Get an AI agent built, integrated and supported by Paloren
Paloren helps companies worldwide get AI agents that do real work, and this page walks through how that happens. Below you will find the process, the pricing factors and the questions to ask before you start.
Operations, revenue and technology leaders who want an AI agent built properly
Paloren builds AI agents for companies worldwide, and getting one starts with a short readiness conversation. Most engagements begin with a readiness assessment, move into scoping, then a build that runs six to ten weeks. Agent projects typically sit between USD 40k and 90k.
An AI agent is software that completes work rather than only answering questions. A chatbot replies to a message; an agent can read the request, check your systems, take action, update records and report back on what it did.
Getting an AI agent therefore means more than installing a tool. It means deciding which job the agent owns, connecting it to the data and systems it needs, setting rules for what it may and may not do, and preparing your team to work alongside it.
Paloren treats this as a structured delivery project rather than a plug-in. The work usually starts with an AI readiness assessment, moves through strategy and scoping, then continues into the build itself.
Some organisations arrive with a clear task in mind, such as automating call analysis or CRM updates. Others know they want agents but need help choosing the first job.
Both paths work, and both begin with the same step: understanding your processes, your data and your systems well enough to design an agent that will actually hold up in daily use.
The work, made concrete
An agent takes action across systems, not just replies
Every agent needs a defined job, guardrails and an owner
Delivery runs from readiness through build to support
Readiness is less about technology and more about process. The strongest signal is repetition: if your team performs the same multi-step task every week, an agent can usually learn it.
The second signal is trapped data. When call recordings, CRM records and reports hold answers nobody has time to extract, an agent creates value quickly. The third signal is frustration with handoffs, where work stalls because it moves between people and tools that do not talk to each other.
Paloren makes readiness measurable instead of guesswork. An AI readiness assessment starts at USD 8k and runs two to three weeks. It maps your processes, reviews where data lives, checks system access and flags governance questions before anything gets built.
The output is a shortlist of agent opportunities ranked by effort and impact, so you invest in the right first build. Companies that skip this step often pick a task that looks simple but sits on top of messy data, and the project stalls.
The assessment exists to prevent exactly that outcome, and it doubles as the foundation for every agent you build afterwards.
| Phase | What it covers | Typical duration and range |
|---|---|---|
| AI readiness assessment | Process mapping, data review, governance flags, ranked agent opportunities | 2-3 weeks, from USD 8k |
| AI strategy | Agent selection, blueprint, guardrails, success measures | 3-4 weeks, USD 12k-25k |
| Agent build | Integrations, configuration, edge-case testing, supervised launch | 6-10 weeks, USD 40k-90k |
| Workflow automation and integrations | Connecting the agent to CRM and core tools | 3-8 weeks, USD 15k-60k |
| Ongoing support | Monitoring, tuning and improvements after launch | Monthly, from USD 2,500 for 10 hours |
Durations and figures come from Paloren's published service ranges.
Source: Paloren published service ranges
| Scope driver | Lower end of range | Upper end of range |
|---|---|---|
| Systems connected | One or two integrations | Many systems, including write-back to CRM |
| Task complexity | Single narrow process | Multiple steps, exceptions and judgement calls |
| Oversight model | Simple escalation to a person | Full governance with logging and access rules |
| Channels | One interface | Chat, voice and internal tools together |
| Team readiness | Process already documented | Discovery and training needed from scratch |
Scope drivers explain the spread within Paloren's published ranges.
Source: Paloren published service ranges
The work, made concrete
Repeated multi-step tasks are the best candidate for an agent
An assessment from USD 8k maps processes, data and governance
The shortlist ranks agent opportunities by effort and impact
Agents update CRM records after every customer conversation. They analyse calls and surface themes a manager would take days to find. They draft and route content, compile reports and handle routine requests end to end.
Voice agents and AI receptionists answer calls, capture details and pass anything sensitive to a person. The pattern across all of these is the same: the agent handles volume and repetition, while your people handle judgement.
An agent is not a hire that replaces a department. It is a worker with one job, designed carefully, connected to the right systems and monitored as it runs. When the job is defined narrowly and the data behind it is solid, agents hold up remarkably well in production, and expanding to the next task becomes straightforward.
