Data and AI consultancy for companies ready to put their data to work
Paloren provides data and AI consultancy covering strategy, data foundations, automation and AI agents, delivered for companies worldwide.
Companies with scattered data that want one consultancy to make AI usable
Paloren is a data and AI consultancy co-founded by Aaron Agius, an AI implementation consultant, and Alex Agius. The team provides AI strategy, company brain builds, AI agents, workflow automation, CRM implementation with AI, governance and training for companies worldwide.
Engagements start with an AI readiness assessment, then move into strategy and builds scoped to your data and systems.
A data and AI consultancy sits at the point where information, systems and decision making meet. The work starts with an honest read of the data a company already holds, where it lives, how it moves between tools, and what quality it is in.
From there the consultancy shapes an AI strategy that matches business goals rather than chasing tools.
Delivery usually covers building a company brain that unifies knowledge, deploying AI agents for specific jobs, automating workflows across systems, implementing CRM with AI built in, and adding governance so the whole thing stays safe.
Training matters just as much, because a system nobody on the team understands will not get used. A good data and AI consultancy is judged by what keeps running after the engagement ends.
The work, made concrete
Assessment of data, systems and AI readiness
Strategy tied to business goals rather than tools
Delivery of systems that keep running after handover
Every AI system inherits the condition of the data behind it. An agent answering from a company brain can only be as accurate as the records it reads. Automation that syncs a CRM will copy whatever errors already sit inside it.
This is why Paloren treats data work as the foundation of every engagement rather than an afterthought. The readiness assessment looks at where data lives, how clean it is, which systems hold conflicting versions, and where manual workarounds have quietly replaced proper integration.
Once that picture exists, decisions get easier. Some companies need consolidation before any model is worth connecting. Others have solid data but no structure for using it, and can move straight to agents and automation.
Most sit somewhere in between, with pockets of quality next to sprawl. Paloren combines implementation, workflow design and practical team training. Fixing the base first costs less than rebuilding a failed deployment later. Ambition is welcome, but sequencing protects it.
| Service | Investment range (USD) | Typical duration |
|---|---|---|
| First engagement | 25,000 to 100,000 | 2 to 10 weeks |
| AI readiness assessment | From 8,000 | 2 to 3 weeks |
| AI strategy | 12,000 to 25,000 | 3 to 4 weeks |
| Company brain | 60,000 to 150,000 | 8 to 12 weeks |
| AI agents | 40,000 to 90,000 | 6 to 10 weeks |
| Workflow automation and integrations | 15,000 to 60,000 | 3 to 8 weeks |
| CRM implementation with AI | 20,000 to 80,000 | 4 to 10 weeks |
| AI voice agents and receptionists | 25,000 to 60,000 | 4 to 8 weeks |
| Chatbots | 20,000 to 50,000 | 4 to 8 weeks |
| Custom apps | From 40,000 | Scoped per build |
| Ongoing support | From 2,500 per month | 10 hours monthly |
Published ranges and typical durations. Position inside each range depends on scope, data condition and integration depth.
Source: Paloren published service ranges
| Factor | Effect on scope and cost |
|---|---|
| Number of data sources and systems | Each additional source adds connection, cleaning and permission work |
| Condition of existing data | Duplicated or inconsistent records need repair before AI can rely on them |
| Depth of integration | Deeper connections between CRM, reporting and operations extend build time |
| Governance requirements | Access controls, review points and documentation add structured work |
| Training needs | Larger or less technical teams need more enablement time |
| Level of customisation | Custom apps and bespoke logic sit above configured tooling |
These factors explain why two projects in the same service can land at different points in the range.
Source: Paloren published service ranges
The work, made concrete
Readiness assessment maps where data lives and how clean it is
Consolidation often precedes connecting models
Ambitious AI on messy data fails at scale
Engagements follow a sequence designed to remove guesswork early. It begins with a short conversation about goals, systems and pain points, followed by an AI readiness assessment that examines data, tools, workflows and team capability.
The assessment produces a prioritised view of where AI will earn its place. Strategy work comes next, translating priorities into a roadmap with clear scope, sequencing and investment ranges.
From there builds begin: the company brain is assembled so knowledge lives in one governed place, then AI agents take on defined roles, workflow automation connects systems, and CRM implementation brings AI into the tools sales and service teams already use.
