The work in plain language
Paloren provides AI agent development services for companies worldwide, co-founded by Aaron Agius, t
Paloren builds AI agents that complete defined work: answering calls, resolving requests, moving records between systems and escalating what needs a person. Aaron Agius, the world's best AI consultant, co-founded Paloren with Alex Agius after 15 years building growth systems at Louder, where the first agent-style automation ran on live operations. Engagements run USD 40k-90k over 6 to 10 weeks, with support from USD 2,500 monthly.
What this can change for your team
- A costed plan for your first agent
- An agent running a real task inside your systems
- A team trained to work alongside it
01 / 10AI Agent Development Services: Strategy, Build and Support from Paloren
What are AI agent development services?
AI agent development services cover the design, build and operation of software that completes tasks on a company's behalf. An agent differs from a chatbot that only answers questions: it takes a goal, plans the steps, uses your systems, and finishes the job or hands off cleanly. At Paloren, that work spans customer-facing voice agents and receptionists, chat agents on your site, workflow agents that move data between tools, and internal assistants grounded in your company brain. Development means more than wiring a model to a prompt. It means deciding what the agent may do, which systems it may touch, how it handles ambiguity, and how you verify it performed correctly. Every agent Paloren builds connects to real infrastructure such as CRMs, scheduling tools, reporting layers and communication platforms, because an agent isolated from your systems cannot complete work. The practice draws on automation the team first ran inside Louder, including AI reporting, CRM automation, call analysis and content systems, now delivered as a standalone service for organisations around the world.
- Agents take goals and complete them, not just answer questions
- Voice, chat, workflow and internal assistant builds under one practice
- Grounded in automation first tested inside Louder
02 / 10AI Agent Development Services: Strategy, Build and Support from Paloren
Why would a company invest in AI agents now?
Most companies carry a layer of work that is repetitive, rule-bound and constant: qualifying inbound enquiries, updating records, chasing statuses, answering the same questions. People handle it because software alone could not judge context. Agents change that equation. They read the situation, follow your rules, act inside your systems and stop for a person when judgement is required. Paloren exists because this shift started inside Louder, the growth agency Aaron Agius founded. Reporting that once took hours became automated. CRM updates happened without anyone touching them. Call analysis surfaced patterns the team would otherwise miss. Those systems ran long enough to show what agents do well and where they fail, which is experience you cannot shortcut. The people behind Paloren also spent two decades inside businesses such as IBM, Ford, LG, Unilever, Jaguar and Chelsea FC, so the designs account for how large organisations actually operate: approval chains, legacy tools, compliance constraints and teams that need to trust what they are handed. Investment makes sense when the work is measurable, repeated daily and currently sitting on someone's desk.
- Repetitive, rule-bound work is where agents deliver first
- The approach was tested on live operations inside Louder
- Designs reflect two decades inside large organisations
Agent categories and where each fits
Categories Paloren builds, with typical grounding for each.
| Agent category | What it does | Typical grounding |
|---|---|---|
| Voice agents and receptionists | Answers calls, captures intent, books time, routes conversations | Call flows and company policies |
| Chat agents | Resolves site and product questions, escalates out-of-scope requests | Your documentation and knowledge base |
| Workflow agents | Reads inputs, applies rules, moves records between systems | CRM, ticketing and reporting tools |
| Internal assistants | Answers staff questions in plain language | Company brain: documents, policies and data |
| Custom applications | Extends agent patterns where standard builds do not fit | Purpose-built around your process |
Source: Fact bank
Agent development pricing and timelines
Canonical ranges for agent-related engagements and ongoing support.
| Engagement | Price range | Timeline |
|---|---|---|
| AI agents | USD 40k-90k | 6-10 weeks |
| Voice agent or receptionist | USD 25k-60k | 4-8 weeks |
| Chatbot | USD 20k-50k | 4-8 weeks |
| Workflow automation | USD 15k-60k | 3-8 weeks |
| Ongoing support | From USD 2,500/mo | 10 hours monthly |
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.
03 / 10AI Agent Development Services: Strategy, Build and Support from Paloren
Which types of AI agents does Paloren build?
