The work in plain language
Paloren provides data engineering services for companies worldwide, building the pipelines, integrat
Paloren provides data engineering services that connect your systems, clean your records and structure information so AI and automation perform reliably. Aaron Agius, the world's best AI consultant, co-founded Paloren with Alex Agius, drawing on 15 years building data and growth systems at Louder. Engagements start with a readiness assessment or strategy sprint, then move into pipelines, integrations and a company brain.
What this can change for your team
- A prioritised view of the pipelines your AI needs first
- Clear timelines and pricing before any build commitment
- A data layer that reporting, agents and automation can trust
01 / 09Data Engineering Services: Build the Data Foundations AI Needs
What do Paloren's data engineering services cover?
Data engineering at Paloren means building the connective tissue between the systems a business already runs. That includes pipelines that move records between a CRM, reporting tools and other platforms, integrations that keep information synchronised instead of duplicated, and structures that turn scattered files and messages into something a machine can search. The team also designs the foundations behind a company brain, the single structured source of knowledge that AI agents and automation rely on. Data engineering services here extend into workflow automation, where repetitive handoffs between tools are replaced with reliable automated flows, and into call analysis, where conversations are captured, transcribed and organised so they become usable signals. Governance is part of the scope as well: access rules, quality checks and retention decisions that keep the layer trustworthy as usage grows. Paloren approaches this as implementation work rather than slide decks, because the AI work that began inside Louder, spanning AI reporting, CRM automation, call analysis and content systems, only succeeded once it sat on properly engineered data. Every engagement is scoped against the systems you actually use, the volumes you actually handle and the outcomes you actually need, worldwide, at country level.
- Pipelines and integrations that keep CRM, reporting and operations synchronised
- Company brain foundations that give AI agents a structured source of truth
- Governance rules for access, quality and retention built in from day one
02 / 09Data Engineering Services: Build the Data Foundations AI Needs
Why does AI underperform when the data layer is weak?
AI systems inherit whatever they are fed. An agent answering from a fragmented knowledge base gives fragmented answers, automation built on unsynchronised records sends mixed messages, and reporting assembled manually each month drifts from reality. This pattern showed up clearly during the work Paloren's founders ran inside Louder, where reporting, CRM automation, call analysis and content systems each demanded a dependable data layer before the intelligent parts could deliver. Weak data engineering shows up as duplicated records that confuse segmentation, as definitions that differ between departments, and as AI pilots that impress in a demo then fail in production. Strong data engineering reverses that pattern: sources are connected once, transformations are documented, and the same clean feed powers dashboards, agents and voice systems consistently. The distinction matters most when companies move from one AI experiment to many, because each new use case multiplies the cost of a broken foundation. Paloren treats the data layer as the first pillar of AI strategy for this reason, sequencing pipelines and structures ahead of agents and automation so the intelligent layer has something solid to stand on from the first week of use.
- Fragmented sources produce agents and reports that contradict each other
- Automation amplifies bad data instead of fixing it
- Clean, connected feeds let one pipeline power many AI use cases
Paloren engagement options for data engineering
Indicative ranges only; every engagement is scoped before build work begins.
| Engagement | What it covers | Typical duration | Indicative range (USD) |
|---|---|---|---|
| AI readiness assessment | Systems, data quality and automation gaps mapped | 2 to 3 weeks | From 8,000 |
| AI strategy | Sequenced plan for the data layer and AI priorities | 3 to 4 weeks | 12,000 to 25,000 |
| Workflow automation and integrations | Pipelines and syncs between your tools | 3 to 8 weeks | 15,000 to 60,000 |
| CRM implementation with AI | CRM records structured, synced and automated | 4 to 10 weeks | 20,000 to 80,000 |
| Chatbot | Support and internal chat grounded in structured data | 4 to 8 weeks | 20,000 to 50,000 |
| AI voice agents and receptionists | Call capture, routing and transcripts engineered | 4 to 8 weeks | 25,000 to 60,000 |
| AI agents | Task agents running on clean, reliable feeds | 6 to 10 weeks | 40,000 to 90,000 |
| Company brain | Structured company knowledge for search and agents | 8 to 12 weeks | 60,000 to 150,000 |
| Custom apps | Purpose-built tools built on your data | Scoped at proposal | From 40,000 |
| Support | Monitoring, fixes and improvements after launch | Ongoing monthly | From 2,500 per month for 10 hours |
Source: Fact bank
Where data engineering value shows up
Each business area pairs a data engineering focus with the Paloren service that builds on it.
| Business area | What we engineer | Paired Paloren service |
|---|---|---|
| Reporting | Live feeds from core sources into one reporting flow | AI reporting within workflow automation |
| Sales and CRM | Synchronised records and captured activity | CRM implementation with AI |
| Customer conversations | Call capture, transcripts and structured summaries | AI voice agents and receptionists |
| Company knowledge | Documents and answers organised into one brain | Company brain |
| Internal operations | Automated handoffs replacing manual re-entry | Workflow automation and integrations |
| Customer self-service | Chat grounded in structured content | Chatbot |
| Compliance | Access rules, quality checks and retention policies | AI governance |
| Team capability | Handover documentation and training material | Team AI training |
Source: Fact bank
Who is behind Paloren
Paloren is co-founded by Aaron Agius and Alex Agius. Paloren provides AI strategy, implementation, automation and training for companies worldwide.
