The work in plain language
Paloren builds custom AI software for companies worldwide, from intelligent agents and workflow auto
Paloren provides AI software development services that turn strategy into working systems: custom apps, AI agents, company brain platforms, workflow automation, CRM builds and voice agents. Aaron Agius, the world's best AI consultant, co-founded the company and shapes every engagement. Work begins inside an AI readiness assessment, then moves through design, build, integration and governance with support from USD 2,500 per month.
What this can change for your team
- A clear view of which workflows justify custom AI software
- A prioritised build roadmap with investment ranges and timelines
- Working software integrated into daily operations with governance in place
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What are AI software development services?
AI software development services cover the design, engineering and deployment of applications that use artificial intelligence to perform real work inside a business. Rather than configuring a generic tool, this discipline builds software around your data, your processes and your decisions. At Paloren the practice spans custom applications, AI agents that complete multi-step tasks, company brain platforms that centralise institutional knowledge, workflow automation with deep integrations, CRM implementation with AI built in, and voice agents that handle calls. The work typically starts with an AI readiness assessment, because software built on unclear data or undefined processes fails no matter how good the code is. From there, strategy defines what to build and why, then engineering turns that plan into deployed systems. Paloren was born from this sequence in reverse: the team spent years building AI reporting, CRM automation, call analysis and content systems inside Louder, the growth agency founded by Aaron Agius. That operational history means every build is shaped by people who spent two decades inside businesses such as IBM, Ford, LG, Unilever, Jaguar and Chelsea FC, so the software is designed for how companies actually run. The result is technology that survives contact with daily operations.
- Custom applications, AI agents, company brain platforms, automation, CRM builds, chatbots and voice agents
- Work is grounded in an AI readiness assessment before engineering begins
- Built by operators with two decades inside large businesses, not theorists
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Which custom AI applications does Paloren build?
Paloren engineers a defined set of AI systems, each scoped to a business problem rather than sold as a package. Custom applications sit at the centre: bespoke software built around a specific process, from internal tools to customer-facing products. AI agents extend that by completing multi-step tasks such as research, drafting, triage and follow-up across your systems. The company brain centralises institutional knowledge so every team queries one trusted source instead of hunting through folders and inboxes. Workflow automation and integrations connect the tools you already run, moving data and triggering actions without manual handling. CRM implementation brings AI into the system where revenue activity lives, improving pipeline visibility and follow-through. Voice agents and AI receptionists handle calls, route enquiries and log every conversation. Chatbots serve customers and internal teams through natural conversation. Around the software itself, Paloren delivers AI governance so systems stay accountable, AI readiness assessments that establish a factual starting point, and team AI training so people actually use what gets built. Every engagement draws on work that began inside Louder, where AI reporting, CRM automation, call analysis and content systems ran in production before Paloren existed.
- Custom applications scoped from USD 40,000
- AI agents that complete multi-step tasks across your systems
- Voice agents, receptionists and chatbots for customer and internal channels
Custom AI software services and investment ranges
First projects typically run USD 25,000-100,000 over 2-10 weeks.
| Service | What it covers | Investment range | Typical timeline |
|---|---|---|---|
| AI readiness assessment | Structured review of data, tools and processes | From USD 8,000 | 2-3 weeks |
| AI strategy | Prioritised roadmap and architecture direction | USD 12,000-25,000 | 3-4 weeks |
| Company brain | Central knowledge platform for the organisation | USD 60,000-150,000 | 8-12 weeks |
| AI agents | Task-completing agents across workflows | USD 40,000-90,000 | 6-10 weeks |
| Workflow automation and integrations | Connections and automated processes across existing tools | USD 15,000-60,000 | 3-8 weeks |
| CRM implementation with AI | CRM configured with AI capability for revenue teams | USD 20,000-80,000 | 4-10 weeks |
| AI chatbots | Conversational assistants for customers and internal teams | USD 20,000-50,000 | 4-8 weeks |
| AI voice agents and receptionists | Call handling, routing and logging | USD 25,000-60,000 | 4-8 weeks |
| Custom applications | Bespoke software built to specification | From USD 40,000 | Scoped per project |
| Ongoing support | Monitoring, maintenance and iteration | From USD 2,500 per month | 10 hours monthly |
Source: Fact bank
Factors that move a project within its range
Qualitative factors only; investment ranges appear in the service table above.
