Build AI that runs real work inside your company
Paloren builds AI for companies worldwide: strategy, company brain, AI agents, automation, CRM, voice agents and team training. Start with readiness.
Operations, revenue and technology leaders who want to build AI that works.
Paloren builds AI for companies worldwide, covering strategy, the company brain, AI agents, workflow automation, CRM implementation with AI, voice agents, custom apps, governance and team training. First projects run USD 25,000 to 100,000 over two to ten weeks.
Building AI means assembling several pieces into one working system rather than switching on a single tool. Something has to hold company knowledge so answers stay consistent.
Something has to act: draft, sort, summarise, qualify, answer calls. Something has to move work between the systems you already run, from CRM to finance to support. And something has to set limits: who can use the system, what it may touch, how output gets reviewed.
Paloren treats those four pieces as the build. The company brain holds knowledge. Agents handle tasks that need judgement. Workflow automation and integrations carry handoffs between platforms.
Governance sets the guardrails. Skip one piece and the build stalls: agents without a knowledge foundation invent answers, automation without governance creates risk, and tools without training sit unused.
That is why Paloren starts with readiness and strategy before any build work begins, and why every engagement ends with training so your people can run what was built.
The work, made concrete
A build combines knowledge, action, integration and governance
Agents and automation serve different jobs in the same system
Training decides whether the build gets used
Readiness comes first, because a build reflects the data and habits it stands on. Strategy follows, turning ambition into an ordered plan with governance attached. Then the build itself, shipped in increments so your team sees working software within weeks rather than waiting months for a reveal.
Integration gets equal weight: agents and automations must connect to the CRM, databases and tools your people already use, or the work just moves sideways. Every engagement closes with team training and documented governance, because a system nobody trusts is a system nobody uses.
First projects typically run between USD 25,000 and 100,000 over two to ten weeks, with scope agreed before work starts.
| Build option | What it covers | Investment (USD) | Timeline |
|---|---|---|---|
| Typical first project | Most engagements start within this envelope | 25,000 to 100,000 | 2 to 10 weeks |
| AI readiness assessment | Current state review, opportunity map, first build recommendation | From 8,000 | 2 to 3 weeks |
| AI strategy | Priorities, architecture choices, sequencing and governance plan | 12,000 to 25,000 | 3 to 4 weeks |
| Company brain | Central knowledge foundation that powers every other build | 60,000 to 150,000 | 8 to 12 weeks |
| AI agents | Task-specific agents that execute defined work | 40,000 to 90,000 | 6 to 10 weeks |
| Workflow automation and integrations | Connections between the tools your team already uses | 15,000 to 60,000 | 3 to 8 weeks |
| CRM implementation with AI | CRM setup enriched with AI-driven workflows | 20,000 to 80,000 | 4 to 10 weeks |
| AI chatbot | Customer or internal chat assistant | 20,000 to 50,000 | 4 to 8 weeks |
| AI voice agent or receptionist | Phone handling, routing and call summaries | 25,000 to 60,000 | 4 to 8 weeks |
| Custom apps | Purpose-built applications around your processes | From 40,000 | Scoped per build |
published Paloren ranges. Every engagement is scoped and approved before work begins.
Source: Paloren published service ranges
| Factor | Why it matters | Typical effect on the build |
|---|---|---|
| Data readiness | Agents perform on organised knowledge | Scattered data extends the foundation phase |
| Number of systems involved | Each integration adds build and test time | More tools means longer integration work |
| Workflow complexity | Multi-step processes need more guardrails | Complex flows increase agent design time |
| Governance requirements | Regulated teams need review paths and controls | Added controls extend strategy and rollout |
| Team training depth | Adoption decides whether the build pays off | Deeper training adds sessions after launch |
| Rollout breadth | One team differs from company-wide release | Wider rollout stretches the timeline |
These factors move a build within its published band, up or down.
