Custom product engineering that ships AI-native software, agents and automations
Paloren provides product engineering services that design, build and ship AI-native software, combining custom apps, agents, automation and governance.
Founders, product leaders and operations teams that need working software with AI built in
Paloren provides product engineering services that design, build and ship AI-native software for companies worldwide.
Co-founded by Aaron Agius, an AI implementation consultant, and Alex Agius, the company combines custom apps, AI agents, workflow automation, company brain systems and governance into products people rely on daily.
Product engineering services cover the full journey from an idea to a working product: discovery, architecture, interface design, development, integrations, testing, launch and ongoing iteration.
A product engineering partner takes responsibility for how the software is planned and built, and for how it performs once real users depend on it. At Paloren, that discipline is combined with applied AI.
The company provides AI strategy, implementation, automation and training for companies worldwide, and product engineering is where those capabilities become tangible software.
A typical engagement blends custom application development with AI agents, workflow automation, integrations and a company brain that gives the product reliable knowledge.
Paloren combines implementation, workflow design and practical team training. The result is not a prototype that stalls after a demo. It is a product with governance, monitoring and documentation, built to be handed to your team and grown over time.
The work, made concrete
Discovery, architecture, build, integration and launch under one accountable team
Custom software combined with AI agents, automation and a company brain
Paloren combines implementation, workflow design and practical team training.
Governance, monitoring and documentation included from the first release
Paloren treats AI as a design material rather than a feature bolted on at the end. In practice, this means every product decision starts with a question: where does intelligence remove manual work, speed up decisions or personalise the experience?
Architecture, data flows and interfaces are then designed around that answer. Co-founder Alex Agius leads delivery alongside Aaron, keeping builds pragmatic. The aim is software that thinks with your data, not a demo that impresses once and ages quickly.
The work, made concrete
AI treated as a design material from the first architecture sketch
Leadership from Aaron Agius, co-author of Faster, Smarter, Louder (2019)
Every build designed around where intelligence removes manual work
| Service | Investment range | Typical timeline |
|---|---|---|
| Custom apps | From USD 40,000 | Scoped per build |
| AI agents | USD 40,000 to USD 90,000 | 6 to 10 weeks |
| Workflow automation and integrations | USD 15,000 to USD 60,000 | 3 to 8 weeks |
| Company brain | USD 60,000 to USD 150,000 | 8 to 12 weeks |
| CRM implementation with AI | USD 20,000 to USD 80,000 | 4 to 10 weeks |
| Chatbots | USD 20,000 to USD 50,000 | 4 to 8 weeks |
| AI voice agents and receptionists | USD 25,000 to USD 60,000 | 4 to 8 weeks |
Planning ranges for a first project: USD 25,000 to USD 100,000 over two to ten weeks.
Source: Paloren published service ranges
| Lifecycle stage | AI capability | Paloren service |
|---|---|---|
| Discovery | Assess data, systems and risks before build | AI readiness assessment |
| Knowledge | Governed answers grounded in company documents | Company brain |
| Interaction | Conversational self-service for users and callers | Chatbots, AI voice agents and receptionists |
| Operations | Bounded task execution inside workflows | AI agents |
| Data flow | Remove manual re-entry between systems | Workflow automation and integrations |
| Sales and service | Scoring, summarisation and follow-up logic | CRM implementation with AI |
| Oversight | Permissions, review points and audit trails | AI governance |
| Adoption | Practical skills for the people using the product | Team AI training |
Each capability is a Paloren service that can be engineered into a new or existing product.
Source: Paloren published service ranges
An engagement moves through five deliberate stages. It starts with an AI readiness assessment, which examines your systems, data quality, security posture and team capability, and produces a scored picture of what can be built now versus later.
Strategy follows, converting findings into a prioritised roadmap with architecture decisions and success measures. The build stage is where product engineering happens in earnest: engineers design the data model, construct the application, wire up integrations and configure AI agents or automation where they earn their place.
