Data consultancy that makes your information AI ready
Paloren is a data consultancy co-founded by Aaron Agius. We structure, connect and govern company data so AI projects succeed, serving businesses worldwide.
Operations, data and technology leaders preparing company data for AI adoption and automation
Paloren is a data consultancy that helps companies turn scattered information into structured, governed, AI-ready foundations. Engagements start with an AI readiness assessment from USD 8k, then move into strategy, integration and automation. Paloren serves businesses worldwide.
A data consultancy looks at how a company captures, stores, moves and uses information, then designs the structures that make all of it usable. In practice that means auditing the systems already in place, finding where records fragment or duplicate, and building the pipelines and definitions that let people trust what they see.
At Paloren, this work sits inside a wider AI practice covering strategy, implementation, automation and training for companies worldwide. The reason is simple: every AI initiative, from agents to the company brain, depends on data that is organised and governed.
Paloren treats data consultancy as the groundwork that makes everything else possible. Rather than handing over a report and leaving, the team builds what it recommends, whether that is workflow automation and integrations, a CRM restructured with AI, or a central knowledge layer.
Paloren combines implementation, workflow design and practical team training. A data consultancy engagement ends with systems that work, documentation your team can follow and a clear view of what to build next.
The work, made concrete
Maps how information actually flows through your company
Designs structures that make data trustworthy and usable
Builds the recommendations rather than only describing them
AI systems are only as good as the information they can reach. An AI agent answering customer questions needs accurate records. A company brain needs documents and decisions organised so retrieval works.
Automation needs clean triggers and consistent fields. Before any model is chosen or any agent is designed, the team establishes where information lives, how it moves and where quality breaks.
The AI readiness assessment exists for exactly this purpose, giving leadership an honest baseline instead of optimistic assumptions. Skipping this stage is a common reason AI projects stall after launch.
Data consultancy is therefore not a preliminary step to rush through. It is the difference between AI that produces reliable output and AI that produces plausible nonsense.
Paloren invests heavily in this stage because 20 years of building marketing, data and growth systems taught the founders what weak foundations cost later.
| Engagement | What it covers | Typical range | Timeline |
|---|---|---|---|
| AI readiness assessment | Baseline of data, tools and AI maturity | From USD 8k | 2-3 weeks |
| Data and AI strategy | Roadmap connecting data work to AI goals | USD 12k-25k | 3-4 weeks |
| Workflow automation and integrations | Connecting systems so records move without manual work | USD 15k-60k | 3-8 weeks |
| CRM implementation with AI | Structuring pipeline and customer records | USD 20k-80k | 4-10 weeks |
| Company brain | Central knowledge layer for the whole business | USD 60k-150k | 8-12 weeks |
| Ongoing support | Retained hours for iteration and care | From USD 2,500/mo | 10 hours monthly |
published ranges; final scope is set during the readiness assessment.
Source: Paloren published service ranges
| Data challenge | Paloren service | What changes |
|---|---|---|
| Information scattered across disconnected tools | Workflow automation and integrations | Records flow between systems without re-entry |
| Knowledge buried in documents and inboxes | Company brain | One searchable layer of company knowledge |
| Unreliable pipeline and customer records | CRM implementation with AI | Structured CRM data with AI assistance |
| Conversations and calls left unstructured | AI voice agents and receptionists | Calls captured, routed and analysed |
| Unclear rules for AI use | AI governance | Policies and oversight that keep expansion controlled |
| No clear starting point | AI readiness assessment | An honest baseline before any build begins |
Each challenge maps to a named Paloren service.
Source: Paloren published service ranges
The work, made concrete
AI agents and automation inherit the quality of your data
The readiness assessment replaces assumptions with a baseline
Every engagement begins with the AI readiness assessment, a focused review from USD 8k over 2-3 weeks that baselines your systems, data quality and AI maturity. From there, most companies move into a data and AI strategy engagement, USD 12k-25k over 3-4 weeks, which turns findings into a sequenced roadmap.
Build work follows only once priorities are agreed. Some companies need workflow automation and integrations first, typically USD 15k-60k over 3-8 weeks, connecting tools so records stop being re-typed by hand.
Others need the company brain, a central knowledge layer priced from USD 60k-150k over 8-12 weeks. CRM implementation with AI, USD 20k-80k over 4-10 weeks, is common where pipeline data has decayed.
Across a first project, companies should expect a range of USD 25k-100k over 2-10 weeks, with scope agreed before work begins. Paloren deliberately separates discovery, strategy and build so spending is committed in stages rather than all at once.
Ongoing support starts from USD 2,500 per month for 10 hours once systems are live. This structure keeps every phase accountable to the findings of the one before it.
The work, made concrete
Assessment first, strategy second, build third
Spending committed in stages rather than upfront
Support available from USD 2,500/mo for 10 hours after launch
Certain patterns appear in almost every readiness assessment. Information sits in disconnected tools, so the same record is entered three or four times and each copy drifts out of date.