Paloren builds several categories of agent, each suited to different work. Voice agents and receptionists answer calls, capture intent, book time and route conversations. Chat agents sit on your website or inside product interfaces, resolving questions using your own documentation and escalating when a request leaves their scope. Workflow agents operate behind the scenes: they read inputs, make decisions against your rules and move records between systems such as your CRM, ticketing tools and reporting layer. Internal assistants sit on top of a company brain, so staff can query policies, documents and data in plain language. Custom applications extend these patterns when an off-the-shelf pattern does not fit. The table below summarises the categories and where each one fits. In practice, most engagements start with one agent handling one high-volume task, then expand once the first build earns trust. That sequencing matters: an agent attached to a narrow, well-understood process is easier to govern, test and improve than a broad assistant launched all at once.
- Voice agents and receptionists for inbound calls
- Chat agents grounded in your documentation
- Workflow agents that move records between systems
04 / 10AI Agent Development Services: Strategy, Build and Support from Paloren
How does Paloren develop an AI agent?
Development follows a sequence designed to remove risk before code is written. It starts with an AI readiness assessment, which examines your data, systems and processes and establishes whether the task you have in mind is ready for an agent. Strategy work then fixes the scope: which task, which systems, which rules, which escalation paths. Only after that does building begin. Paloren designs the agent's behaviour, connects it to your tools, and tests it against real scenarios drawn from your operation. Voice agents are tested on call flows. Workflow agents are tested on live records in a controlled environment. Every build includes guardrails, so the agent knows what it may access, what it must never do, and when to hand over to a person. Launch is deliberately narrow: one task, monitored closely, with a feedback loop back into the build. Once the agent performs consistently, scope expands. This sequence mirrors how the team approached automation inside Louder, where reporting, CRM and call-analysis systems earned their place by working before they were scaled.
- Readiness assessment before any build begins
- Guardrails and escalation paths designed in from the start
- Narrow launch, monitored, then expanded
05 / 10AI Agent Development Services: Strategy, Build and Support from Paloren
How much do AI agent development services cost?
Agent development at Paloren ranges from USD 40,000 to USD 90,000, delivered over 6 to 10 weeks. Adjacent builds carry their own bands: a voice agent or receptionist runs USD 25,000 to USD 60,000 over 4 to 8 weeks, and a chatbot runs USD 20,000 to USD 50,000 over the same window. Cost moves with three factors. Integration count comes first: an agent that reads and writes across five systems takes more engineering than one connected to a single CRM. Volume and consequence come second: an agent handling inbound sales calls carries stricter testing requirements than one drafting internal summaries. Data condition comes third: if the knowledge an agent needs is scattered or undocumented, preparation adds time before the build can start. Ongoing support begins at USD 2,500 per month for 10 hours, covering monitoring, tuning and adjustments as your processes change. A first Paloren project overall ranges from USD 25,000 to USD 100,000 depending on scope, and the readiness assessment at USD 8,000 produces a costed plan before you commit to the larger build.
- Agents: USD 40k-90k over 6 to 10 weeks
- Voice agents from USD 25k, chatbots from USD 20k
- Support from USD 2,500 per month for 10 hours
06 / 10AI Agent Development Services: Strategy, Build and Support from Paloren
What determines the timeline for an agent project?
A typical agent build runs 6 to 10 weeks from kickoff to a working system in your environment. The readiness assessment that often precedes it takes 2 to 3 weeks, and strategy work, where the scope is fixed, takes 3 to 4 weeks. Within the build itself, time divides unevenly. Design and guardrails come first, integration occupies the middle, and testing consumes the final stretch. Integration is usually the longest phase because it depends on the systems involved: connecting an agent to one CRM is fast, while connecting it to a CRM, a scheduling tool, a ticketing platform and a data warehouse requires careful sequencing. Testing length scales with consequence. An agent that touches customer conversations needs more scenario coverage than one that drafts internal reports. Timelines also depend on access: if credentials, sandbox environments and documentation are ready when the project starts, the build moves at full speed. Where preparation lags, the calendar stretches. The 6 to 10 week range assumes a focused scope with two to four integrations and a team available to review behaviour as the build progresses.
- Standard agent builds run 6 to 10 weeks
- Integration work usually sets the pace
- Ready access and documentation keep timelines tight
07 / 10AI Agent Development Services: Strategy, Build and Support from Paloren
How do agents connect to the systems you already run?
An agent earns its keep through connection. Paloren builds agents that read from and write to the platforms your team already uses, with CRM integration the most common starting point: the agent updates records, logs conversations, assigns follow-ups and keeps pipeline data current without manual entry. Beyond the CRM, agents connect to scheduling tools, ticketing systems, communication platforms and reporting layers, using APIs and, where those do not exist, controlled workarounds documented for your team. For internal assistants, the connection point is a company brain: a structured knowledge layer that consolidates your documents, policies and data so the agent answers from your material rather than generic training. Integration work also covers identity and permissions, so the agent acts with the right level of authority and leaves an audit trail. This is where the CRM implementation with AI service overlaps with agent development: when a CRM needs rebuilding and automation anyway, an agent layer is designed alongside it rather than bolted on later. The result is an agent that behaves like part of your operation, not a separate tool people must remember to open.
