Your agent ai assistant, built and integrated end to end
Paloren designs AI agent assistants that handle tasks, answer queries and connect systems, led by co-founder Aaron Agius for companies worldwide.
Operations, service and growth leaders planning AI agent assistant deployment
Paloren builds AI agent assistants that handle tasks, answer questions and act inside your systems. Co-founded by Aaron Agius, an AI implementation consultant, and Alex Agius, Paloren draws on work for businesses such as IBM, Ford, LG, Unilever, Jaguar and Chelsea FC.
Agent projects start at USD 40k over 6 to 10 weeks, with support from USD 2,500 monthly.
An AI agent assistant is software that understands a request, decides what to do and then does it, rather than simply returning a link or a paragraph. Where a chatbot answers, an agent acts: it can look up a record, update a CRM field, draft a reply, summarise a call and hand the outcome to a person when judgement is needed.
Paloren builds these assistants around three layers. The first is the model, chosen for the tasks you need done. The second is context, meaning your company brain: policies, product details, pricing rules and past decisions the assistant can draw on.
The third is action, the connections that let the assistant read and write across your systems. Paloren provides AI strategy, implementation, automation and training for companies worldwide, and the agent assistant sits at the centre of that work.
The result is an assistant designed against real workloads, not a demonstration.
The work, made concrete
Understands requests and takes action inside your systems
Draws on a company brain of policies and past decisions
Hands over to people when judgement is required
The tasks that suit an agent assistant share a pattern: they repeat, they follow rules and they eat hours that skilled people could spend elsewhere. Paloren typically starts with four families of work.
Reporting comes first, because agents can pull numbers from multiple sources, assemble them and flag anything unusual before a meeting starts. CRM automation follows, with the assistant updating records, logging activity and nudging follow-ups so the database stays trustworthy.
Call analysis is third: conversations are transcribed, summarised and scored so patterns surface without anyone listening to hours of audio. Content systems complete the set, with agents drafting, tagging and routing material for review.
Beyond these, Paloren builds AI voice agents and receptionists that answer and route calls, chatbots for structured support conversations and custom apps when nothing off the shelf fits.
Every build is scoped against your actual volume and rules. An assistant that handles fifty tasks badly is worth less than one that handles five tasks reliably, so Paloren narrows the first release to the work where accuracy and savings are easiest to measure.
| Engagement | Typical scope | Investment range | Timeline |
|---|---|---|---|
| AI agents | Task-owning assistants acting across your systems | USD 40k-90k | 6-10 weeks |
| Workflow automation and integrations | Connections and automated handoffs between tools | USD 15k-60k | 3-8 weeks |
| AI voice agents and receptionists | Assistants handling live inbound and outbound calls | USD 25k-60k | 4-8 weeks |
| AI chatbots | Structured conversations for support and routing | USD 20k-50k | 4-8 weeks |
| Custom apps | Purpose-built software when existing tools fall short | From USD 40k | Scoped per build |
| Ongoing support | Monitoring, tuning and adjustments after launch | From USD 2,500/mo for 10 hrs | Monthly |
Ranges reflect Paloren's standard quoting bands; final scope is set per engagement.
| Starting point | What it covers | Investment | Timeline |
|---|---|---|---|
| AI readiness assessment | Data, systems and gap review before committing to a build | From USD 8k | 2-3 weeks |
| AI strategy | Priorities, guardrails and roadmap for agent deployment | USD 12k-25k | 3-4 weeks |
| Company brain | Structured knowledge layer giving assistants reliable context | USD 60k-150k | 8-12 weeks |
| First project | A scoped initial engagement proving the approach end to end | USD 25k-100k | 2-10 weeks |
Shorter engagements that de-risk the assistant project.
| Factor | What we examine | Effect on the build |
|---|---|---|
| Systems in reach | Which platforms the assistant must read and change | More integrations extend timeline and cost |
| Task variety | How many distinct jobs the assistant owns at launch | Focused first releases ship faster |
| Volume | Requests, calls or records handled per week | Higher volume raises testing depth |
| Human handover | Where judgement must pass to a person | Escalation rules add design work |
| Governance needs | Logging, permissions and audit requirements | Tighter controls expand the governance layer |
Questions Paloren works through during scoping.