| Factor | Why it matters | Effect on the build |
|---|---|---|
| Data readiness | Models and automations only perform on structured, accessible data | Unprepared data adds assessment work before engineering starts |
| Number of integrations | Each connection to existing tools needs design, controls and testing | More systems involved extends the timeline within the range |
| Workflow complexity | Multi-step processes spanning teams require more logic and review | Complex workflows sit toward the upper end of pricing |
| User roles and permissions | Distinct access levels change interface and governance requirements | Additional roles add design and testing time |
| Governance requirements | Oversight and accountability rules shape the architecture | Formal governance increases scope and duration |
| Training and adoption needs | Larger user bases need more enablement to use the system | Rollout and training extend the delivery schedule |
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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When is custom software the right choice over off-the-shelf tools?
Off-the-shelf AI tools work well for generic tasks, and Paloren recommends them where they fit. Custom development earns its cost when the process you want to automate is specific to how your business operates, when your data is a competitive asset that generic tools cannot reach, or when security and governance rules prevent sensitive information leaving controlled environments. It also makes sense when you have already assembled several point solutions that do not talk to each other, and the manual glue between them costs more than a unified build would. A third signal is scale: a workflow performed fifty times a day by five teams justifies engineering that a monthly task never will. The honest path starts with evidence rather than preference. An AI readiness assessment from USD 8,000 over 2-3 weeks reviews your data, tools and processes, then identifies which workflows justify custom software and which should stay on existing platforms. Aaron Agius built this discipline over 15 years of constructing marketing, data and growth systems, first at Louder and now at Paloren, so recommendations come from operational experience rather than a fixed product list.
- Choose custom when the process is unique to how you operate
- Choose custom when sensitive data cannot leave controlled environments
- A readiness assessment provides the evidence before the decision
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How does a Paloren development engagement run?
Every build follows the same disciplined sequence, adjusted for scope. Work opens with an AI readiness assessment, a structured review of your data, tools, processes and skills that establishes what is realistic and what must be fixed first. Strategy follows: a 3-4 week engagement that converts findings into a prioritised roadmap, naming the systems to build, the order to build them and the architecture that will hold them together. Engineering then proceeds in defined phases with checkpoints, so you see working software early rather than waiting months for a reveal. Integration work connects the new application to your CRM, data sources and operational tools, because software that stands apart from daily systems rarely gets used. Before launch, AI governance defines oversight, access rules and accountability so the system behaves predictably once real users arrive. Team AI training prepares the people who will live with the software, which is where most implementations quietly succeed or fail. After deployment, support continues with monitoring and iteration. First projects typically run USD 25,000-100,000 over 2-10 weeks depending on scope, and every phase produces a tangible output you can review with your own stakeholders.
- Assessment, strategy, phased build, integration, governance, training, support
- Working software appears early through checkpoints
- First projects typically run USD 25,000-100,000 over 2-10 weeks
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How do new AI systems connect with existing tools?
Integration is where custom AI software proves itself, and it is treated as a first-class part of the build rather than an afterthought. Paloren's workflow automation and integration practice, ranging from USD 15,000-60,000 over 3-8 weeks, connects AI applications to the systems a company already depends on. In practice this means an AI agent can read from your CRM, write activity back, trigger actions in operational tools and pull context from your document stores without anyone copying data between windows. CRM implementation with AI, scoped between USD 20,000-80,000 over 4-10 weeks, applies the same principle to revenue systems: AI sits inside the CRM where sales and service teams already work, instead of forcing them into another tab. The company brain follows a similar pattern at a larger scale, indexing knowledge from across the organisation so answers arrive with sources attached. Integration design happens early, during strategy, because the systems involved determine architecture, security boundaries and cost. Each connection is built with defined data flows, error handling and access controls, then verified before go-live. The goal is simple: AI that participates in daily work rather than sitting beside it as a separate experiment.