The work, made concrete
Agents update CRM records, analyse calls and compile reports
Voice agents and receptionists handle calls and pass on sensitive ones
The agent handles volume while your people handle judgement
Scoping starts with one question: what single process should this agent own? From there, the team lists every step in that process, every system the agent must touch and every rule about what it may decide alone.
Anything requiring human judgement becomes an escalation path rather than an agent task. This discipline keeps builds achievable. Paloren typically captures the scope in an AI strategy engagement, priced at USD 12k-25k over three to four weeks, which produces the blueprint the build team works from.
The blueprint covers the agent's tasks, its data sources, its integrations, its guardrails and the way success will be measured. It also names the owner on your side, because an agent without an owner drifts.
Scoping is where budgets are set honestly. A narrow agent with two integrations sits at the lower end of the agent range; an agent that reads multiple systems, writes back to your CRM and escalates to people sits higher.
Nobody benefits from a vague scope, so Paloren puts the boundaries in writing before any build work begins, and the same document governs change requests later.
The work, made concrete
One agent owns one process, defined before build
Strategy runs USD 12k-25k over 3-4 weeks and produces the blueprint
Guardrails and escalation paths are written, not assumed
The build is where the blueprint becomes software. Paloren's agent projects typically run USD 40k-90k over six to ten weeks, and the weeks follow a rhythm. Early weeks go to connecting the agent to your systems, because integrations with your CRM, your data sources and your communication tools take real engineering time.
Middle weeks go to teaching the agent the process: the steps, the exceptions, the tone and the limits. Later weeks go to testing against real scenarios from your business, not generic examples, because agents fail on edge cases rather than on the happy path.
Governance runs alongside the build rather than after it. Rules about data access, logging and human review are configured as features, not documents nobody reads. Your team sees the agent working throughout, which removes the surprise factor at launch.
By the final week, the agent is running real tasks under supervision, and the handover focuses on what to watch, what to escalate and how to request changes. The build ends when the agent handles its process reliably, not when a demo looks impressive.
The work, made concrete
Builds run USD 40k-90k over 6-10 weeks
Integrations come first, edge-case testing comes late
Governance is configured as features during the build
Paloren publishes ranges because every build differs, and honest ranges beat fake precision. An AI agent project typically costs USD 40k-90k and takes six to ten weeks. A first project with Paloren overall sits between USD 25k and 100k depending on what you include.
Related work has its own ranges: workflow automation and integrations run USD 15k-60k over three to eight weeks, CRM implementation with AI runs USD 20k-80k, chatbots run USD 20k-50k and voice agents run USD 25k-60k over four to eight weeks.
Custom apps start at USD 40k. After launch, support starts at USD 2,500 per month for 10 hours. What moves you up or down a range is scope, not negotiation. More systems to connect, more exception handling and stricter governance requirements all add engineering time.
The useful way to think about budget is to price the process, not the technology: what does the manual version cost your business every month, and what would a reliable agent version be worth?
That comparison, which the readiness assessment helps you build, tells you whether the range makes sense for your situation.
The work, made concrete
Agent builds: USD 40k-90k over 6-10 weeks
First projects overall: USD 25k-100k depending on scope
Support after launch starts at USD 2,500 per month for 10 hours
Timelines stack, so it helps to see the whole path. The readiness assessment takes two to three weeks. If you continue into strategy, add three to four weeks for scoping and the blueprint.
The agent build itself takes six to ten weeks. Run sequentially, that puts a first agent live roughly three to four months after your first conversation.
Many organisations compress this by combining phases, starting the strategy while the assessment finishes, or by arriving with a process already documented, which shortens discovery.
Speed also depends on your side: access to systems, availability of the people who know the process and quick decisions when questions surface. Projects slow down most often waiting for system permissions or for a decision about an edge case, not during engineering.
Paloren plans the calendar with you at scoping, so you know which weeks need your attention before the build starts. If timing matters, say so early; a phased plan can put a narrow version of the agent live sooner and extend its abilities in a second pass rather than delaying everything for one large launch.