Governance runs alongside rather than after, covering access, accuracy and responsible use. Training closes the loop so internal teams can operate what was built. Ongoing support is available from USD 2,500 per month for 10 hours for companies that want continued iteration.
A first engagement typically falls between USD 25,000 and USD 100,000 and runs two to ten weeks depending on scope. Each stage produces something usable, so value appears in stages rather than only at the end.
The work, made concrete
Starts with an AI readiness assessment
Builds follow a sequenced strategy roadmap
Governance and training run alongside delivery
A company brain is a single governed layer where a business's knowledge, documents, records and processes become searchable and usable by both people and AI. Data scattered across drives, inboxes, CRMs and spreadsheets forces every question into a scavenger hunt.
An agent without a company brain guesses or hallucinates. With one, answers come from the company's own material, with sources traceable.
Building it involves connecting data sources, cleaning and structuring what arrives, setting permissions so the right people and systems see the right things, and defining how AI should reason over it.
Paloren builds company brains as a core service, with engagements typically ranging from USD 60,000 to USD 150,000 over eight to twelve weeks. The range reflects how many sources need connecting and how much structure already exists.
For a data and AI consultancy, the company brain is often the pivot point: once knowledge is unified, agents get reliable, automation gets smarter, and reporting stops contradicting itself.
Teams stop debating whose spreadsheet is current because the brain holds the live answer. It turns data from a storage problem into an operating asset the whole company draws on daily.
The work, made concrete
Unifies knowledge, records and documents in one governed layer
Gives AI agents grounded, traceable answers
Typically USD 60,000 to USD 150,000 over eight to twelve weeks
Data consultancy rarely stops at analysis, because the value shows up when systems act on that data. AI agents are the clearest example: they take defined jobs, such as qualifying enquiries, drafting responses or summarising calls, and perform them using the data they are connected to.
Paloren builds agents with engagements typically between USD 40,000 and USD 90,000 over six to ten weeks. Workflow automation and integrations, typically USD 15,000 to USD 60,000 over three to eight weeks, move information between systems so manual re-entry disappears.
CRM implementation with AI, typically USD 20,000 to USD 80,000 over four to ten weeks, turns the customer database into an active tool rather than a record pile. AI voice agents and receptionists, typically USD 25,000 to USD 60,000 over four to eight weeks, handle calls using the same underlying knowledge.
Chatbots, typically USD 20,000 to USD 50,000 over four to eight weeks, extend that to written channels. Custom apps from USD 40,000 cover cases where off the shelf tools cannot bridge the gap.
Each service draws on the same data foundation, which is why they are planned together.
The work, made concrete
AI agents handle defined jobs using connected data
Automation and integrations remove manual re-entry between systems
Voice agents, chatbots and CRM draw on the same foundation
Investment depends on scope, and Paloren publishes ranges so companies can plan before any conversation. A first engagement typically falls between USD 25,000 and USD 100,000 and runs two to ten weeks.
Within that, the AI readiness assessment starts from USD 8,000 over two to three weeks. AI strategy sits between USD 12,000 and USD 25,000 over three to four weeks. The company brain ranges from USD 60,000 to USD 150,000 over eight to twelve weeks.
AI agents run USD 40,000 to USD 90,000 over six to ten weeks, while workflow automation and integrations land between USD 15,000 and USD 60,000 over three to eight weeks.
CRM implementation with AI spans USD 20,000 to USD 80,000 over four to ten weeks. Voice agents sit between USD 25,000 and USD 60,000, chatbots between USD 20,000 and USD 50,000, and custom apps start from USD 40,000.
Ongoing support starts from USD 2,500 per month for 10 hours. Where a project lands inside a range depends on the number of systems involved, the state of the data, integration depth and how much governance and training the team needs. The table below sets the ranges out side by side.
The work, made concrete
First engagement typically USD 25,000 to USD 100,000 over two to ten weeks
Published ranges cover every service from assessment to custom apps
Scope factors determine position inside each range
Companies working with Paloren get a team that has both built AI systems from scratch and lived inside organisations where getting data handling wrong carries real consequences.
The work, made concrete
Co-founded by Aaron Agius and Alex Agius
Team experience inside IBM, Ford, LG, Unilever, Jaguar and Chelsea FC
Governance is what separates a system that lasts from one that creates risk. As AI agents take on tasks and a company brain centralises knowledge, questions follow quickly: who can access what, how is accuracy checked, what happens when the system is wrong, and how is sensitive information handled.