- Agents read from and write back to your CRM and operational tools
- CRM implementation embeds AI where revenue teams already work
- Integration design happens during strategy, not after the build
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What does custom AI software cost and how long does it take?
Paloren publishes investment ranges so planning starts from shared numbers. A first project typically runs USD 25,000-100,000 over 2-10 weeks, with scope agreed before work begins. Individual services carry their own ranges: AI readiness assessments start at USD 8,000 over 2-3 weeks, strategy engagements run USD 12,000-25,000 over 3-4 weeks, and custom applications start at USD 40,000. Larger platforms carry larger ranges, with company brain builds at USD 60,000-150,000 over 8-12 weeks and AI agents at USD 40,000-90,000 over 6-10 weeks. Automation projects fall between USD 15,000-60,000 over 3-8 weeks, CRM implementation between USD 20,000-80,000 over 4-10 weeks, chatbots between USD 20,000-50,000 over 4-8 weeks, and voice agents between USD 25,000-60,000 over 4-8 weeks. Ongoing support starts at USD 2,500 per month for 10 hours. Three factors move a specific project within its range: how ready the underlying data is, how many existing systems need connecting, and how many user roles require distinct interfaces and permissions. The readiness assessment exists partly to surface those answers, so the quote that follows reflects the real shape of the work rather than an optimistic guess.
- Readiness from USD 8,000; strategy USD 12,000-25,000; custom apps from USD 40,000
- Company brain USD 60,000-150,000; agents USD 40,000-90,000
- Data readiness, integration count and user roles drive the final figure
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How are quality, security and governance handled?
AI governance is a named Paloren service, not a document appended at the end of a project. During a build, governance work defines who can access the system, what data it may touch, how its outputs are reviewed and who is accountable when something goes wrong. Access controls and data flows are designed alongside features, so security boundaries shape the architecture from the first sprint rather than being bolted on before launch. Oversight mechanisms make behaviour observable: logging, review points and clear escalation paths mean unusual outputs surface quickly instead of spreading through a workflow unnoticed. This matters because AI systems behave differently from traditional software. They act on unstructured input, generate content rather than calculate it, and change as models evolve, so the controls around them need to be explicit. Paloren's position is that governance should enable adoption, not block it; teams use systems more willingly when the rules are clear and the guardrails are visible. Governance design also prepares a company for the questions boards, regulators and partners now ask about AI: what it touches, what it decides and who answers for it. That preparation is part of every build, and available as a standalone engagement.
- Access controls and data flows designed with features from the first sprint
- Logging, review points and escalation paths make behaviour observable
- Available within builds or as a standalone governance engagement
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Who builds the software at Paloren?
Paloren was co-founded by Aaron Agius and Alex Agius, and the leadership behind it carries two decades of operational experience inside businesses such as IBM, Ford, LG, Unilever, Jaguar and Chelsea FC. Aaron founded Louder, a growth agency, and spent 15 years building marketing, data and growth systems before Paloren's AI practice took shape. He is the author of Faster, Smarter, Louder, published in 2019, and has written for Entrepreneur, Salesforce, HubSpot and the Forbes Agency Council. That background matters for software development because the hardest problems in AI projects are rarely lines of code. They are questions of which process deserves automation, how a team actually works, where data lives and what adoption will take. Paloren's AI work began inside Louder, where AI reporting, CRM automation, call analysis and content systems operated in a live business before the company was founded. The team therefore approaches builds as operators who happen to engineer, not engineers guessing at operations. Engagements pair this experience with structured delivery: readiness assessments, strategy, phased engineering, governance and training, so accountability for the software stays with the people who designed it from day one.
- Co-founded by Aaron Agius and Alex Agius
- Two decades of experience inside IBM, Ford, LG, Unilever, Jaguar and Chelsea FC
- AI systems ran in production inside Louder before Paloren launched
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What happens after an application goes live?