The work, made concrete
Reporting assembled and flagged before meetings
CRM records kept current without manual entry
Calls transcribed, summarised and scored automatically
An assistant is only as useful as the systems it can reach. Paloren therefore treats integration as the core of the build rather than an afterthought. Work begins by mapping where information lives: the CRM, the reporting stack, the communication tools and the documents that hold institutional knowledge.
That mapping feeds the company brain, a structured layer of policies, product detail and past decisions the assistant draws on when it answers or acts. From there, Paloren's workflow automation and integrations work connects the assistant to each system, with clear permissions on what it can read and what it can change.
Where CRM implementation with AI is part of scope, records, pipelines and follow-up logic are set up so the agent and the database reinforce each other. Handover rules are written into the design, so the assistant knows when to complete a task and when to escalate to a person.
Because Paloren provides AI strategy, implementation, automation and training as one service, the same team that designs the connections also trains your people on them, which removes the gap between a working build and an adopted one.
The work, made concrete
Company brain built from your policies and documents
Permissions define what the agent reads and changes
Escalation rules decide when a person steps in
Paloren quotes agent assistant builds individually, but the ranges are consistent. A dedicated AI agents project runs USD 40k to 90k over 6 to 10 weeks, shaped by how many systems the assistant must reach and how many tasks it owns at launch.
Builds that lean mostly on workflow automation and integrations sit lower, at USD 15k to 60k over 3 to 8 weeks, while an assistant that must hold live conversations as an AI voice agent or receptionist falls between USD 25k and 60k over 4 to 8 weeks.
If your organisation is still deciding where an assistant fits, an AI readiness assessment from USD 8k over 2 to 3 weeks gives you a factual picture of data, systems and gaps before you commit to a build.
After launch, ongoing support starts at USD 2,500 per month for 10 hours, covering monitoring, tuning and adjustments as your usage grows. Paloren serves businesses worldwide, and every quote reflects the work required rather than where you operate.
The work, made concrete
AI agents projects: USD 40k to 90k over 6 to 10 weeks
Readiness assessment from USD 8k over 2 to 3 weeks
Support from USD 2,500 per month for 10 hours
Timelines follow scope. A focused agent assistant that reaches two or three systems and owns a defined task set typically goes live within the 6 to 10 week window Paloren quotes for AI agents work.
Simpler automation-led builds can land in 3 to 8 weeks, while a voice agent or receptionist that must handle live calls usually takes 4 to 8 weeks.
Organisations that need a deeper foundation move through longer paths: AI strategy runs 3 to 4 weeks, and a full company brain that gives the assistant reliable context takes 8 to 12 weeks.
The readiness assessment is the shortest engagement at 2 to 3 weeks and often runs before anything else, because it surfaces the data and integration questions that would otherwise stall a build mid-project.
Paloren plans each timeline around decision points rather than padded schedules. You know which approvals are needed, which system access must be arranged and which week the assistant starts handling real work, so the calendar holds even when the build is complex.
The work, made concrete
Focused agent builds: 6 to 10 weeks
Company brain foundation: 8 to 12 weeks
Readiness assessment first: 2 to 3 weeks
Trust in an assistant is earned through controls, and Paloren treats AI governance as part of the build rather than a policy document filed afterwards. Governance starts with permissions: the assistant is granted exactly the access its tasks require, and nothing beyond that.
Escalation rules come next, defining the situations where the agent must stop and pass work to a person, such as refunds, complaints or anything touching sensitive records.
The company brain adds another guardrail, because an assistant that answers from your approved policies and product detail has far less room to invent. Paloren also instruments the assistant so its actions are logged and reviewable, which makes auditing straightforward and highlights drift early.