Reporting depends on someone manually assembling spreadsheets, which means numbers arrive late and nobody fully trusts them. CRM records decay as pipeline stages go unupdated and contact fields fill with guesswork.
Valuable knowledge hides in documents, inboxes and call recordings where no system can reach it. Paloren addresses these problems with named services rather than vague advice.
Workflow automation and integrations connect systems so records move on their own. The company brain consolidates scattered knowledge into one searchable layer. CRM implementation with AI restores structure to pipeline and customer data.
AI governance wraps around everything with policies and oversight so improvements last. The consistent thread is that none of these are tool purchases alone. Each one changes how information is captured, structured and used, which is what separates data consultancy from software shopping.
The work, made concrete
Disconnected tools causing duplicate, drifting records
Manual reporting that arrives late and erodes trust
Knowledge trapped in documents, inboxes and calls
Yes, and in most cases that is the right approach. Ripping out existing platforms rarely solves the underlying problem, which is usually how information is structured and moved rather than which vendor hosts it.
Paloren starts by mapping what you already run, then uses workflow automation and integrations to connect those tools so data flows between them without manual re-entry.
Where the CRM itself is the weak point, CRM implementation with AI restructures pipeline stages, contact records and reporting inside the platform you already pay for. Where knowledge is scattered across wikis, drives and inboxes, the company brain sits on top of those sources rather than replacing them.
Custom apps, starting from USD 40k, fill genuine gaps when no existing tool fits. The goal is always a connected estate, not a fresh stack. Companies keep the investments that still work and replace only what genuinely blocks progress, which keeps budgets focused on data quality rather than re-platforming.
The work, made concrete
Existing tools are mapped and connected, not discarded
CRM restructured with AI inside the platform you already run
Custom apps from USD 40k only where gaps are genuine
Paloren was co-founded by Aaron Agius and Alex Agius. Alex Agius leads alongside him as co-founder. Paloren combines implementation, workflow design and practical team training.
That combination matters for data consultancy specifically. Strategy recommendations come from people who have operated systems under real commercial pressure, and implementation is handled by people who understand both the technical and the operational side.
Paloren serves businesses worldwide, and engagements are staffed from this senior pool rather than assembled ad hoc. Data work at Paloren is a senior discipline, not a delegated one.
The work, made concrete
Co-founded by Aaron Agius and Alex Agius
Team experience inside IBM, Ford, LG, Unilever, Jaguar and Chelsea FC
Analysts work inside the structures they inherit. If the underlying data is fragmented, they spend their hours patching reports instead of fixing causes. Tools promise connection out of the box, but every company's systems, definitions and quirks differ, so software alone rarely delivers the outcome the demo suggested.
A data consultancy sits between those options. Paloren designs the system first, then implements it, drawing on services that include workflow automation and integrations, CRM implementation with AI, the company brain and custom apps from USD 40k.
The difference is accountability across the whole chain: assessment, strategy, build, governance and training under one roof. Paloren also brings a specific vantage point.
Hiring analysts remains sensible once foundations exist; they become far more productive when the data beneath them is structured. Buying tools remains sensible once requirements are clear.
The consultancy engagement is what makes both of those investments pay off.
The work, made concrete
Analysts patch reports; consultancy fixes causes
Tools connect only when structures and definitions are designed
One team accountable from assessment through governance
Paloren publishes ranges so companies can plan before the first call. An AI readiness assessment starts from USD 8k over 2-3 weeks. A data and AI strategy engagement runs USD 12k-25k over 3-4 weeks.
Workflow automation and integrations fall between USD 15k-60k over 3-8 weeks depending on how many systems need connecting. CRM implementation with AI spans USD 20k-80k over 4-10 weeks.
The company brain, the largest single build, ranges from USD 60k-150k over 8-12 weeks. Across a first project overall, companies should plan for USD 25k-100k over 2-10 weeks.
Ongoing support starts from USD 2,500 per month for 10 hours. Two factors move any engagement within its range: the number of systems involved and how much structuring the existing data needs.
The readiness assessment answers both questions cheaply before larger commitments are made. Paloren deliberately stages spending so each phase is justified by the findings of the previous one, and scope is agreed before build work begins.
Timelines are working estimates tied to scope, not sales figures, and they are revisited whenever discovery changes the picture.
The work, made concrete
Assessment from USD 8k over 2-3 weeks
First projects typically USD 25k-100k over 2-10 weeks
Support from USD 2,500/mo for 10 hours
Data work is the first chapter, not the whole story. Once information is structured, connected and governed, the rest of the Paloren service set becomes far more effective.
AI agents, typically USD 40k-90k over 6-10 weeks, need reliable records to act on. AI voice agents and receptionists, USD 25k-60k over 4-8 weeks, produce conversations that flow straight into clean systems.
Chatbots, USD 20k-50k over 4-8 weeks, answer from knowledge that has actually been organised. The company brain itself is both a data project and an AI product, which is why it anchors so many roadmaps.