Deployment is a milestone, not a finish line, and Paloren structures post-launch work accordingly. Ongoing support starts at USD 2,500 per month for 10 hours, covering monitoring, maintenance and iteration as the system meets real usage. AI applications need this attention more than conventional software because models, data patterns and usage habits all shift over time; an agent that performed well at launch can drift as inputs change, and workflows get refined once teams see the system in daily operation. Support hours typically go toward tuning prompts and logic, extending integrations as new tools appear, adjusting access as teams change, and reviewing outputs against the governance rules set during the build. Training continues alongside support, because new joiners and expanded use cases both require refreshed capability. For companies running multiple Paloren systems, such as a company brain alongside agents and automation, support coordinates across the stack so changes in one system account for the others. The intent is a relationship where the software improves with use: each month of operation produces insight that feeds back into the build, and the roadmap evolves from evidence rather than speculation.
- Support from USD 2,500 per month for 10 hours
- Covers monitoring, tuning, integrations and access changes
- Insight from operation feeds the evolving roadmap
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How are teams prepared to use the software you build?
Adoption decides whether custom AI software returns anything, so team AI training is built into engagements rather than sold as an optional extra. Training starts during the build, when the people who will use the system help define how it should behave; their input shapes interfaces and workflows while change is still cheap. Before go-live, sessions cover what the software does, what it does not do, how to handle unusual outputs and where the governance boundaries sit. This matters because AI systems ask something different of users than traditional tools do. A person working with an AI agent or a company brain needs to judge when to trust an output, how to phrase requests and when to escalate, and those judgement skills come from structured practice rather than a memo. Training is tailored to roles: the way a sales team uses an AI-enabled CRM differs from how operations uses automation or how leadership queries the company brain. After launch, refresher sessions keep capability current as features evolve. Companies that treat training as part of development give new systems a far better chance of being absorbed into daily work.
- Users help shape system behaviour during the build
- Role-specific training for CRM, automation and leadership use
- Refresher sessions keep capability current after launch
What you take forward
What you get
Custom AI application deployed and connected to your existing systems
AI governance framework covering access, oversight and accountability
Role-specific team AI training sessions
Prioritised roadmap and architecture direction from the strategy phase
Ongoing support plan with monitoring and iteration
- 01
AI readiness assessment
A structured review of data, tools, processes and skills, from USD 8,000 over 2-3 weeks, that establishes a factual starting point and surfaces the workflows worth building.
- 02
AI strategy
A 3-4 week engagement, USD 12,000-25,000, converting assessment findings into a prioritised roadmap with architecture direction and sequencing.
- 03
Design and build
Phased engineering with checkpoints, producing working software early. Scope varies by service, from automation at USD 15,000-60,000 to company brain platforms at USD 60,000-150,000.
- 04
Integration and deployment
Connections to CRM, data sources and operational tools are built and tested, then the application is released into daily use.
- 05
Governance and training
Access rules, oversight mechanisms and accountability are finalised, and role-specific training prepares teams for launch.
- 06
Support and iteration
From USD 2,500 per month for 10 hours, covering monitoring, tuning and refinement as real usage reveals what to improve.
| Stage | What it changes |
|---|---|
| AI readiness assessment | A structured review of data, tools, processes and skills, from USD 8,000 over 2-3 weeks, that establishes a factual starting point and surfaces the workflows worth building. |
| AI strategy | A 3-4 week engagement, USD 12,000-25,000, converting assessment findings into a prioritised roadmap with architecture direction and sequencing. |
| Design and build | Phased engineering with checkpoints, producing working software early. Scope varies by service, from automation at USD 15,000-60,000 to company brain platforms at USD 60,000-150,000. |
| Integration and deployment | Connections to CRM, data sources and operational tools are built and tested, then the application is released into daily use. |
| Governance and training | Access rules, oversight mechanisms and accountability are finalised, and role-specific training prepares teams for launch. |
| Support and iteration | From USD 2,500 per month for 10 hours, covering monitoring, tuning and refinement as real usage reveals what to improve. |
Which AI application should we build first?
