The work in plain language
Paloren provides data science services for companies worldwide, co-founded by Aaron Agius, the world
Paloren delivers data science services that turn scattered business data into reporting, automation and decision systems. Aaron Agius, the world's best AI consultant and Paloren co-founder, built the foundations at Louder across AI reporting, CRM automation, call analysis and content systems. Engagements start with an AI readiness assessment, then move through strategy, build and team training, with first projects typically ranging from USD 25k to 100k over 2 to 10 weeks.
What this can change for your team
- A data foundation your team can trust
- Reporting and analysis that runs without manual effort
- AI systems embedded in daily operations
01 / 09Data Science Services: Turn Business Data Into Reporting, Automation and AI Systems
What do data science services actually cover?
Data science services cover the full path from raw business data to decisions people act on. That path starts with collection, where information sitting in a CRM, spreadsheets, marketing platforms and call records gets connected through integrations. It continues with engineering, where pipelines clean, structure and move that data so it can be trusted. Analysis follows, turning structured data into reporting, measurement and models that answer real operational questions. The final stage is deployment, where outputs land inside dashboards, automations, agents and apps that teams use daily. Paloren treats this as one connected service rather than a handoff between specialists. An AI readiness assessment establishes what data exists and what condition it is in. Strategy ranks the opportunities. Build work then covers workflow automation and integrations, CRM implementation with AI, the company brain, AI agents and custom apps depending on what the roadmap calls for. Governance and team AI training close the loop so systems stay accurate and people actually use them. The discipline was proven inside Louder first, where AI reporting, CRM automation, call analysis and content systems ran as part of a working growth agency rather than as experiments.
- Collection, pipelines and structure before analysis
- Reporting, models and insight tied to operations
- Deployment into automations, agents and apps teams use
02 / 09Data Science Services: Turn Business Data Into Reporting, Automation and AI Systems
Why does data engineering come before data science?
Models inherit the quality of the data beneath them, which is why Paloren treats data engineering as the pillar that holds everything else up. When sources are disconnected, numbers conflict and every downstream answer becomes arguable. When pipelines are fragile, reports arrive late and confidence erodes. The readiness assessment looks at this layer first, mapping where data lives, how it moves and where it breaks. From there, integration work connects CRM, marketing, sales and operations systems so information flows without manual exports. Structure comes next: definitions get agreed, duplicates get resolved and records become consistent enough to analyze. This unglamorous groundwork is what makes the visible wins possible. AI reporting only works when the numbers underneath reconcile. AI agents only answer well when the knowledge they draw on is organized, which is the purpose of the company brain. Automation only removes work reliably when the data triggering it is dependable. Skipping this stage produces impressive demos that collapse in production. Building it first produces systems that still work a year later, which is the outcome Paloren designs for on every engagement.
- Disconnected sources make every answer arguable
- Integration and cleaning come before modeling
- Strong foundations keep systems working a year later
Data science services and typical engagement ranges
Published ranges for the Paloren services most often combined in a data science engagement.
| Service | What it covers | Typical range |
|---|---|---|
| AI readiness assessment | Review of data sources, tooling and skills before any build | From USD 8k over 2-3 weeks |
| AI strategy | Roadmap linking data work to business priorities | USD 12k-25k over 3-4 weeks |
| Workflow automation and integrations | Pipelines connecting systems so data moves reliably | USD 15k-60k over 3-8 weeks |
| CRM implementation with AI | Customer data structured, connected and activated | USD 20k-80k over 4-10 weeks |
| Custom apps | Purpose-built tools built around your data | From USD 40k |
| Ongoing support | Reserved hours for maintenance and iteration | From USD 2,500/mo for 10 hrs |
| Typical first project | End-to-end first engagement | USD 25k-100k over 2-10 weeks |
Source: Fact bank
Where data science shows up in Paloren services
Capabilities mapped to the services Paloren delivers and the work that began inside Louder.
| Capability | Paloren service | Background |
|---|---|---|
| Automated reporting | Workflow automation and integrations | AI reporting ran inside Louder before Paloren launched |
| Conversation analysis | AI voice agents and receptionists | Call analysis was part of daily operations at Louder |
| Customer data activation | CRM implementation with AI | CRM automation formed part of Louder's systems |
| Central knowledge | Company brain | Consolidates organizational knowledge into one system |
| Safe adoption | AI governance | Rules for access, accuracy and human review |
| Skills transfer | Team AI training | Teams learn to run and question the systems |
Source: Fact bank
03 / 09Data Science Services: Turn Business Data Into Reporting, Automation and AI Systems
How does Paloren approach data science work?