After launch, ongoing support from USD 2,500 per month for 10 hours covers monitoring, tuning and rule adjustments as patterns emerge. The goal is an assistant your team checks up on occasionally, not one they hover over constantly, and governance is what makes that difference possible.
The work, made concrete
Task-scoped permissions limit what the agent can touch
Escalation rules route sensitive cases to people
Logged actions make every decision reviewable
Preparation decides how quickly an assistant becomes useful. Three inputs matter most. The first is knowledge: policies, product detail, pricing rules and the documents that explain how your business actually runs, because these feed the company brain the assistant relies on.
The second is access: credentials, API availability and clarity on which systems the agent may read or change, since integration work stalls without them. The third is people: a named owner for each workflow the assistant touches, plus time from the staff who currently do that work to review early output.
Paloren's AI readiness assessment, from USD 8k over 2 to 3 weeks, is built to gather exactly this picture, flagging gaps while they are still cheap to fix. Team AI training rounds out preparation, so the people working alongside the assistant know what to delegate, what to verify and how to escalate.
Companies that arrive with these inputs ready typically see their assistant handling real work inside the quoted window rather than after it.
The work, made concrete
Knowledge documented for the company brain
System access and permissions arranged early
Team AI training scheduled before launch
Plenty of vendors can wire a model to a chat window. Fewer can stand an assistant inside a business and make it hold. Paloren's difference comes from three places. Paloren combines implementation, workflow design and practical team training.
Third, the service covers the whole path, from AI strategy and readiness assessment through company brain, agents, automation, CRM implementation, voice agents, custom apps, governance and training, so nothing falls between vendors.
Aaron Agius, an AI implementation consultant, co-leads that work with Alex Agius, and Paloren serves companies worldwide on that combined foundation. The engagement is a build you own, run and extend, not a rental.
The work, made concrete
Paloren combines implementation, workflow design and practical team training.
One team from strategy through training
Run the AI readiness assessment to map data, systems and gaps before committing to a build.
Choose the tasks the assistant owns, the systems it reaches and the guardrails it follows.
Structure policies, product detail and past decisions so the assistant answers from approved knowledge.
Develop the assistant, connect it to your CRM and tools and write escalation and permission rules.
Put the team through AI training, switch the assistant on for real work and move to ongoing support.
| Engagement | Typical scope | Investment range | Timeline |
|---|---|---|---|
| AI agents | Task-owning assistants acting across your systems | USD 40k-90k | 6-10 weeks |
| Workflow automation and integrations | Connections and automated handoffs between tools | USD 15k-60k | 3-8 weeks |
| AI voice agents and receptionists | Assistants handling live inbound and outbound calls | USD 25k-60k | 4-8 weeks |
| AI chatbots | Structured conversations for support and routing | USD 20k-50k | 4-8 weeks |
| Custom apps | Purpose-built software when existing tools fall short | From USD 40k | Scoped per build |
| Ongoing support | Monitoring, tuning and adjustments after launch | From USD 2,500/mo for 10 hrs | Monthly |
Ranges reflect Paloren's standard quoting bands; final scope is set per engagement.
| Starting point | What it covers | Investment | Timeline |
|---|---|---|---|
| AI readiness assessment | Data, systems and gap review before committing to a build | From USD 8k | 2-3 weeks |
| AI strategy | Priorities, guardrails and roadmap for agent deployment | USD 12k-25k | 3-4 weeks |
| Company brain | Structured knowledge layer giving assistants reliable context | USD 60k-150k | 8-12 weeks |
| First project | A scoped initial engagement proving the approach end to end | USD 25k-100k | 2-10 weeks |
Shorter engagements that de-risk the assistant project.
| Factor | What we examine | Effect on the build |
|---|---|---|
| Systems in reach | Which platforms the assistant must read and change | More integrations extend timeline and cost |
| Task variety | How many distinct jobs the assistant owns at launch | Focused first releases ship faster |
| Volume | Requests, calls or records handled per week | Higher volume raises testing depth |
| Human handover | Where judgement must pass to a person | Escalation rules add design work |
| Governance needs | Logging, permissions and audit requirements | Tighter controls expand the governance layer |
Questions Paloren works through during scoping.