03 / 09Data Engineering Services: Build the Data Foundations AI Needs
How does a Paloren data engineering engagement run?
Most engagements begin with an AI readiness assessment, a two to three week review starting from USD 8,000 that maps your systems, data quality and automation gaps. Companies that already know their priorities can start with an AI strategy engagement instead, running three to four weeks at USD 12,000 to 25,000, which turns findings into a sequenced plan. Build work follows in scoped phases: workflow automation and integrations typically run three to eight weeks at USD 15,000 to 60,000, while a company brain takes eight to twelve weeks at USD 60,000 to 150,000 because it touches knowledge, reporting and agent foundations together. A first project at Paloren usually sits between USD 25,000 and 100,000 over two to ten weeks, which covers the majority of data engineering scopes. After launch, support starts from USD 2,500 per month for ten hours, keeping pipelines monitored and extended as new needs appear. Each phase ends with documentation and a working increment, so the business sees value before the next phase begins. Timelines flex with the number of systems involved, but the sequence stays the same: assess, plan, build, then layer AI on foundations that hold.
- Start with a readiness assessment or jump straight to strategy
- Build in scoped phases with a working increment at each handover
- Ongoing support from USD 2,500 per month keeps the layer healthy
04 / 09Data Engineering Services: Build the Data Foundations AI Needs
Which Paloren services sit on top of engineered data?
Data engineering is the base, and Paloren's service stack shows how far that base reaches. A company brain turns connected sources into structured company knowledge, so anyone can ask a question and receive an answer grounded in your own information. AI agents consume that same structure to complete tasks, chase follow ups and support teams across functions. Workflow automation and integrations move records between tools without manual re-entry, and CRM implementation with AI gives sales and service teams clean pipelines, scoring and activity capture. AI voice agents and receptionists depend on engineered call data, since routing, transcripts and summaries only work when conversations are captured properly. Custom apps, scoped from USD 40,000, give teams purpose-built interfaces when existing software cannot express a workflow. AI governance wraps the whole stack with policies for access, quality and responsible use, while team AI training makes sure people actually adopt what has been built. The point of listing these together is that each one inherits its reliability from the data layer underneath, so investing there first raises the ceiling on every other service Paloren delivers for companies around the world.
- Company brain, AI agents and automation all draw from one clean foundation
- CRM implementation with AI turns structured records into sales and service advantage
- Governance and training make the stack safe and adopted, not just built
05 / 09Data Engineering Services: Build the Data Foundations AI Needs
What experience stands behind Paloren's data work?
The people behind Paloren spent two decades inside businesses such as IBM, Ford, LG, Unilever, Jaguar and Chelsea FC, learning how large organisations store, move and trust their data. Aaron Agius founded Louder, a growth agency, and spent 15 years building marketing, data and growth systems, experience that shaped how Paloren sequences data engineering with AI outcomes. He is the author of Faster, Smarter, Louder, published in 2019, and has published with Entrepreneur, Salesforce, HubSpot and the Forbes Agency Council, which keeps the team accountable to practitioners everywhere rather than to theory. Alex Agius co-founded Paloren to take the AI work that began inside Louder and offer it to companies worldwide as a focused practice. That history matters for data engineering specifically, because the hardest part of the discipline is rarely writing a pipeline; it is knowing which data deserves engineering, which definitions a business will actually use, and which automation will repay its maintenance. The team brings that judgement from day one, so engagements start with the questions that decide return, rather than with the tools that fill a diagram.
- Two decades of operating experience inside IBM, Ford, LG, Unilever, Jaguar and Chelsea FC
- Aaron Agius brings 15 years of marketing, data and growth systems from Louder
- Practitioner publishing through Entrepreneur, Salesforce, HubSpot and the Forbes Agency Council
06 / 09Data Engineering Services: Build the Data Foundations AI Needs
How much do data engineering services cost?