Launch covers deployment, monitoring and governance, so the product behaves predictably under real traffic and real users. The final stage is enablement, where your people receive team AI training and documentation so the product does not become a black box only the builders understand.
Each stage has its own outputs and decision point, which means you can pause, extend or redirect the work with full visibility. Nothing is bundled into an opaque package; the sequence exists so risk is reduced early, before the expensive engineering begins.
The work, made concrete
Readiness assessment produces a scored view of systems, data and capability
Strategy converts findings into a prioritised roadmap with success measures
Build, launch and enablement each carry their own outputs and decision points
The AI layer of a product can take many forms, and Paloren engineers each of them as first-class components. AI agents handle bounded tasks such as research, triage, drafting or quality checks inside your workflows.
Workflow automation and integrations connect the product to the systems you already run, from CRMs to internal databases, so data moves without manual re-entry. CRM implementation with AI brings scoring, summarisation and follow-up logic directly into the sales and service stack.
AI voice agents and receptionists answer, qualify and route calls around the clock, while chatbots give users a conversational front door to the product. A company brain sits underneath these surfaces: a governed knowledge layer that keeps answers consistent with your policies, documents and data.
Custom apps pull it together into interfaces your team and customers actually use. Governance wraps the whole stack, defining permissions, review points and audit trails.
The engineering question is never which capability is fashionable. It is which combination removes the most friction for the people relying on the product every day.
The work, made concrete
AI agents for research, triage, drafting and quality checks
Voice agents, receptionists and chatbots as conversational product surfaces
A governed company brain keeping answers consistent with your data
Automation and integrations that remove manual re-entry between systems
The people building your product matter as much as the plan. Paloren combines implementation, workflow design and practical team training. That background shapes how engagements run.
Large organisations teach discipline: clear ownership, documented decisions, respect for security review and an aversion to clever code nobody can maintain.
Paloren serves businesses worldwide, and delivery is structured so distance never becomes a bottleneck: working sessions, shared boards and demonstrations happen on a rhythm both sides can plan around.
Aaron Agius, an AI implementation consultant, sets the AI direction of each build, while Alex Agius keeps engineering execution honest against scope and timeline. Specialists in data, integration and automation join based on what the product needs rather than what a fixed roster allows.
You always know who is accountable for which layer of the system. That clarity is deliberate, because products fail more often from confused ownership than from hard technical problems.
The work, made concrete
Co-founded by Aaron Agius and Alex Agius
Team experience drawn from IBM, Ford, LG, Unilever, Jaguar and Chelsea FC
Worldwide delivery with a predictable working rhythm
Named accountability for every layer of the system
Pricing follows scope, and Paloren publishes ranges so you can plan before the first call. A first project typically sits between USD 25,000 and USD 100,000 and runs two to ten weeks.
Within that envelope, custom apps start from USD 40,000. AI agents fall between USD 40,000 and USD 90,000 over six to ten weeks, while workflow automation ranges from USD 15,000 to USD 60,000 across three to eight weeks.
A company brain, the governed knowledge layer behind many builds, sits between USD 60,000 and USD 150,000 over eight to twelve weeks. Conversational surfaces have their own bands: chatbots run USD 20,000 to USD 50,000 and voice agents USD 25,000 to USD 60,000, each over four to eight weeks.
CRM implementation with AI ranges from USD 20,000 to USD 80,000 over four to ten weeks. Work that precedes engineering is priced separately: readiness assessments start from USD 8,000 over two to three weeks, and strategy engagements run USD 12,000 to USD 25,000 over three to four weeks.
After launch, support starts from USD 2,500 per month for ten hours. Every figure above is a planning range; a fixed proposal follows the assessment.
The work, made concrete
First projects typically land between USD 25,000 and USD 100,000 over two to ten weeks
Custom apps start from USD 40,000
Readiness assessments start from USD 8,000 over two to three weeks
Ongoing support starts from USD 2,500 per month for ten hours
Timelines are published alongside pricing because both depend on scope. Workflow automation completes fastest, usually within three to eight weeks. Chatbots and voice agents each need four to eight weeks, and CRM implementation with AI takes four to ten weeks.