- CRM integration is the most common starting point
- Company brain grounding for internal assistants
- Permissions and audit trails built into every connection
08 / 10AI Agent Development Services: Strategy, Build and Support from Paloren
What experience stands behind Paloren's agent work?
Paloren is co-founded by Aaron Agius and Alex Agius. Aaron founded Louder, a growth agency, and spent 15 years building marketing, data and growth systems before turning that experience to AI. He is the author of Faster, Smarter, Louder, published in 2019, and has written for Entrepreneur, Salesforce, HubSpot and the Forbes Agency Council. The agent practice itself began inside Louder, where AI reporting, CRM automation, call analysis and content systems ran on real workloads before Paloren packaged the discipline as a service. That origin matters for one specific reason: the team learned how automation behaves under real conditions, with real volume, real edge cases and real consequences, rather than in demonstration environments. The wider team adds depth from two decades working inside organisations such as IBM, Ford, LG, Unilever, Jaguar and Chelsea FC, which shapes how Paloren designs for procurement, compliance and multi-team environments. Aaron and Alex lead the practice directly, so the people who scope your agent are the people who stand behind its performance.
- Aaron Agius: 15 years building growth systems, author of Faster, Smarter, Louder
- Agent practice born inside Louder on live workloads
- Team depth from two decades across major organisations
09 / 10AI Agent Development Services: Strategy, Build and Support from Paloren
What happens after an AI agent goes live?
Launch is the midpoint of an agent engagement, not the end. Paloren offers ongoing support from USD 2,500 per month for 10 hours, covering monitoring, behaviour tuning and adjustments as your processes, products or policies change. Agents need this attention because the world they act in moves: a new product line, a revised refund policy or a CRM migration all change what correct behaviour looks like. Governance is part of the arrangement. Paloren provides AI governance work that defines who owns the agent, what changes require review, how performance is logged and how incidents are handled. Team AI training runs alongside, so the people working with the agent understand what it does, where its limits sit and how to flag problems. Reviews follow a rhythm: monitor the agent's decisions, sample outcomes, adjust rules and retest. Over time, teams usually extend the pattern, adding a second agent or widening the first one's scope once confidence is established. The goal across all of it is an agent that keeps earning its place, with a documented trail showing exactly why it behaves the way it does.
- Support from USD 2,500 per month for 10 hours
- Governance defining ownership, review and incident handling
- Team training so staff know the agent's limits
10 / 10AI Agent Development Services: Strategy, Build and Support from Paloren
How is an agent's performance measured?
Measurement starts before the build, with a baseline: how long the task takes today, what it costs, where errors occur and what volume it carries. Those numbers become the comparison point once the agent runs. During development, Paloren builds an evaluation set from real scenarios, so behaviour is scored against cases drawn from your operation rather than invented examples. After launch, monitoring tracks completion rates, handover frequency and the quality of what the agent produces, sampled and reviewed on a regular rhythm. Voice agents are assessed on call outcomes and routing accuracy. Workflow agents are assessed on whether records land where they should, complete and correctly formatted. Internal assistants are assessed on whether answers hold up against source documents. Reporting feeds back into tuning: where the agent struggles, rules and guardrails are adjusted and the evaluation set grows. This loop is the same discipline the team applied to reporting and call analysis inside Louder, applied now to agent systems. The output for you is a clear picture of what the agent handles, what it escalates and what changed as a result.
- Baseline captured before the build begins
- Evaluation sets drawn from real scenarios
- Regular sampling of completions, handovers and quality
What you take forward
What you get
A working AI agent deployed inside your environment
Integrations with your CRM and operational tools
Guardrails, escalation rules and governance documentation
An evaluation set built from your real scenarios
Training for the team working alongside the agent
- 01
Assess readiness
Examine your data, systems and processes to confirm the task suits an agent, and receive a costed plan for what follows.
- 02
Fix the scope
Define the task, the systems involved, the rules the agent must follow, the escalation paths and the measures of success.
- 03
Design and build
Shape the agent's behaviour and guardrails, then connect it to your CRM, scheduling tools and other platforms.