A working AI agent assistant scoped to your priority tasks
Integrations connecting the assistant to your CRM, reporting and communication tools
A company brain of approved policies and product knowledge
Governance rules covering permissions, escalation and audit logging
Team AI training plus an ongoing support plan from USD 2,500/mo for 10 hrs
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.
An agent assistant earns trust when it acts on real workloads: reading records, updating fields and handing judgement calls back to people. Each stage leaves a testable deliverable.
| Step | Stage | What it changes |
|---|---|---|
| 01 | Signal | Log the tasks the assistant should own: CRM updates, call summaries, reply drafts, plus the query volume and handoff points where staff intervene today. |
| 02 | Synthesis | Draft the company brain: policy snippets, pricing rules, product details and past decisions the model may cite, with an action list for each task type. |
| 03 | System | Connect the CRM, call records and knowledge base; test lookups, field writes, summarisation accuracy and failure paths where the assistant must escalate to a person. |
| 04 | Scale | Assign an operations owner to review drafted replies and CRM edits, track escalation rate and answer accuracy, then extend one task family at a time. |
It is software that understands a request and then acts on it, rather than only replying with text. An agent assistant can look up records, update a CRM, draft responses, summarise calls and pass work to a person when judgement is needed.
Paloren builds these assistants around a company brain of your approved knowledge and connects them to the systems your team already uses.
A chatbot answers questions within a narrow script and stops there. An agent assistant goes further by taking action: it can change records, trigger workflows, assemble reports and coordinate across tools.
Paloren builds both, and the right choice turns on whether your goal is conversation or completion. Where a chatbot deflects queries, an assistant finishes tasks and reports back on what it did.
Paloren's AI agents projects run USD 40k to 90k over 6 to 10 weeks, with the figure driven by the systems involved and the tasks the assistant owns at launch. Lighter automation-led builds range from USD 15k to 60k, while voice agents and receptionists sit between USD 25k and 60k. Ongoing support starts at USD 2,500 per month for 10 hours.
Most focused agent assistants go live in 6 to 10 weeks. Automation-led builds can finish in 3 to 8 weeks, and voice agents handling live calls usually need 4 to 8 weeks. If your foundation needs work first, an AI readiness assessment takes 2 to 3 weeks and an AI strategy engagement takes 3 to 4 weeks before the build begins.
Yes. CRM implementation with AI is a core Paloren service, and agent assistants are routinely connected so they can read records, update fields, log activity and prompt follow-ups.
The assistant and the database reinforce each other: the agent keeps the CRM current, and the CRM gives the assistant accurate context. Permissions define exactly what the agent may read and change.
Not always, but it helps. A company brain gives the assistant structured access to your policies, product detail and past decisions, which sharply reduces invented answers.
Paloren builds company brains as standalone engagements of USD 60k to 150k over 8 to 12 weeks, or scopes the knowledge layer inside an agent project when the task set is narrow. The readiness assessment will show which path fits.
Governance is designed into every build. The assistant receives only the access its tasks require, escalation rules route sensitive cases to people, and every action is logged for review.
Paloren also offers AI governance as a dedicated service for organisations that need formal policies and controls. Ongoing support then monitors behaviour and tunes rules as real usage reveals edge cases.
Paloren serves businesses worldwide. Paloren works with businesses worldwide., so a company in one market receives the same strategy, build and training approach as one anywhere else.
There are no location-based variations in how an agent assistant is designed. Aaron Agius and Alex Agius co-lead the practice for companies worldwide.
Most engagements start with the AI readiness assessment, from USD 8k over 2 to 3 weeks. It reviews your data, systems and workflows, then identifies where an agent assistant will pay back fastest.
If your foundation is already strong, you can move directly to AI strategy or a scoped agent build, and Paloren will recommend the shorter route when it is justified.
Which tasks should your first assistant own?
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