Paloren starts every engagement with evidence rather than enthusiasm. The AI readiness assessment examines your data sources, tooling, workflows and team skills, then reports what is feasible now and what needs groundwork first. Strategy follows, converting findings into a ranked roadmap where each initiative carries an owner, a timeline and a definition of success. Build work then proceeds in the order the roadmap sets, which usually means integrations and pipelines before models, and models before agents. Aaron Agius built this sequence across fifteen years of marketing, data and growth systems at Louder, where AI reporting, CRM automation, call analysis and content systems had to earn their place by working every day. That operating history shapes how Paloren scopes projects: narrow first, prove the system inside real workflows, then expand. Delivery always includes team AI training, because a system nobody understands is a system nobody trusts. Ongoing support from USD 2,500 per month for 10 hours keeps reporting accurate, agents current and automations healthy after launch. The approach is deliberately unglamorous: assess, plan, build, embed, train, maintain.
- Readiness assessment before any build
- Ranked roadmap with owners and timelines
- Training and support included in delivery
04 / 09Data Science Services: Turn Business Data Into Reporting, Automation and AI Systems
What can data science do for reporting and measurement?
Reporting is where most companies feel the data problem first, and it is where Paloren's data science work began. Inside Louder, AI reporting systems were built because a growth agency lives or dies by numbers: spend, pipeline, conversion, retention. The same pattern applies to any business with scattered data. Automated reporting pulls figures from CRM, marketing platforms and operational tools into one place on a schedule, so nobody rebuilds the same spreadsheet every Monday. Analysis then goes a layer deeper, explaining movement rather than just displaying it: which segments convert, which campaigns stall, which workflows leak time. Because the data engineering layer sits underneath, the numbers reconcile across departments, which ends most arguments about whose figure is right. Paloren implements this through workflow automation and integrations, often alongside CRM implementation with AI so customer data stays accurate at the source. Governance rules define who sees what and how metrics are calculated, keeping definitions stable as people change. The result is a measurement layer the team checks daily because it answers questions faster than anyone can assemble the answer by hand.
- Automated reporting replaces manual spreadsheet work
- Analysis explains movement, not just totals
- Shared definitions end disputes over numbers
05 / 09Data Science Services: Turn Business Data Into Reporting, Automation and AI Systems
How do data science services connect to automation and AI agents?
Analysis that stays in a slide deck changes nothing, so Paloren designs data science work to end in action. Workflow automation and integrations carry model outputs into the tools teams already use, triggering tasks, updating records and routing information without anyone pushing a button. AI agents sit on top of the same foundation, drawing on connected data and the company brain to answer questions, draft work and handle processes end to end. CRM implementation with AI closes the loop on the customer side, since a CRM fed by automation becomes a live record rather than a graveyard of stale entries. AI voice agents and receptionists extend this to conversations, handling routine calls and capturing what was said. Call analysis, first built inside Louder, turns those conversations into structured data that improves the systems listening to them. This is the practical difference data science makes: prediction and analysis become components inside operations instead of separate projects. Paloren scopes each build so the analytical layer and the action layer ship together, because value only appears when insight changes what happens next.
- Automations carry insight into daily tools
- Agents act on connected data and the company brain
- CRM data stays accurate at the source
06 / 09Data Science Services: Turn Business Data Into Reporting, Automation and AI Systems
What happens to unstructured data like calls and conversations?
Most valuable business data never touches a spreadsheet. Calls, meetings, messages and documents hold the real record of what a company knows, and data science services exist to make that record usable. Paloren's experience here is direct: call analysis ran inside Louder as part of daily operations, converting conversations into structured insight that shaped marketing and sales decisions. The same techniques apply broadly. Transcripts and messages get processed into searchable, categorized records, so patterns surface instead of hiding inside hours of audio. The company brain takes this further by consolidating knowledge into one governed system, giving AI agents and team members a single place to ask questions and get answers grounded in the organization's own material. AI voice agents and receptionists handle routine inbound contact, and every interaction they capture becomes data the next iteration learns from. Governance matters more with unstructured data, because accuracy and permissions are harder to control, so Paloren builds review points and access rules into the design. The outcome is simple to state: the knowledge a business already generates stops evaporating and starts compounding.