Source: Paloren published service ranges
The work, made concrete
Readiness and strategy precede any build work
Builds ship in increments with working software early
Every engagement closes with training and documented governance
The service list covers the full span of an AI program. AI strategy sets direction and sequence. The company brain builds the central knowledge foundation that every other component draws on.
AI agents take on defined tasks such as qualifying enquiries, summarising calls or drafting responses. Workflow automation and integrations connect the platforms you already run so work moves without manual re-entry.
CRM implementation with AI puts intelligence inside the system your revenue team lives in daily. AI voice agents and receptionists handle calls, route conversations and capture summaries.
Custom apps wrap AI around processes that off-the-shelf products cannot serve. AI governance sets the rules for safe use. The AI readiness assessment establishes your starting point, and team AI training gives your people the skills to operate everything built.
Each component stands alone, yet the strongest outcomes come when they share the same foundation. Paloren scopes the right combination during strategy rather than selling pieces in isolation.
The work, made concrete
Strategy, company brain, agents, automation, CRM, voice, apps, governance and training
Components share one foundation for the strongest results
Scope is set during strategy, not sold piece by piece
Timing shows up in patterns rather than dates. The signal is strong when the same manual work repeats across your week: copying data between systems, writing similar replies, chasing updates, compiling reports by hand.
It is strong when knowledge lives in inboxes and heads, so answers change from person to person. It is strong when leadership wants measurement but reporting takes days to assemble.
And it is strong when your team is curious about AI but has no shared rules for using it, which is where mistakes happen. Any one of these justifies a readiness assessment, the lowest-cost entry at USD 8,000 over two to three weeks.
Waiting rarely helps, because scattered data and undocumented processes grow more tangled with time. Moving early, on a contained first build of USD 25,000 to 100,000, lets you learn how AI behaves on your real work while the stakes stay manageable.
The companies that gain most treat the first build as a foundation, not a finale.
The work, made concrete
Repeated manual work is the clearest trigger
Readiness assessment is the lowest-cost entry point
Early contained builds beat delayed large ones
Paloren publishes ranges because scope drives price, and scope can be described honestly before work begins. A first project generally sits between USD 25,000 and 100,000 and runs two to ten weeks.
Within that envelope, each build type carries its own band: readiness from USD 8,000, strategy at USD 12,000 to 25,000, automation at USD 15,000 to 60,000, chatbots at USD 20,000 to 50,000, voice agents at USD 25,000 to 60,000, CRM implementations at USD 20,000 to 80,000, agents at USD 40,000 to 90,000, custom apps from USD 40,000, and the company brain at USD 60,000 to 150,000.
What moves a specific build within its band is usually data condition, the number of systems involved, workflow complexity and how deeply governance must reach. The tables on this page set out the bands and the factors behind them.
Scoping happens before commitment, so the range you approve is the range you work within.
The work, made concrete
First projects run USD 25,000 to 100,000 over two to ten weeks
Each build type carries a published range
Data condition, integrations and governance move cost within the band
The sequence stays consistent even when scope changes. Weeks one to three usually cover the readiness assessment: reviewing data, tools, workflows and skills, then mapping where AI can take real work.
Strategy follows over three to four weeks, converting that map into priorities, architecture choices and a governance plan. From there the build phase begins. A company brain takes eight to twelve weeks, agents six to ten, automation three to eight, and voice agents four to eight.
Builds run in increments, so a working slice arrives early and grows rather than appearing fully formed at the end. Integration and testing sit alongside construction, because agents only earn trust when they behave correctly inside your actual systems.
The final stretch is training and handover: sessions for the people who will run the build, documentation, and a support path from USD 2,500 per month for ten hours where you want cover after launch.
The work, made concrete
Assessment and strategy fill the first four to six weeks
Builds ship in increments with early working slices
Training and handover close every engagement
Every component you build draws on the same raw material: what your company knows. Product details, policies, process notes, customer history, pricing logic. When that knowledge sits scattered across documents and inboxes, each agent invents its own version of the truth, and confidence in the whole program erodes.