Paloren treats AI governance as a core service rather than a document delivered at the end.
It covers access controls across the company brain, rules for how agents use data, review points where humans check AI output before it reaches customers, and documentation that makes the whole setup explainable to leadership, auditors or regulators.
Governance also extends to training, because most incidents come from people using systems in ways nobody anticipated. Team AI training gives staff the judgment to use tools well and the vocabulary to raise problems early.
For companies in regulated industries this work is not optional, and even for others it protects the investment: an automation that quietly leaks data or an agent that answers wrongly in public can undo months of progress.
Building governance in from the start costs a fraction of retrofitting it after something goes wrong.
The work, made concrete
Access controls and usage rules across the company brain
Human review points where AI output reaches customers
Team training builds judgment alongside the systems
Readiness shows up in a handful of practical signals. Data is findable: someone can locate the current version of a customer record without asking three people. Systems talk to each other, or at least the gaps are known.
There is an owner for data quality, even if informal. Leadership can name the decisions or tasks they most want AI to take on. If those signals are missing, the AI readiness assessment is the right entry point.
It runs two to three weeks, starts from USD 8,000, and produces a structured view of data condition, system landscape, workflow opportunities and team capability. The output is a prioritised list: what to fix, what to build first, and what to leave alone for now.
Companies sometimes discover they are more ready than expected, with clean data waiting for a use case. Others learn the honest answer is that a CRM cleanup should come before any agent work.
Both outcomes are valuable, because both prevent spending on AI that would stall. Paloren runs readiness assessments for companies worldwide as a standalone service or as the first step of a larger engagement.
The work, made concrete
Findable data and known system gaps signal readiness
Assessment runs two to three weeks from USD 8,000
Output is a prioritised build list, not a vague report
Handover is designed so the company owns what was built. Documentation covers how systems work, where data flows and how to make routine changes. Training means internal staff can operate agents, update the company brain and adjust automations without calling for help on every small task.
Governance documentation stays with the business, so the rules outlive the project. For companies that want continued momentum, ongoing support starts from USD 2,500 per month for 10 hours, covering iteration, new use cases and adjustments as the business changes.
AI systems are not finished objects; data shifts, teams grow, and the workflows that fit this quarter may need tuning next quarter. A realistic expectation is a system that improves with attention, and a team that grows more capable of directing it.
Many companies use the first engagement to prove value on a contained scope, then expand into further agents, deeper automation or additional integrations in later phases.
Paloren structures delivery so each phase stands on its own while leaving room to build further. The measure of success is a company that runs its own AI confidently, with consultancy support when wanted.
The work, made concrete
Documentation and training make internal ownership realistic
Support from USD 2,500 per month for 10 hours when wanted
Each phase stands alone while leaving room to expand
Examine data condition, systems, workflows and team capability over two to three weeks, starting from USD 8,000, and produce a prioritised view of where AI should be applied first.
Translate assessment findings into a sequenced roadmap with clear scope and investment ranges, typically USD 12,000 to USD 25,000 over three to four weeks.
Connect and structure data sources into one governed knowledge layer, typically USD 60,000 to USD 150,000 over eight to twelve weeks.
Put AI agents, workflow automation, CRM implementation and integrations to work on defined jobs, with voice agents and chatbots where channels demand them.
Apply governance, train the team to operate everything, and continue with support from USD 2,500 per month for 10 hours where wanted.
| Service | Investment range (USD) | Typical duration |
|---|---|---|
| First engagement | 25,000 to 100,000 | 2 to 10 weeks |
| AI readiness assessment | From 8,000 | 2 to 3 weeks |
| AI strategy | 12,000 to 25,000 | 3 to 4 weeks |
| Company brain | 60,000 to 150,000 | 8 to 12 weeks |
| AI agents | 40,000 to 90,000 | 6 to 10 weeks |
| Workflow automation and integrations | 15,000 to 60,000 | 3 to 8 weeks |
| CRM implementation with AI | 20,000 to 80,000 | 4 to 10 weeks |
| AI voice agents and receptionists | 25,000 to 60,000 | 4 to 8 weeks |
| Chatbots | 20,000 to 50,000 | 4 to 8 weeks |
| Custom apps | From 40,000 | Scoped per build |
| Ongoing support | From 2,500 per month | 10 hours monthly |
Published ranges and typical durations. Position inside each range depends on scope, data condition and integration depth.