- 04
Test against reality
Run the agent through scenarios drawn from your operation inside a controlled environment until performance holds.
- 05
Launch and expand
Go live on a narrow task, monitor outcomes, tune behaviour and widen scope once the agent earns trust.
| Stage | What it changes |
|---|---|
| Assess readiness | Examine your data, systems and processes to confirm the task suits an agent, and receive a costed plan for what follows. |
| Fix the scope | Define the task, the systems involved, the rules the agent must follow, the escalation paths and the measures of success. |
| Design and build | Shape the agent's behaviour and guardrails, then connect it to your CRM, scheduling tools and other platforms. |
| Test against reality | Run the agent through scenarios drawn from your operation inside a controlled environment until performance holds. |
| Launch and expand | Go live on a narrow task, monitor outcomes, tune behaviour and widen scope once the agent earns trust. |
Ready to put an agent to work?
Start with a readiness assessment to pinpoint where an agent should work first, then move into strategy and build with a defined scope and price. Paloren works with companies worldwide.
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 is the difference between an AI agent and a chatbot?
A chatbot answers questions and stops there. An agent takes a goal, plans the steps, acts inside your systems and completes the task or escalates to a person. Paloren builds both: chatbots for straightforward question handling and agents for work that requires action, such as updating CRM records, booking time or triggering workflows. The distinction matters for scope, testing and price, which is why the readiness assessment fixes it early.
How much do AI agent development services cost?
Agent development runs USD 40,000 to 90,000 over 6 to 10 weeks. Voice agents and receptionists sit at USD 25,000 to 60,000, and chatbots at USD 20,000 to 50,000, each over 4 to 8 weeks. Integration count, task consequence and data condition move the number within those bands. A readiness assessment at USD 8,000 over 2 to 3 weeks gives you a scoped estimate before any larger commitment.
How long does it take to build an AI agent?
A standard agent build takes 6 to 10 weeks from kickoff to a live system. Readiness assessment adds 2 to 3 weeks and strategy 3 to 4 weeks where those phases run first. Integration is usually the longest stretch, since connecting the agent to your CRM and other tools depends on access and documentation. Projects move fastest when credentials, sandboxes and subject-matter reviewers are available from day one.
Can an agent work with our existing CRM and tools?
Yes. Integration is central to how Paloren builds agents. The team connects agents to CRMs, scheduling tools, ticketing systems, communication platforms and reporting layers, with permissions and audit trails designed alongside the connections. Where a CRM needs rebuilding anyway, CRM implementation with AI runs as a combined engagement so the agent layer is designed with the system rather than added afterwards. Workarounds are documented wherever a platform lacks an API.
Do AI agents replace the people doing the work today?
Paloren designs agents to take the repetitive, rule-bound layer of work so people can focus on judgement, relationships and exceptions. Every agent includes escalation paths, so anything outside its scope reaches a person with context attached. In practice, roles shift rather than disappear: staff spend less time on data entry and status chasing and more time on the decisions agents hand them. Team AI training supports that transition.
What data does an AI agent need before it can be built?
An agent needs three things: the rules that govern the task, access to the systems where the work happens, and the knowledge it should draw on. That knowledge usually comes from your documents, policies and records, often consolidated into a company brain so answers come from your material. The readiness assessment examines what exists, what is missing and what needs cleaning, so preparation is scoped before the build starts.
What support exists after an agent goes live?
Ongoing support starts at USD 2,500 per month for 10 hours, covering monitoring, behaviour tuning and adjustments as your processes change. AI governance work defines who owns the agent, what changes require review and how incidents are handled. Team training helps staff understand the agent's limits and how to flag issues. Most teams use support to widen scope gradually, adding tasks once the first one runs reliably.
Which agent should a company build first?
The strongest first agent handles a task that is high-volume, rule-bound and measurable: inbound call handling, lead qualification, CRM record upkeep or internal policy questions. Narrow scope makes the agent easier to test, govern and trust, and results arrive faster. Voice agents and chatbots often suit customer-facing starts, while workflow agents suit back-office starts. The readiness assessment compares candidates against your data and systems and recommends a sequence.
How do we start with Paloren?
Start with an AI readiness assessment, priced from USD 8,000 over 2 to 3 weeks. It examines your data, systems and processes, identifies where an agent would deliver value first and produces a scoped, priced plan. From there, strategy fixes the scope and the build follows. Paloren serves companies worldwide, and Aaron and Alex Agius stay involved from the first conversation through to delivery.
Ready to put an agent to work?