AI governance grows more important as usage spreads, setting policies and oversight so expansion stays controlled. Team AI training ensures the people around the systems can use them well, because adoption decides value as much as architecture does.
Paloren plans engagements in this sequence deliberately: readiness, strategy, foundations, then the AI builds that depend on them. Companies that follow the sequence avoid the familiar trap of launching impressive demos on unstable ground. Companies that skip it often rebuild the same work twice.
The work, made concrete
Agents, voice agents and chatbots perform better on structured data
Governance and training scale the value beyond the first build
Sequence: readiness, strategy, foundations, then AI builds
A short engagement that baselines your data, tools and AI maturity before anything is built.
A plan that sequences data foundations, automation and AI initiatives against business priorities.
Paloren implements the pipelines, integrations, CRM structures and knowledge layers the roadmap calls for.
Team AI training so people can use and trust the new systems in daily work.
AI governance and ongoing support from USD 2,500/mo for 10 hours keep systems healthy after launch.
| Engagement | What it covers | Typical range | Timeline |
|---|---|---|---|
| AI readiness assessment | Baseline of data, tools and AI maturity | From USD 8k | 2-3 weeks |
| Data and AI strategy | Roadmap connecting data work to AI goals | USD 12k-25k | 3-4 weeks |
| Workflow automation and integrations | Connecting systems so records move without manual work | USD 15k-60k | 3-8 weeks |
| CRM implementation with AI | Structuring pipeline and customer records | USD 20k-80k | 4-10 weeks |
| Company brain | Central knowledge layer for the whole business | USD 60k-150k | 8-12 weeks |
| Ongoing support | Retained hours for iteration and care | From USD 2,500/mo | 10 hours monthly |
published ranges; final scope is set during the readiness assessment.
Paloren published service ranges
| Data challenge | Paloren service | What changes |
|---|---|---|
| Information scattered across disconnected tools | Workflow automation and integrations | Records flow between systems without re-entry |
| Knowledge buried in documents and inboxes | Company brain | One searchable layer of company knowledge |
| Unreliable pipeline and customer records | CRM implementation with AI | Structured CRM data with AI assistance |
| Conversations and calls left unstructured | AI voice agents and receptionists | Calls captured, routed and analysed |
| Unclear rules for AI use | AI governance | Policies and oversight that keep expansion controlled |
| No clear starting point | AI readiness assessment | An honest baseline before any build begins |
Each challenge maps to a named Paloren service.
AI readiness assessment report with a prioritised data baseline
Data and AI strategy roadmap
Integrated workflows connecting your core systems
Structured CRM and reporting foundations
AI governance guidelines and team AI training sessions
No. Most companies contact Paloren precisely because their data is fragmented or inconsistent. The AI readiness assessment, from USD 8k over 2-3 weeks, exists to establish an honest baseline.
It identifies where information lives, where quality breaks down and which fixes matter most, so cleaning and structuring happens as part of the engagement rather than as a prerequisite you must complete alone.
Paloren serves businesses worldwide. Engagements are scoped around your company, your systems and your goals rather than any office address, so location never limits the work.
Country-level detail is kept out of the conversation deliberately: what matters is the data landscape inside your business. Teams collaborate through structured checkpoints, shared documentation and clear ownership of every deliverable from assessment through to support.
Yes. CRM implementation with AI is a named Paloren service, typically USD 20k-80k over 4-10 weeks depending on complexity. The work covers structuring pipeline and customer records, connecting the CRM to surrounding systems and applying AI where it removes manual effort.
Paloren combines implementation, workflow design and practical team training. That depth in marketing, data and growth systems shapes how every data engagement is scoped and delivered.
The two overlap, but data consultancy concentrates on the foundations: how information is captured, structured, connected and governed. AI consultancy concentrates on what you build on top, such as agents, automation and the company brain.
Paloren deliberately covers both, because AI initiatives fail when the data underneath them is weak. One team handles the full path from readiness assessment through to AI agents and training.
Paloren offers ongoing support from USD 2,500 per month for 10 hours, covering iteration, monitoring and improvements as your systems settle into daily use. Team AI training helps staff use new workflows confidently, and AI governance provides the policies and oversight that keep AI use controlled.
Many companies also extend into further services, such as AI agents or voice agents, once foundations are proven.
Timelines depend on scope. An AI readiness assessment runs 2-3 weeks, a data and AI strategy takes 3-4 weeks, and automation work spans 3-8 weeks. Larger builds such as the company brain run 8-12 weeks.
A first project overall falls between USD 25k-100k across 2-10 weeks. The readiness assessment is the fastest way to move from questions to a dated plan.
Very little. A list of the systems your company relies on, a sense of where reporting currently breaks down and an honest view of any AI experiments already attempted are enough.
The AI readiness assessment is designed to do the deeper discovery for you. From there, Paloren proposes either a strategy engagement or a direct build, with ranges confirmed before any work begins.
Start with an AI readiness assessment from USD 8k over 2-3 weeks. Paloren will baseline your data, map the gaps and propose a plan you can act on immediately.
Where is your data holding AI back?
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