The work, made concrete
Assessment 2-3 weeks, strategy 3-4 weeks, build 6-10 weeks
Sequential first agents go live in roughly three to four months
Access to systems and fast decisions keep the calendar honest
You do not need a technical brief, a vendor shortlist or a budget approval to start a useful conversation. You need three things written down. First, the processes: list the recurring tasks that eat your team's week, even roughly.
Second, the systems: note where the data for those tasks lives, whether that is your CRM, your call platform, your spreadsheets or something older. Third, the people: name whoever owns each process today, because that person becomes the agent's owner during the project.
Anything beyond this is optional. Some organisations arrive with a written process document; others arrive with a frustrated operations manager and a shared drive. Both work.
The first conversation with Paloren is about understanding, not selling, and the questions will focus on volume, exceptions and systems rather than on technology preferences.
If you have already run an internal AI review or attempted a build that stalled, mention it, because knowing what failed before saves weeks of rediscovery. The goal of preparation is simply to make the first hour specific instead of general, which is what turns a conversation into a scoped project.
The work, made concrete
List recurring tasks, the systems behind them and the process owner
No technical brief or budget approval is needed to start
Mention any previous AI attempt that stalled so it is not repeated
Paloren was co-founded by Aaron Agius and Alex Agius, and the background matters for agent work specifically. Paloren combines implementation, workflow design and practical team training.
Paloren serves businesses worldwide and works across strategy, implementation, automation and training, so the same team that scopes your agent also builds it and trains your people to run it.
The work, made concrete
Co-founded by Aaron Agius and Alex Agius
Aaron brings 20 years of growth systems work and co-authored Faster, Smarter, Louder
Paloren combines implementation, workflow design and practical team training.
Launch is the midpoint, not the finish. In the first weeks after go-live, an agent needs watching: exceptions it handled oddly, escalations it routed wrongly, data it misread.
Paloren's support starts at USD 2,500 per month for 10 hours, which covers monitoring, tuning and adjustments as the agent settles into production. Support also matters because your business changes.
Processes shift, systems get replaced, new products appear, and an agent built for last year's process quietly drifts out of date unless someone owns it. Alongside support, Paloren provides team AI training, so the people working alongside the agent understand what it does, where its limits sit and how to request improvements.
Governance continues here too: access reviews, logging checks and periodic reviews of what the agent is allowed to decide. Most organisations use a stable first agent as the template for the next one, because the guardrails, integrations and training patterns are already proven.
The practical picture is simple: build once, support continuously, and expand task by task as confidence grows rather than betting everything on one large system.
The work, made concrete
Support starts at USD 2,500 per month for 10 hours
Team AI training covers limits, escalation and improvement requests
A proven first agent becomes the template for the next
Tell Paloren which recurring tasks you want an agent to own and which systems hold the data. This first call establishes whether an agent, an automation or a different service is the right starting point.
A two to three week engagement, starting at USD 8k, that maps your processes, reviews your data and systems, and flags governance questions. It ends with a ranked shortlist of agent opportunities.
A three to four week engagement, USD 12k-25k, that turns the chosen opportunity into a build-ready scope with guardrails, integrations, escalation paths and success measures.
Six to ten weeks, typically USD 40k-90k. Paloren connects the agent to your CRM and tools, configures governance, tests against real scenarios and runs it under supervision before handover.
Team AI training prepares the people working alongside the agent. Ongoing support starts at USD 2,500 per month for 10 hours and covers monitoring, tuning and improvements.
| Phase | What it covers | Typical duration and range |
|---|---|---|
| AI readiness assessment | Process mapping, data review, governance flags, ranked agent opportunities | 2-3 weeks, from USD 8k |
| AI strategy | Agent selection, blueprint, guardrails, success measures | 3-4 weeks, USD 12k-25k |
| Agent build | Integrations, configuration, edge-case testing, supervised launch | 6-10 weeks, USD 40k-90k |
| Workflow automation and integrations | Connecting the agent to CRM and core tools | 3-8 weeks, USD 15k-60k |
| Ongoing support | Monitoring, tuning and improvements after launch | Monthly, from USD 2,500 for 10 hours |
Durations and figures come from Paloren's published service ranges.