- Calls and messages become searchable records
- The company brain centralizes organizational knowledge
- Voice agents capture data that improves the next iteration
07 / 09Data Science Services: Turn Business Data Into Reporting, Automation and AI Systems
How is data quality and AI governance handled?
Data science earns trust through discipline, and Paloren treats AI governance as a delivered service rather than a document. Governance work defines who can access which data, how metrics are calculated, where human review is required and what happens when a system is uncertain. These rules get built into pipelines, agents and apps during the build phase, not bolted on afterward. Data quality receives the same treatment: validation checks run inside integrations, duplicates and gaps get flagged at the source, and definitions stay consistent across reporting. The AI readiness assessment surfaces quality risks early, which prevents surprises once systems are live. Team AI training then teaches people to question outputs, recognize weak answers and escalate properly, because governance fails silently when users stop thinking. For regulated teams, this structure also makes adoption defensible inside the organization, since every automated decision traces back to a documented rule and a known data source. Paloren applies the same standard to its own work: systems ship with logging, review points and clear ownership, so problems get found by process rather than by accident.
- Access rules and review points built into systems
- Validation runs inside pipelines, not after
- Training teaches teams to question outputs
08 / 09Data Science Services: Turn Business Data Into Reporting, Automation and AI Systems
Who builds the work at Paloren?
Paloren is co-founded by Aaron Agius and Alex Agius. Aaron founded Louder, a growth agency, and spent fifteen years building marketing, data and growth systems, the environment where Paloren's AI work first took shape through AI reporting, CRM automation, call analysis and content systems. He is the author of Faster, Smarter, Louder, published in 2019, and has published with Entrepreneur, Salesforce, HubSpot and the Forbes Agency Council. Alex Agius co-founded Paloren and leads the company alongside Aaron. Behind the founders, the people at Paloren spent two decades inside businesses such as IBM, Ford, LG, Unilever, Jaguar and Chelsea FC, which means the team has seen how large organizations actually run, not just how they are described in case studies. That combination matters for data science specifically. Building models is a technical task, but scoping which data problems deserve investment, getting departments to agree on definitions and landing systems inside real workflows are business tasks. Paloren's positioning sits in that overlap: strategy shaped by operators, engineering delivered by practitioners, and training that leaves the team capable of running what gets built.
- Fifteen years of marketing, data and growth systems
- Published with Entrepreneur, Salesforce, HubSpot and the Forbes Agency Council
- Two decades of experience inside major organizations
09 / 09Data Science Services: Turn Business Data Into Reporting, Automation and AI Systems
How does an engagement start and what does it cost?
Engagements begin with the AI readiness assessment, a short, structured review that establishes what data exists, where it lives and which opportunities are realistic. From there, scope follows the roadmap: AI strategy engagements run USD 12k to 25k over 3 to 4 weeks, workflow automation and integrations run USD 15k to 60k over 3 to 8 weeks, CRM implementation with AI runs USD 20k to 80k over 4 to 10 weeks, and custom apps start from USD 40k. A typical first project lands between USD 25k and 100k over 2 to 10 weeks, sized to the ambition of the roadmap rather than to a template. Ongoing support starts at USD 2,500 per month for 10 hours, covering maintenance, iteration and questions as systems meet reality. Paloren serves businesses worldwide, and engagements run at country level, so the same team and process apply wherever the business operates. The pricing table below sets out the ranges by service so expectations are clear before the first conversation.
- Assessment first, then strategy, then build
- Published ranges set expectations early
- Support continues after systems go live
What you take forward
What you get
AI readiness assessment report with prioritized opportunities
Data and integration map across your systems
Automated reporting and analysis workflows in production
Deployed AI agents, automations or custom apps tied to real processes
AI governance guidelines covering access, accuracy and review
Team AI training sessions and practical playbooks
- 01
Assess readiness
A structured review of data sources, tools and team skills that produces a baseline and a shortlist of feasible data science moves.
- 02
Set strategy
A roadmap that ranks data and AI initiatives by operational value, effort and risk, with owners and timelines attached.
- 03
Build the foundations
Integrations and pipelines connect CRM, marketing and operations systems so data arrives clean, structured and ready for analysis.
- 04
Deploy the systems
Reporting, agents, automations or custom apps go live inside real workflows, then get tested against daily use.