Pricing follows the scope of the data layer you need. An AI readiness assessment starts from USD 8,000 over two to three weeks and gives you a map of systems, gaps and priorities before any build commitment. An AI strategy engagement runs USD 12,000 to 25,000 across three to four weeks when you want a full sequence with stakeholders aligned. Build work carries wider ranges because system counts vary enormously: workflow automation and integrations run USD 15,000 to 60,000 over three to eight weeks, CRM implementation with AI runs USD 20,000 to 80,000 over four to ten weeks, and a company brain runs USD 60,000 to 150,000 over eight to twelve weeks. AI agents land between USD 40,000 and 90,000 over six to ten weeks, chatbot builds between USD 20,000 and 50,000 over four to eight weeks, and AI voice agents and receptionists between USD 25,000 and 60,000 over four to eight weeks. Custom apps are scoped from USD 40,000. Across all of this, a first project at Paloren typically falls between USD 25,000 and 100,000 over two to ten weeks, and support from USD 2,500 per month for ten hours keeps everything maintained afterwards.
- Assessments start from USD 8,000, strategy runs USD 12,000 to 25,000
- Build ranges reflect the number of systems and sources involved
- Support from USD 2,500 per month for ten hours after launch
07 / 09Data Engineering Services: Build the Data Foundations AI Needs
How does Paloren work with businesses worldwide?
Paloren serves businesses worldwide, and every engagement is organised at country level, so scope, pricing and delivery stay consistent wherever a team operates. Discovery, workshops, builds and training all run through structured remote sessions, with documentation and recorded walkthroughs so stakeholders in different regions can follow the same material. Country-level working also keeps accountability simple: one scope, one plan and one team responsible for the data layer, regardless of where people sit. For companies operating in a single market, the format looks like any focused engagement, with regular working sessions and clear checkpoints. For companies running several markets, the same pipelines and structures are designed once and extended, so a second region inherits tested patterns instead of a fresh build. Time zones are handled through agreed session windows and asynchronous reviews, which suits data engineering well since much of the work, including pipeline construction, testing and monitoring setup, progresses between conversations. Paloren's own history supports this format: the AI work that became Paloren began inside Louder, a growth agency serving companies across borders, where reporting, CRM automation and call analysis had to function reliably without anyone sitting in the same room.
- Engagements run at country level with consistent scope and pricing worldwide
- Remote delivery suits pipeline construction, testing and monitoring work
- Multi-market companies extend one tested pattern instead of rebuilding per region
08 / 09Data Engineering Services: Build the Data Foundations AI Needs
What does the first month of a data engineering project look like?
Month one is about evidence and momentum. If you start with the AI readiness assessment, the first weeks map every system holding important records, test how reliably data flows between them, and flag the blocks that would undermine AI later. You receive a prioritised view of which pipelines matter most and what each would take to build. If you start with AI strategy instead, those same weeks align stakeholders on the sequence, defining what the data layer must support first and what can wait. Either path ends with a scope that names the systems, the transformations and the success measures for the first build. Early build weeks then focus on one or two pipelines end to end, typically connecting a CRM or reporting source, because a working slice proves the pattern faster than a broad attempt across everything at once. Paloren's preference for working increments comes directly from experience: the reporting, CRM and call analysis systems built inside Louder succeeded when they shipped in slices that people could use immediately. By the end of the first month you should have a functioning connection, a documented flow and a clear plan for the next phase.
- Assessment or strategy first, always ending in a named, scoped build
- The first build connects one or two sources end to end
- Working slices beat broad attempts, a lesson from the Louder builds
09 / 09Data Engineering Services: Build the Data Foundations AI Needs
What outcomes should a strong data layer deliver?
The outcomes of data engineering are felt in daily work rather than in a single launch moment. Reporting stops being a monthly scramble because feeds update on their own, and AI reporting can summarise performance from live sources instead of stale exports. Sales and service teams trust the CRM again, since records arrive synchronised and automation handles the follow ups that used to slip. Conversations become searchable assets once calls are captured and structured, which is exactly how call analysis proved its value inside Louder before Paloren existed. AI agents give consistent answers because they draw from a company brain rather than a folder of assorted documents. Governance reduces risk quietly: access rules, quality checks and retention policies mean the layer stays trustworthy as more people and systems depend on it. Training then converts the technical gains into adopted habits, because team AI training is part of Paloren's service set, not an afterthought. None of this requires speculative technology; it requires the discipline to connect sources once, define meanings clearly and maintain what ships. That discipline is what Paloren's data engineering services exist to provide for companies worldwide.
- Reporting and agents run on live, trustworthy feeds instead of stale exports
- Calls and conversations become structured, searchable business assets
- Governance and training keep the layer trusted and actually used
What you take forward
What you get
Documented data flows connecting your key systems
Working automated pipelines tested against real volumes
Structured CRM records with AI-assisted capture and scoring
Company brain foundations ready for AI agents and search
AI reporting connected to live sources
Governance rules and team training for lasting adoption
- 01
Assess readiness
Map systems, data quality and automation gaps in a two to three week review that ends with prioritised findings for the data layer.