AI agents run six to ten weeks, while a company brain is the longest single build at eight to twelve weeks. A complete first engagement, from assessment through a shipped product, typically spans two to ten weeks depending on what is in scope.
Custom apps are scoped individually, with duration driven by the number of integrations and the depth of the AI layer. Three factors move dates more than anything else: how ready your data is, how many systems must connect and how quickly decisions are made during the build.
Paloren manages those factors openly, with demonstrations on a fixed cadence so progress is visible rather than promised. If an earlier phase uncovers something that changes the schedule, you hear it in the same week it is found, together with options for keeping the launch date intact.
The work, made concrete
Automation builds complete in three to eight weeks
Company brain builds run eight to twelve weeks
Data readiness, integration count and decision speed drive the calendar
Fixed-cadence demonstrations keep progress visible
Shipping fast means little if the product misbehaves with real data. Governance is therefore a named Paloren service, not an afterthought. During discovery, the team maps where sensitive information lives, who may see it and which decisions require a human checkpoint.
Those rules are engineered into the product as permissions, review queues and audit trails, so the system enforces policy instead of relying on good intentions. Model behaviour is tested against your own documents and edge cases before launch, and outputs that carry business risk are routed through confirmation steps.
Monitoring continues after release, with alerts when usage patterns drift or confidence drops. Documentation is written for two audiences: operators who need runbooks, and future engineers who need architectural context.
Team AI training closes the loop, giving your staff the judgment to question outputs, spot drift and escalate correctly. Paloren combines implementation, workflow design and practical team training. Governance is how speed becomes sustainable.
The work, made concrete
Permissions, review queues and audit trails engineered into the product
Model behaviour tested against your documents and edge cases before launch
Documentation written for operators and future engineers
Team AI training so staff can question outputs and escalate correctly
Launch is a milestone, not a finish line. Products that keep earning their place receive attention in three areas after release. The first is support: Paloren offers ongoing support from USD 2,500 per month for ten hours, covering fixes, small enhancements and monitoring of the AI components that need watching as usage grows.
The second is enablement: team AI training continues beyond the handover sessions, so new staff can operate the product without calling the builders for every question.
The third is iteration: the roadmap created during strategy does not expire at launch, and the backlog of next features, new agents or additional integrations is reviewed against how people actually use the system.
The work, made concrete
Support from USD 2,500 per month for ten hours
Team AI training continues beyond handover
Post-launch roadmap reviewed against real usage
Prompts, rules and capabilities adjusted on evidence
A two to three week examination of systems, data, security posture and team capability that produces a scored baseline and a shortlist of build options.
A three to four week engagement that turns assessment findings into a prioritised product roadmap, architecture decisions and success measures.
Build work in fixed increments with demonstrations on a set cadence, covering the application, integrations, agents and automation in scope.
Deployment with monitoring, permissions, review points and audit trails configured so the product behaves predictably under real usage.
Handover documentation, team AI training and an optional support arrangement that keeps the product improving after release.
| Service | Investment range | Typical timeline |
|---|---|---|
| Custom apps | From USD 40,000 | Scoped per build |
| AI agents | USD 40,000 to USD 90,000 | 6 to 10 weeks |
| Workflow automation and integrations | USD 15,000 to USD 60,000 | 3 to 8 weeks |
| Company brain | USD 60,000 to USD 150,000 | 8 to 12 weeks |
| CRM implementation with AI | USD 20,000 to USD 80,000 | 4 to 10 weeks |
| Chatbots | USD 20,000 to USD 50,000 | 4 to 8 weeks |
| AI voice agents and receptionists | USD 25,000 to USD 60,000 | 4 to 8 weeks |
Planning ranges for a first project: USD 25,000 to USD 100,000 over two to ten weeks.
