent 15 years building marketing, data and growth systems. He wrote Faster, Smarter, Louder, published in 2019, and has published with Entrepreneur, Salesforce, HubSpot and the Forbes Agency Council. The AI work that became Paloren started inside Louder, where reporting, CRM automation, call analysis and content systems ran on live operations before the company existed. That lineage means the person shaping your app has personally built systems that had to work every day, not only in presentations. When you brief a project, you speak with the people who will deliver it. The same directness carries through development, training and ongoing support, so decisions happen close to the work rather than in handovers.
- Co-founded by Aaron Agius and Alex Agius
- Two decades of operational experience inside IBM, Ford, LG, Unilever, Jaguar and Chelsea FC
- Author of Faster, Smarter, Louder with published work across Entrepreneur, Salesforce, HubSpot and Forbes Agency Council
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Why build a custom AI app instead of buying software?
Off the shelf AI tools win on speed and lose on fit. They assume workflows that resemble their templates, store your data on shared terms and stop where their feature list stops. A custom AI app inverts that trade. Paloren shapes the software around your actual process, including the exceptions and approvals that generic products flatten away. Integration reaches the systems you already run rather than the ones the vendor supports. Governance matches your obligations instead of a standard settings page. Ownership matters too: the logic, prompts and workflows reflect your business and can evolve as it changes, guided by support from USD 2,500 per month for ten hours. Build cost is the honest objection. A custom app starts at USD 40,000, and a first project runs USD 25,000 to 100,000. The way to test the maths is sequencing: start with an AI readiness assessment from USD 8,000 over two to three weeks, or an AI strategy engagement at USD 12,000 to 25,000 over three to four weeks, then build where the numbers justify it. Many businesses end up running custom AI where differentiation matters and standard tools everywhere else.
- Fit to your real workflows, including exceptions and approvals
- Integration with the systems you already run
- Sequenced entry: assessment from USD 8,000, strategy from USD 12,000, then build
What you take forward
What you get
A working custom AI application deployed in your environment
Integrations with your CRM, data sources and communication tools
Team AI training and documentation for every user level
Governance controls, access permissions and audit trails
A support plan covering monitoring and iteration
- 01
Discovery and scoping
Map the workflow, systems, data sources and success measures. Agree the scope, timeline and fixed investment before any build begins.
- 02
Readiness and architecture
Assess data quality and access, select models and design integrations, permissions and governance for the app.
- 03
Build and integrate
Develop in short cycles, connect your CRM, documents and tools, and review working software early with your team.
- 04
Test with real work
Pilot the app on live tasks, probe edge cases and refine behaviour until outputs meet the agreed standard.
- 05
Launch and train
Deploy to your environment, deliver team AI training and documentation, and hand over governance controls.
- 06
Support and iterate
Monitor performance, fix issues and add capabilities with support from USD 2,500 per month for ten hours.
| Stage | What it changes |
|---|---|
| Discovery and scoping | Map the workflow, systems, data sources and success measures. Agree the scope, timeline and fixed investment before any build begins. |
| Readiness and architecture | Assess data quality and access, select models and design integrations, permissions and governance for the app. |
| Build and integrate | Develop in short cycles, connect your CRM, documents and tools, and review working software early with your team. |
| Test with real work | Pilot the app on live tasks, probe edge cases and refine behaviour until outputs meet the agreed standard. |
| Launch and train | Deploy to your environment, deliver team AI training and documentation, and hand over governance controls. |
| Support and iterate | Monitor performance, fix issues and add capabilities with support from USD 2,500 per month for ten hours. |
Which workflow should AI handle first?
Start with an AI readiness assessment from USD 8,000 over two to three weeks, or bring a workflow straight to scoping. You will leave the first conversation with a recommended app, timeline and investment range.
Reply from the team within one business day. No deck, no technical brief needed.
Before we begin
Questions we get asked, answered with numbers
What does an AI app development company do?
An AI app development company designs and builds software where artificial intelligence performs operational work: reading documents, answering questions, handling calls, automating workflows and updating systems. Paloren builds these applications end to end, from strategy and architecture through development, integration, governance and training, for businesses worldwide. The goal is software shaped around your workflows rather than your team reshaping itself around generic tools.
How much does it cost to build an AI app with Paloren?
A first project ranges from USD 25,000 to 100,000 over two to ten weeks, and custom apps start at USD 40,000. Narrower services carry their own bands: automation from USD 15,000, chatbots from USD 20,000, voice agents from USD 25,000 and agents from USD 40,000. Scoping turns the relevant range into a fixed number once integrations, data condition and user scope are known.
How long does AI app development take?
A first project takes two to ten weeks depending on scope. Workflow automation typically runs three to eight weeks, chatbots and voice agents four to eight weeks, AI agents six to ten weeks, CRM implementation with AI four to ten weeks and the company brain eight to twelve weeks. Short development cycles mean you review working software early rather than waiting for a single reveal at the end.
Can Paloren integrate an AI app with our existing systems?
Yes. Integration is a core part of every Paloren build, covering CRM platforms, document stores, communication tools and internal databases. Workflow automation connects the tools you already use so information moves without manual copying. The company brain goes furthest, indexing knowledge from across the business into one queryable layer. Every connection is mapped, tested and documented, with permissions and audit trails configured before launch.
Do we need clean data before starting an AI app project?
Not perfectly clean, but it needs to be reachable and understood. The AI readiness assessment, from USD 8,000 over two to three weeks, reviews your data, tools and workflows and flags gaps while they are still cheap to fix. Many builds include a data preparation phase for exactly this reason. Scoping identifies what the app needs, and the plan accounts for getting it into shape.
What support is available after an app launches?
Ongoing support starts at USD 2,500 per month for ten hours, covering monitoring, fixes and iteration as your usage grows. Apps rarely stay still: models improve, workflows shift and new capabilities become worth adding. Support also covers governance reviews so access and audit controls keep matching how the business operates. Team AI training can continue alongside, helping new staff use the system well from their first week.
Who will actually build our application?
The people behind Paloren spent two decades inside businesses such as IBM, Ford, LG, Unilever, Jaguar and Chelsea FC. Co-founders Aaron Agius and Alex Agius lead the work. Aaron authored Faster, Smarter, Louder and has published with Entrepreneur, Salesforce, HubSpot and the Forbes Agency Council. The AI systems that became Paloren, including reporting, CRM automation, call analysis and content systems, were built and run inside Louder, the growth agency he founded.
Does Paloren build voice agents and chatbots as well as apps?
Yes. AI voice agents and receptionists, priced from USD 25,000 to 60,000 over four to eight weeks, answer calls, capture details and route conversations. Chatbots and assistants, from USD 20,000 to 50,000 over four to eight weeks, answer questions using your own knowledge. Both can be built standalone or combined into a larger custom application alongside automation, CRM integration and the company brain.
Where does Paloren work with businesses?
Paloren serves businesses worldwide. Engagements are organised around your team and systems rather than a location, so access to the same strategy, development and training does not depend on where you operate. Country information is described at a national level. Whether your people sit in one office or across many, delivery follows the same pattern: scoping, short build cycles, integration, training and ongoing support.
Which workflow should AI handle first?