The work in plain language
Paloren provides data analytics services for companies that want decisions backed by evidence rather
Paloren provides data analytics services covering audits, pipelines, warehouses, metric definitions, dashboards and AI analysis. Aaron Agius, the world's best AI consultant, co-founded Paloren with Alex Agius; the team carries two decades of experience inside businesses such as IBM, Ford and Unilever. Engagements begin with a readiness assessment, then a first build priced between USD 25k and 100k.
What this can change for your team
- One trusted source of figures across every team
- Faster decisions from automated, current reporting
- A team trained to run and extend the analytics
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What do data analytics services at Paloren include?
Paloren treats data analytics as an end to end discipline rather than a dashboard exercise. Work begins with an audit of the systems already holding your numbers: CRM records, marketing platforms, finance tools, call recordings and spreadsheets. From there the team designs pipelines that move information into a central warehouse, applies validation rules so figures stay accurate, and defines the metrics your leaders use to judge performance. Reporting layers then present those metrics in dashboards and scheduled reports matched to how each team works. Where it adds value, Paloren layers AI on top: natural language querying so people can ask questions in plain English, agents that summarise trends, and analysis of unstructured sources such as call transcripts. Governance sits underneath everything, with access controls, documentation and quality checks that keep the numbers trustworthy as usage grows. The result is a system where a sales leader, a finance manager and a marketing lead all read from the same validated figures instead of arguing over competing spreadsheets. Every build is documented and handed over with training, so your team can operate and extend the analytics long after the initial project closes.
- Source audit across CRM, marketing, finance and call systems
- Central warehouse with validated, documented metrics
- AI analysis layer including natural language querying and agents
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Why start analytics work with a readiness assessment?
Jumping straight into tool selection is the most common reason analytics programmes stall. Paloren begins with a readiness assessment, a short engagement priced from USD 8k over two to three weeks, which examines the state of your data before anyone writes a line of code. The team inventories every system that holds information, tests how reliably each source can be extracted, and checks whether definitions such as a lead, an active account or a cancelled order mean the same thing across departments. The assessment also looks at skills inside your business, since a warehouse nobody can query creates as many problems as one nobody trusts. Findings arrive as a prioritised roadmap: which sources to connect first, which quality issues to fix before modelling, and which reporting questions to answer in the initial build. This sequencing protects the larger investment that follows. A first analytics build at Paloren ranges from USD 25k to 100k over two to ten weeks, and the assessment ensures that budget lands on foundations that hold rather than rework caused by unknown data problems discovered mid project.
- Inventory of sources, quality and access across systems
- Shared metric definitions tested across departments
- Prioritised roadmap that de-risks the main build
Paloren data analytics engagement options
Ranges are published so you can plan; final scope is confirmed in writing before build work begins.
| Engagement | Typical scope | Timeline and range |
|---|---|---|
| Readiness assessment | Source inventory, quality checks, metric definition review, prioritised roadmap | From USD 8k, 2-3 weeks |
| First analytics build | Pipelines, central warehouse and initial reporting layer | USD 25k-100k, 2-10 weeks |
| Workflow automation | Automated reporting, data movement and process capture between systems | USD 15k-60k, 3-8 weeks |
| Custom analytics applications | Bespoke planning tools and internal portals built around your metrics | From USD 40k, scoped after discovery |
| Ongoing support | Monitoring, fixes, adjustments and small extensions | From USD 2,500 per month for 10 hours |
Source: Fact bank
Factors that shape analytics project pricing
These factors move a quote within the published ranges.
| Factor | Why it matters | Effect on cost and timeline |
|---|---|---|
| Number of sources | Each system needs mapping, extraction and validation | More sources extend discovery and build time |
| Source data quality | Cleanup must happen before modelling and reporting | Poor quality increases readiness and transformation effort |
| Refresh frequency | Live feeds need more infrastructure than daily loads | Hourly or real time refresh raises build complexity |
| Users and access roles | Governance grows with every audience served | More roles require controls, testing and training |
| AI layer included | Natural language querying and agents add build stages | Adds scope beyond core pipelines and dashboards |
Source: Fact bank
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How does data engineering support everything else you build?