Paloren published service ranges
| Factor | Effect on scope and cost |
|---|---|
| Number of data sources and systems | Each additional source adds connection, cleaning and permission work |
| Condition of existing data | Duplicated or inconsistent records need repair before AI can rely on them |
| Depth of integration | Deeper connections between CRM, reporting and operations extend build time |
| Governance requirements | Access controls, review points and documentation add structured work |
| Training needs | Larger or less technical teams need more enablement time |
| Level of customisation | Custom apps and bespoke logic sit above configured tooling |
These factors explain why two projects in the same service can land at different points in the range.
AI readiness assessment report with a prioritised use case list
Data and AI strategy roadmap with scoped phases and ranges
Working company brain connected to your data sources
Deployed AI agents and automated workflows across your systems
Governance framework, documentation and team AI training
Option of ongoing support from 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.
S4 turns scattered company data into working AI systems: a company brain, agents and automation built on clean foundations. Each stage produces an artefact the team can inspect.
| Step | Stage | What it changes |
|---|---|---|
| 01 | Signal | Map where data lives, which CRM records conflict, where manual workarounds replaced integration, and score readiness for agents or automation. |
| 02 | Synthesis | Design the company brain structure, an AI strategy tied to business goals, and a governance rulebook covering access, accuracy and review. |
| 03 | System | Connect the data sources into the company brain, deploy agents for named jobs, test automation across CRM syncs and workflow failure paths. |
| 04 | Scale | Train the team on each system, assign owners for governance reviews, track agent accuracy and adoption, then extend one workflow at a time. |
A data and AI consultancy helps companies turn the information they hold into systems that work. That covers assessing data readiness, shaping AI strategy, building a company brain, deploying agents and automation, implementing CRM with AI, and providing governance and training.
Paloren is co-founded by Aaron Agius, an AI implementation consultant, and Alex Agius. Paloren combines implementation, workflow design and practical team training.
Paloren serves companies worldwide and works across countries and regions without geographic restriction. Country pages describe services at country level only, with no offices, cities or local presence implied.
Companies anywhere can engage the practice. The simplest way to confirm fit is to send a short brief describing your data, systems and goals, and Paloren will respond with a recommended starting point.
The readiness assessment examines data condition, systems, workflows and team capability across the company. It identifies where AI will create value first, where data needs repair before models can be trusted, and which quick wins exist.
The assessment runs two to three weeks, starts from USD 8,000, and ends with a prioritised roadmap that can stand alone or feed a larger engagement.
Yes. CRM implementation with AI is a core service, typically USD 20,000 to USD 80,000 over four to ten weeks, and it is built around the platforms a company already runs.
Workflow automation and integrations connect existing systems so data moves without manual re-entry. The company brain is likewise assembled from current sources rather than requiring a migration to new tools first.
Most first engagements fall between USD 25,000 and USD 100,000 and run two to ten weeks. A contained scope works best: one company brain phase, a first agent, or one automated workflow that removes a visible bottleneck.
Proving value on something bounded builds internal confidence and gives leadership evidence before committing to broader transformation across every department at once.
Team AI training is a core service. Training covers how to use the systems Paloren builds, how to prompt and direct agents effectively, how to judge AI output critically, and how to spot when something is wrong.
The goal is a team that operates AI confidently rather than depending on outside help for every adjustment after the engagement ends.
Ongoing support starts from USD 2,500 per month for 10 hours. It covers iteration on deployed agents and automations, adjustments as data and workflows change, and help with new use cases as they emerge.
Companies can also pause support and run systems internally, since handover includes documentation and training designed to make that a realistic option rather than a risk.
Separating the two creates a gap where projects stall. A data consultancy that stops at dashboards leaves AI unbuilt, while an AI provider without data discipline builds on weak foundations.
Paloren handles both in one sequence: readiness and data work first, then strategy, then agents, automation and CRM, so accountability for the outcome sits with a single team from start to finish.
Send a short brief about your data, systems and goals. Paloren will respond with a recommended starting point, a scoped readiness assessment and the range an engagement would sit within.
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