Paloren published service ranges
| Scope driver | Lower end of range | Upper end of range |
|---|---|---|
| Systems connected | One or two integrations | Many systems, including write-back to CRM |
| Task complexity | Single narrow process | Multiple steps, exceptions and judgement calls |
| Oversight model | Simple escalation to a person | Full governance with logging and access rules |
| Channels | One interface | Chat, voice and internal tools together |
| Team readiness | Process already documented | Discovery and training needed from scratch |
Scope drivers explain the spread within Paloren's published ranges.
AI readiness assessment report with a ranked shortlist of agent opportunities
Agent blueprint covering tasks, data sources, guardrails and success measures
Working AI agent deployed and integrated with your CRM and core systems
Documented governance rules, logging and escalation paths
Team AI training sessions for the people working alongside the agent
Ongoing support plan starting at USD 2,500 per month for 10 hours
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.
Getting an AI agent is a delivery project: pick the job it owns, connect it to your data and systems, and set rules before anything goes live. S4 structures that work.
| Step | Stage | What it changes |
|---|---|---|
| 01 | Signal | Run the AI readiness assessment: map recurring multi-step tasks, locate trapped data in call recordings and CRM records, and flag handoff friction. |
| 02 | Synthesis | Rank agent opportunities by effort and impact, then scope the first job with clear rules for what the agent may and may not do. |
| 03 | System | Connect the agent to the CRM, call recordings and reports it needs; test real cases, missing-data paths and action limits before launch. |
| 04 | Scale | Assign an operations owner to review agent actions, track time saved per completed task, and extend to the next scoped job. |
Agent builds typically run USD 40k-90k over six to ten weeks. A first project with Paloren sits between USD 25k and 100k depending on scope. If you want to start smaller, a readiness assessment begins at USD 8k and an AI strategy engagement runs USD 12k-25k. Ongoing support starts at USD 2,500 per month for 10 hours.
A readiness assessment takes two to three weeks and an AI strategy engagement takes three to four. The agent build itself runs six to ten weeks. Planned sequentially, a first agent typically goes live within three to four months of your first conversation.
Phases can overlap, and arriving with documented processes shortens discovery further.
You need reachable data more than perfect data. The agent can only use what it can access, so the readiness assessment reviews where your data lives and flags gaps before the build starts.
If cleanup is needed, it becomes part of the plan rather than a surprise. Many projects begin with imperfect data and improve it as the agent runs.
A chatbot answers questions inside a conversation. An agent completes tasks: it reads a request, checks your systems, takes action and reports back. Paloren builds both, and the right choice comes down to whether you need answers or completed work.
Chatbot projects run USD 20k-50k over four to eight weeks, while agent builds run USD 40k-90k over six to ten weeks.
Yes. Integration is a core part of every Paloren build, and agents are designed around the CRM, data sources and tools your team already runs. Where a connection is complex, workflow automation and integrations work is scoped separately at USD 15k-60k over three to eight weeks.
CRM implementation with AI is also available, priced at USD 20k-80k over four to ten weeks.
Paloren offers ongoing support starting at USD 2,500 per month for 10 hours, covering monitoring, tuning and adjustments. Your team also plays a role: the readiness work names a process owner, and team AI training prepares your people to spot issues and request changes.
Governance checks such as access reviews and logging continue on a regular cycle after launch.
No. One engaged process owner is enough on your side, supported by the training Paloren provides. The agent handles its defined process, people handle escalations and judgement calls, and the support arrangement covers the technical upkeep.
Companies of very different sizes run agents this way, because the model relies on clear ownership rather than headcount.
Yes, and that is the recommended path. A narrow first agent proves the guardrails, integrations and training patterns with limited risk. Once it runs reliably, the same blueprint extends to the next process, and workflow automation or voice agents can follow.
Expanding task by task also spreads investment over time instead of committing to one large system upfront.
Paloren serves businesses worldwide. Work is organised at country level without requiring a local office, and delivery is structured to function across locations and time zones.
The readiness assessment, strategy and build all follow the same path regardless of where you operate. What matters most is process clarity and system access, not geography.
Which process should your first agent handle?
Book your readiness conversation