The company brain solves this by creating one central foundation that agents, chatbots, voice systems and custom apps all query.
It takes eight to twelve weeks and ranges from USD 60,000 to 150,000, which reflects the work of organising knowledge, connecting sources and setting permissions so the right people and systems see the right information.
Teams sometimes ask whether they can skip it and build a single agent faster. They can, but that agent inherits the same fragmentation, and every later build repeats the problem.
Treating the brain as the first build turns each later component into assembly rather than archaeology. That difference shows up in every quote that follows.
The work, made concrete
One knowledge foundation serves every later component
Ranges from USD 60,000 to 150,000 over eight to twelve weeks
Skipping the brain passes fragmentation into each agent
These three build types solve different problems, and most companies eventually need all three. Workflow automation, from USD 15,000 to 60,000 over three to eight weeks, moves information between systems on rules you set: a form fills the CRM, a status change notifies the account owner, a contract triggers onboarding.
AI agents, from USD 40,000 to 90,000 over six to ten weeks, handle the judgement calls between those handoffs: reading an enquiry, deciding priority, drafting a response for review.
Voice agents and receptionists, from USD 25,000 to 60,000 over four to eight weeks, extend the same capability to the phone, answering callers, routing conversations and writing summaries back into your systems.
Chatbots, from USD 20,000 to 50,000, cover written customer and internal questions. The pattern that works is layering: automation as the plumbing, agents as the decisions, voice and chat as the front door, all drawing on the company brain so their answers agree.
The work, made concrete
Automation moves work, agents decide, voice and chat answer
Each build type carries its own published range
Layered builds share the company brain for consistent answers
Launch is the midpoint, not the finish. Builds change as your processes change, so Paloren offers ongoing support from USD 2,500 per month for ten hours, covering adjustments, new use cases and questions your team raises while operating the system.
Governance continues past launch too: usage rules get reviewed as the team grows more confident, and review paths keep output accountable. Training also deepens with time.
Initial sessions cover daily operation and escalation, and later sessions add skills as new components come online. Paloren combines implementation, workflow design and practical team training.
A build that runs without constant attention after month three is the goal, and support exists to reach it.
The work, made concrete
Support starts at USD 2,500 per month for ten hours
Governance reviews continue as usage matures
Training deepens as new components come online
Because the practice was built by operators. Paloren combines implementation, workflow design and practical team training. The service span matches that depth: strategy through company brain, agents, automation, CRM, voice, custom apps, governance and training, delivered worldwide.
For companies that want AI built into how they work rather than demonstrated at them, this is the fit. The engagement starts with a conversation about the work you want taken off your plate.
The work, made concrete
Founded by Aaron Agius and Alex Agius
Full service span delivered worldwide
Review data, tools, workflows and skills, then map where AI can take real work off your team.
Agree priorities, architecture and governance so every build serves the same operating picture.
Stand up the company brain so agents and automations draw on one trusted source of knowledge.
Deliver agents, automations and voice in increments, wired into the systems your team already uses.
Run team training, document governance, and move the build into supported operation.
| Build option | What it covers | Investment (USD) | Timeline |
|---|---|---|---|
| Typical first project | Most engagements start within this envelope | 25,000 to 100,000 | 2 to 10 weeks |
| AI readiness assessment | Current state review, opportunity map, first build recommendation | From 8,000 | 2 to 3 weeks |
| AI strategy | Priorities, architecture choices, sequencing and governance plan | 12,000 to 25,000 | 3 to 4 weeks |
| Company brain | Central knowledge foundation that powers every other build | 60,000 to 150,000 | 8 to 12 weeks |
| AI agents | Task-specific agents that execute defined work | 40,000 to 90,000 | 6 to 10 weeks |
| Workflow automation and integrations | Connections between the tools your team already uses | 15,000 to 60,000 | 3 to 8 weeks |
| CRM implementation with AI | CRM setup enriched with AI-driven workflows | 20,000 to 80,000 | 4 to 10 weeks |
| AI chatbot | Customer or internal chat assistant | 20,000 to 50,000 | 4 to 8 weeks |
| AI voice agent or receptionist | Phone handling, routing and call summaries | 25,000 to 60,000 | 4 to 8 weeks |
| Custom apps | Purpose-built applications around your processes | From 40,000 | Scoped per build |
published Paloren ranges. Every engagement is scoped and approved before work begins.