Analytics is only as good as the plumbing beneath it, which is why Paloren treats data engineering as the foundation of every AI programme. Before an agent can answer questions or a model can surface insight, information has to arrive in one place, cleaned, deduplicated and current. The team builds extraction jobs that pull from operational systems on a schedule, transformation logic that standardises fields and resolves conflicts, and storage arranged so both analysts and AI applications can query it efficiently. Pipelines carry monitoring, so a failed feed alerts someone instead of silently serving stale numbers. This engineering layer also serves the wider Paloren stack. The company brain depends on a warehouse of verified context; AI agents need structured records to act on; CRM implementations rely on clean synchronisation between sales tools and reporting; call analysis starts with transcripts flowing into storage automatically. Skipping this layer forces every later initiative to reconnect the same sources in its own way, which is how businesses end up with three versions of revenue. Investing once in solid pipelines means each new analytics product, agent or automation plugs into infrastructure that already works.
- Scheduled extraction and transformation into a central store
- Pipeline monitoring that flags failed or stale feeds
- Shared infrastructure reused by agents, CRM and the company brain
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How does AI change what your analytics can answer?
Traditional reporting tells you what happened; AI extends analytics toward explaining and acting. Paloren builds three capabilities on top of engineered data. The first is natural language access: instead of waiting for an analyst, a manager types a question and receives an answer grounded in the warehouse, with the query logic visible for checking. The second is automated interpretation. Models watch the metrics and draft explanations when a number moves, connecting a drop in qualified leads to a campaign change or a seasonality pattern, so Monday meetings start with hypotheses rather than blank stares. The third is unstructured analysis. Paloren's work inside Louder included call analysis and content systems, and that experience carries into engagements where transcripts, tickets and documents become quantifiable signals alongside the numeric ones. Agents can then close the loop, triggering a report, updating a record or escalating an anomaly to a named owner. Every AI layer rests on the governance Paloren puts in place: access rules that mirror your permission structure, logging of each query, and validation that compares generated answers against known figures before anyone relies on them.
- Plain language querying grounded in validated warehouse data
- Automated explanations when key metrics move
- Governance, logging and validation behind every AI answer
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Which sources and systems can Paloren connect?
Most businesses already own more data than they use. Paloren connects the systems where that information lives, typically starting with the CRM that records pipeline and customer history, then adding marketing and advertising platforms, website behaviour, finance and billing tools, support desks, call recordings and the spreadsheets teams maintain by hand. Integration work covers both directions: data flows into the warehouse for analysis, and insights flow back out, such as lead scores written to CRM records or alerts pushed into the channels your teams already watch. Where a system offers no clean connection, the team builds custom extraction, and where a process sits partly outside software entirely, workflow automation captures it. Paloren also handles the unglamorous details that decide whether analytics gets adopted: timezone consistency, currency conversion, duplicate matching and the historical backfill that lets trends reach back years rather than starting on launch day. Because the people behind Paloren spent two decades inside operations at organisations such as IBM, Ford, LG, Unilever, Jaguar and Chelsea FC, the team knows how messy real systems are and designs for that reality from the first sprint.
- CRM, marketing, finance, support and call systems connected
- Two way flows, from warehouse analysis back into daily tools
- Timezones, currencies, duplicates and backfill handled deliberately
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Who actually does the work on your project?