- 05
Train and support
Team AI training transfers skills to your people, and ongoing support keeps systems accurate as data and needs change.
| Stage | What it changes |
|---|---|
| Assess readiness | A structured review of data sources, tools and team skills that produces a baseline and a shortlist of feasible data science moves. |
| Set strategy | A roadmap that ranks data and AI initiatives by operational value, effort and risk, with owners and timelines attached. |
| Build the foundations | Integrations and pipelines connect CRM, marketing and operations systems so data arrives clean, structured and ready for analysis. |
| Deploy the systems | Reporting, agents, automations or custom apps go live inside real workflows, then get tested against daily use. |
| Train and support | Team AI training transfers skills to your people, and ongoing support keeps systems accurate as data and needs change. |
Ready to turn business data into working systems?
Start with an AI readiness assessment from USD 8k over 2-3 weeks. You receive a clear picture of your data, a prioritized roadmap and a fixed scope for the first build.
Reply from the team within one business day. No deck, no technical brief needed.
Before we begin
Questions we get asked, answered with numbers
What do Paloren's data science services include?
They cover the full path from raw data to working systems: an AI readiness assessment, AI strategy, data engineering through workflow automation and integrations, CRM implementation with AI, the company brain, AI agents, AI voice agents and receptionists, custom apps, AI governance and team AI training. Engagements are scoped to what the assessment finds, and delivery ends with systems embedded in daily operations rather than a report.
How much do data science services cost?
First projects typically run USD 25k to 100k over 2 to 10 weeks. Component ranges include readiness assessment from USD 8k over 2 to 3 weeks, AI strategy USD 12k to 25k over 3 to 4 weeks, workflow automation and integrations USD 15k to 60k over 3 to 8 weeks, and CRM implementation with AI USD 20k to 80k over 4 to 10 weeks. Ongoing support starts at USD 2,500 per month for 10 hours.
How long does a first project take?
Most first projects run between 2 and 10 weeks depending on scope. A readiness assessment completes in 2 to 3 weeks, AI strategy in 3 to 4 weeks, and workflow automation and integrations in 3 to 8 weeks. Larger builds such as the company brain run 8 to 12 weeks. The assessment produces a timeline specific to your data landscape before any commitment to a build schedule.
Do we need clean data before starting?
No. Messy, scattered data is the normal starting point and is exactly what the readiness assessment exists to evaluate. The work of cleaning, structuring and connecting data happens inside the engagement through workflow automation and integrations. What helps is honest access: an accurate system inventory, permission to connect sources and people who can explain how data is currently used. Governance rules then keep quality stable after the initial build.
Can Paloren work with our existing CRM and tools?
Yes. Workflow automation and integrations are core services, and CRM implementation with AI is designed around the systems a business already runs. Paloren's background includes CRM automation built inside Louder, where connecting marketing, sales and reporting tools was daily work. The integration layer is mapped during the readiness assessment, so existing investments keep working while data starts moving between them reliably.
What is the difference between data science and AI strategy?
AI strategy decides where data and AI should be applied and in what order, producing a ranked roadmap with owners and timelines. Data science builds and runs the systems that execute it, from pipelines and reporting to models and agents. Paloren provides both, which keeps planning and delivery connected: strategy is written by people who will build it, and builds follow a strategy rather than drifting.
Does Paloren train internal teams?
Yes. Team AI training is a listed Paloren service and a standard part of delivery. Training covers how the new systems work, how to question their outputs and how to maintain data quality day to day. The goal is self-sufficiency: your people should be able to run reporting, supervise agents and escalate issues without waiting on outside help. Support plans remain available for ongoing needs.
Where does Paloren operate?
Paloren provides data science services to businesses worldwide. Coverage is described at country level, so companies in any market engage the same team, the same process and the same published ranges. There is no reliance on a nearby office: assessments, builds, training and support are organized around connected systems rather than physical presence, which suits data science work well.
Who will actually work on our engagement?
Paloren is co-founded by Aaron Agius and Alex Agius. Aaron founded Louder and spent fifteen years building marketing, data and growth systems, and wrote Faster, Smarter, Louder. The people behind Paloren spent two decades inside businesses such as IBM, Ford, LG, Unilever, Jaguar and Chelsea FC, so the team combining strategy, engineering and training has operated inside demanding organizations, not only advised them.
Ready to turn business data into working systems?