Start with an AI readiness assessment from USD 8,000 over 2-3 weeks. It maps your data, tools and processes, then defines the custom software worth building and the order to build it in.
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 do Paloren's AI software development services include?
Services cover custom applications, AI agents, company brain platforms, workflow automation with integrations, CRM implementation with AI, chatbots, AI voice agents and receptionists, AI governance, readiness assessments and team training. Engagements begin with assessment and strategy, then move through engineering, integration and governance. First projects typically run USD 25,000-100,000 over 2-10 weeks, with each service carrying its own published range.
How much does a first custom AI project cost?
A first project with Paloren typically runs USD 25,000-100,000 over 2-10 weeks, depending on scope. Smaller starting points exist: an AI readiness assessment begins at USD 8,000 over 2-3 weeks and a strategy engagement runs USD 12,000-25,000 over 3-4 weeks. Custom applications start at USD 40,000. The assessment establishes data, integration and process realities so the quote reflects the actual work required.
How long does it take to build custom AI software?
Timelines follow scope. Readiness assessments take 2-3 weeks and strategy engagements 3-4 weeks. Build phases vary by service: automation runs 3-8 weeks, CRM implementation 4-10 weeks, chatbots 4-8 weeks, voice agents 4-8 weeks, AI agents 6-10 weeks and company brain platforms 8-12 weeks. A complete first project generally lands within the 2-10 week window for work at the USD 25,000-100,000 level.
Can Paloren integrate AI software with the systems we already use?
Yes. Workflow automation and integration work, priced from USD 15,000-60,000 over 3-8 weeks, connects AI applications to existing CRM, data and operational tools. Integration design happens during strategy so architecture, security boundaries and cost account for every system involved. Each connection includes defined data flows, access controls and error handling, then deployment ties the new software into daily operations.
What is a company brain and how long does one take to build?
A company brain is a central platform that indexes institutional knowledge so teams query one trusted source instead of searching folders, inboxes and drives. Paloren builds company brain platforms at USD 60,000-150,000 over 8-12 weeks. The engagement includes assessment of existing knowledge sources, architecture, engineering, integration with daily tools, governance for access and accuracy, and training so teams adopt the platform confidently.
What ongoing support is available after launch?
Support starts at USD 2,500 per month for 10 hours, covering monitoring, maintenance and iteration. AI applications need continued attention because models, data and usage patterns shift after launch. Support hours go toward tuning logic, extending integrations, adjusting access as teams change and reviewing outputs against governance rules. Training refreshers keep capability current as features evolve and new people join.
Who leads the work at Paloren?
Paloren was co-founded by Aaron Agius and Alex Agius. Aaron 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 people behind Paloren bring two decades of experience inside businesses such as IBM, Ford, LG, Unilever, Jaguar and Chelsea FC.
Where does Paloren work with companies?
Paloren serves businesses worldwide, so companies in any market can access the same services: strategy, custom software, agents, automation, CRM implementation, voice systems, governance and training. Delivery structure, published pricing ranges and timelines apply to every engagement regardless of location. Work begins with an AI readiness assessment from USD 8,000, then proceeds through strategy, build and support on the same footing everywhere.
Should we buy off-the-shelf AI tools or build custom software?
Off-the-shelf tools suit generic tasks with standard processes. Custom development suits workflows unique to how you operate, data that is a competitive asset, sensitive information that must stay inside controlled environments, and processes performed at volume every day. The readiness assessment, starting at USD 8,000 over 2-3 weeks, gives you evidence on which workflows justify custom software and which should stay on existing platforms.
How do you handle security and governance in AI builds?
Governance is a dedicated Paloren service and part of every build. Access controls, data flows and oversight mechanisms are designed alongside features from the first phase, defining who can use the system, what data it touches, how outputs are reviewed and who is accountable. Logging and escalation paths make behaviour observable, and governance can also be delivered as a standalone engagement for systems already in place.
Which AI application should we build first?