Paloren published service ranges
| Factor | Why it matters | Typical effect on the build |
|---|---|---|
| Data readiness | Agents perform on organised knowledge | Scattered data extends the foundation phase |
| Number of systems involved | Each integration adds build and test time | More tools means longer integration work |
| Workflow complexity | Multi-step processes need more guardrails | Complex flows increase agent design time |
| Governance requirements | Regulated teams need review paths and controls | Added controls extend strategy and rollout |
| Team training depth | Adoption decides whether the build pays off | Deeper training adds sessions after launch |
| Rollout breadth | One team differs from company-wide release | Wider rollout stretches the timeline |
These factors move a build within its published band, up or down.
AI readiness assessment report with an opportunity map
AI strategy document with build sequence and governance plan
Working company brain connected to your core systems
AI agents, automations or voice agents in production
Team AI training sessions and usage guidelines
Support plan with a named response path
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.
First projects usually land between USD 25,000 and 100,000 and run two to ten weeks. Individual builds vary: readiness starts at USD 8,000, strategy sits at USD 12,000 to 25,000, agents run USD 40,000 to 90,000, and automation starts at USD 15,000.
Every engagement is scoped before work begins, so you approve a defined range rather than an open-ended budget.
Timelines follow scope. A readiness assessment runs two to three weeks. Strategy takes three to four weeks. A company brain needs eight to twelve weeks. Agents take six to ten weeks, automation three to eight weeks, and voice agents four to eight weeks.
Most first engagements complete inside ten weeks because builds ship in increments rather than waiting for one large release.
Perfect data is rare, and waiting for it usually delays value. The readiness assessment shows which data is usable now and which needs tidy work first. The company brain then organises what exists into one trusted foundation.
Builds start where information is solid, and later phases strengthen the rest, so progress never hinges on a flawless dataset arriving first.
Yes. CRM implementation with AI is a core service, covering setup, migration of workflows and AI-driven enrichment inside the platform your team already uses. Builds range from USD 20,000 to 80,000 over four to ten weeks.
The work connects CRM records to the company brain so agents, reporting and call analysis draw on the same customer context.
Automation moves work between systems on fixed rules, such as routing a form submission into your CRM. An agent handles judgement inside defined limits, like drafting a reply, summarising a call or qualifying an enquiry.
Many builds combine both: automation carries the handoffs while agents make the decisions in between. Paloren scopes the mix during strategy.
Yes, and training is built into every engagement. Team AI training covers daily use, escalation paths and the governance rules that keep output safe. Documentation travels with each build, and support starts at USD 2,500 per month for ten hours when you want help on hand.
The goal is a team that operates the system without outside help for routine work.
Paloren serves businesses worldwide. Engagements run at country level and are organised around your systems, data and team rather than a street address. Sessions for discovery, build reviews and training fit the working hours of the teams involved.
What stays constant is the method: assess, strategy, foundation, build, then training and support.
The assessment, from USD 8,000 over two to three weeks, reviews your data, tools, workflows and team skills before money goes into builds. It produces an opportunity map that ranks where AI will take real work off your team and a recommendation for the first project.
That order prevents spending on agents that lack the foundation to run well.
Paloren was co-founded by Aaron Agius and Alex Agius. Paloren combines implementation, workflow design and practical team training.
Where should AI take work off your team first?
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