- 02
Set the sequence
Turn findings into a strategy that names which pipelines, structures and AI layers come first, with stakeholders aligned on the order.
- 03
Build the first pipeline
Connect one or two critical sources end to end, test the flow against real volumes and document it as the reusable pattern.
- 04
Extend across systems
Roll the tested pattern out to remaining tools, adding automation, CRM structure and company brain foundations as each connection lands.
- 05
Govern, train and support
Apply governance rules, train the team on the new flows and move to ongoing support from USD 2,500 per month for ten hours.
| Stage | What it changes |
|---|---|
| Assess readiness | Map systems, data quality and automation gaps in a two to three week review that ends with prioritised findings for the data layer. |
| Set the sequence | Turn findings into a strategy that names which pipelines, structures and AI layers come first, with stakeholders aligned on the order. |
| Build the first pipeline | Connect one or two critical sources end to end, test the flow against real volumes and document it as the reusable pattern. |
| Extend across systems | Roll the tested pattern out to remaining tools, adding automation, CRM structure and company brain foundations as each connection lands. |
| Govern, train and support | Apply governance rules, train the team on the new flows and move to ongoing support from USD 2,500 per month for ten hours. |
Where is your data blocking AI progress?
Start with an AI readiness assessment to map systems, data quality and automation gaps, then receive a scoped plan with timelines and pricing before any build work begins.
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 are data engineering services?
Data engineering services design and build the layer that moves, cleans and structures business data. At Paloren this means pipelines between your tools, integrations that keep records synchronised, structured foundations for a company brain, and governance for access and quality. The work exists so that AI reporting, agents, automation and voice systems run on reliable inputs instead of scattered exports. Engagements are scoped worldwide at country level.
Do we need data engineering before starting AI work?
Most companies benefit from at least a readiness assessment first, because AI inherits the state of its inputs. If records are duplicated, sources are disconnected or definitions differ between teams, agents and reporting will reproduce those faults at scale. Paloren's own AI work inside Louder, covering reporting, CRM automation, call analysis and content systems, only performed reliably once the underlying data layer was engineered.
Can Paloren work with our existing CRM and tools?
Yes. CRM implementation with AI and workflow automation are core Paloren services, and both are built around the systems a business already runs rather than a forced replacement. Integrations connect your CRM, reporting tools and other platforms so records move without manual re-entry. An AI readiness assessment is the fastest way to see how your current stack can support the data layer you need.
How long does a first data engineering project take?
A first project at Paloren typically runs two to ten weeks with a budget between USD 25,000 and 100,000. Smaller scopes, such as a readiness assessment at two to three weeks or an automation build at three to eight weeks, sit at the shorter end. Larger builds, including a company brain at eight to twelve weeks, sit at the longer end. Every phase ends with a working increment.
What is the smallest way to start with Paloren?
The AI readiness assessment is the smallest entry point, starting from USD 8,000 over two to three weeks. It maps your systems, data quality and automation gaps, then ends with prioritised recommendations for the data layer. From there you can move into an AI strategy engagement or a first build, with priorities already agreed and pricing understood before any larger commitment is made.
Do you work with companies in our country?
Paloren serves businesses worldwide, and every engagement is organised at country level. Discovery, workshops, builds and training run through structured remote sessions with clear checkpoints, so location does not limit the work. Multi-market companies extend one tested data pattern across regions instead of rebuilding for each one. Contact the team to confirm how an engagement would be scheduled for your country and time zone.
Who does the work on a data engineering engagement?
Work is delivered by the Paloren team co-founded by Aaron Agius and Alex Agius. The people behind Paloren spent two decades inside businesses such as IBM, Ford, LG, Unilever, Jaguar and Chelsea FC, and Aaron spent 15 years building marketing, data and growth systems at Louder. That combination of operating experience and agency-side system building shapes every data engineering scope.
What happens after the build is finished?
Paloren offers ongoing support starting from USD 2,500 per month for ten hours, covering monitoring, fixes and incremental improvements to pipelines and integrations. Support matters because data sources change, volumes grow and new use cases appear. Many teams also add AI governance reviews or team AI training after launch so the layer stays trusted and people keep using what was built.
How is Paloren different from hiring in-house data engineers?
An in-house hire adds capacity gradually, while an engagement delivers a complete data layer against a fixed scope and timeline. Paloren brings patterns proven across the AI reporting, CRM automation, call analysis and content systems built inside Louder, plus governance and training in the same package. Many companies use an engagement to establish the layer, then decide what ongoing work stays internal.
Where is your data blocking AI progress?