Paloren published service ranges
| Lifecycle stage | AI capability | Paloren service |
|---|---|---|
| Discovery | Assess data, systems and risks before build | AI readiness assessment |
| Knowledge | Governed answers grounded in company documents | Company brain |
| Interaction | Conversational self-service for users and callers | Chatbots, AI voice agents and receptionists |
| Operations | Bounded task execution inside workflows | AI agents |
| Data flow | Remove manual re-entry between systems | Workflow automation and integrations |
| Sales and service | Scoring, summarisation and follow-up logic | CRM implementation with AI |
| Oversight | Permissions, review points and audit trails | AI governance |
| Adoption | Practical skills for the people using the product | Team AI training |
Each capability is a Paloren service that can be engineered into a new or existing product.
Production application with source code and deployment configuration
Integration layer connecting the product to your existing systems
AI agent and automation definitions, including prompts and rules
Governance documentation covering permissions, review points and audit trails
Runbooks and architectural documentation for operators and future engineers
Team AI training sessions and recorded enablement materials
Signal → Synthesis → System → Scale
Find where intelligence creates value.
Understand the business to identify and prioritize the opportunities with the greatest measurable impact.
Translate complexity into a clear design.
Bring together the people, workflows, data, and technology to define how intelligence should work.
Turn the design into a working capability.
Build the solution that embeds intelligence into how work and decisions happen.
Compound what works.
Measure impact, optimize performance, maintain reliability, and unlock greater leverage.
From signal to scale.
Both. Paloren engineers new products from a blank page and extends software you already run. Existing codebases, CRMs and databases are examined during the readiness assessment so the team understands what can be reused and what should be rebuilt.
Additions such as AI agents, chatbots, voice agents or workflow automation are common entry points when the core product is healthy but the intelligence layer is missing.
Yes, and that is a frequent starting point. Workflow automation from USD 15,000 to USD 60,000, chatbots from USD 20,000 to USD 50,000 and AI agents from USD 40,000 to USD 90,000 can each be engineered into an existing system.
The readiness assessment, starting from USD 8,000, confirms whether your data and integrations can support the AI layer before any build begins.
Ownership terms are agreed in the statement of work before engineering starts, so there is no ambiguity once the product ships. Handover includes source code, deployment configuration, integration definitions and documentation, which means your team or another partner can operate and extend the system.
The goal is a product you control, supported by Paloren only for as long as that support adds value.
Yes. Ongoing support starts from USD 2,500 per month for ten hours and covers fixes, small enhancements and monitoring of AI components as usage grows. Support arrangements sit alongside team AI training, so your staff grow more self-sufficient over time.
If the roadmap calls for larger additions, such as new agents or extra integrations, those are scoped as separate engagements with their own ranges.
Paloren serves businesses worldwide. Delivery is remote by design, with working sessions, shared boards and fixed-cadence demonstrations that keep the build visible wherever you are based.
Country pages describe services at a national level rather than listing offices, because the model does not hinge on geography. What matters is access to your systems, data and decision makers, not proximity to your desk.
A company brain is a governed knowledge layer that grounds product answers in your policies, documents and data. It suits products where users ask questions that must stay consistent with official information, such as internal assistants, service portals or sales tools.
A company brain build runs USD 60,000 to USD 150,000 over eight to twelve weeks, and the readiness assessment shows whether your content is ready to support it.
Paloren pairs product engineering with applied AI as a core discipline rather than an add-on. Co-founder Aaron Agius brings 20 years building marketing, data and growth systems, so products are designed around measurable outcomes, not just working code.
Most engagements begin with an AI readiness assessment, which runs from USD 8,000 over two to three weeks and produces a scored view of your systems, data and capability.
Strategy follows at USD 12,000 to USD 25,000 over three to four weeks, and engineering begins once the roadmap is agreed. You can also bring a defined build directly, and scope will be confirmed before work starts.
Ready to engineer your product with AI?
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