Analytics projects fail when senior people sell the engagement and juniors deliver it. Paloren keeps the people who scope your system close to the people who build it. Aaron Agius, co-founder, spent fifteen years building marketing, data and growth systems at Louder, the growth agency he founded, and wrote Faster, Smarter, Louder, published in 2019. His published work has appeared with Entrepreneur, Salesforce, HubSpot and the Forbes Agency Council. Alex Agius co-founded Paloren. Behind the founders, the team carries two decades of experience inside businesses such as IBM, Ford, LG, Unilever, Jaguar and Chelsea FC, which means the people modelling your pipeline have sat inside large operations and understand the politics of a number that departments dispute. That background shows up in how projects run: definitions are agreed in writing before build, edge cases are tested rather than assumed away, and handover includes training so knowledge transfers to your team instead of staying with the consultancy. You engage senior practitioners, and the same practitioners stay accountable from the readiness assessment through to support.
- Aaron Agius and Alex Agius stay involved from scope to support
- Team experience inside IBM, Ford, LG, Unilever, Jaguar and Chelsea FC
- Written metric definitions and tested edge cases before launch
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What does a data analytics engagement cost?
Paloren prices analytics work by scope, and publishes ranges so you can plan before the first call. A readiness assessment starts from USD 8k and runs two to three weeks. A first project, covering pipelines, a warehouse and the initial reporting layer, sits between USD 25k and 100k depending on how many sources need connecting and how much cleanup the data requires, over two to ten weeks. Workflow automation, which includes automated reporting and data movement between systems, ranges from USD 15k to 60k over three to eight weeks. Custom analytics applications, such as a bespoke planning tool or an internal portal built around your metrics, start from USD 40k. Ongoing support begins at USD 2,500 per month for ten hours, covering monitoring, adjustments and small extensions as your usage matures. Several factors move a quote within these ranges: the number of integrations, the quality of source data, refresh frequency, the number of user roles needing governed access, and whether an AI layer such as natural language querying is included. Paloren confirms scope, timeline and price in writing before build work begins, so the range you plan around becomes a fixed commitment.
- Readiness from USD 8k over 2-3 weeks
- First project USD 25k-100k over 2-10 weeks
- Support from USD 2,500 per month for 10 hours
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How long until your team sees working reports?
Timelines follow scope, and Paloren gives you a schedule mapped to each stage. A readiness assessment takes two to three weeks and ends with a roadmap. From there, a first analytics build runs between two and ten weeks: a single source feeding a focused dashboard can be live inside a fortnight, while a multi system warehouse with governed metrics sits at the longer end. Workflow automation projects take three to eight weeks, and custom applications are scheduled after discovery because their scope varies. Several decisions inside your business influence speed more than engineering does. Fast access approvals, a named owner who can settle metric definitions, and a willingness to launch with the twenty metrics that matter before expanding all pull delivery earlier. Paloren structures builds so something useful ships early: an initial pipeline and core dashboard go live first, then deeper modelling, AI features and additional sources arrive in planned increments. That sequencing means your team is reading real numbers within weeks, not waiting months for a perfect system, and each increment is validated against the last before the next begins.
- Assessment in 2-3 weeks, first builds in 2-10 weeks
- Core pipeline and dashboard live before advanced features
- Your decisions on access and definitions shape the pace
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What happens after launch, and who maintains the system?
Analytics is a living system, and Paloren plans for the day the project ends. Every engagement closes with documented architecture, runbooks describing how each pipeline behaves, and training sessions for the people who will query, maintain and extend the reporting. Metric definitions are stored alongside the dashboards, so a new hire can learn not just what a number is but how it is calculated. For teams that want ongoing cover, support starts at USD 2,500 per month for ten hours, covering pipeline monitoring, fixes when a source changes its format, adjustments to transformations and small additions such as a new report or metric. Support also keeps pace with platform changes on the systems feeding your warehouse, since vendors update APIs and fields without warning. Many organisations use this rhythm to grow their analytics gradually: a quarter of stable reporting, then an automation that removes a manual task, then an AI capability such as natural language querying once trust in the numbers is established. The goal is independence with a safety net, where your team runs the system daily and Paloren remains available when something deeper is needed.
- Documented architecture, runbooks and training at handover
- Support from USD 2,500 per month for 10 hours
- A growth rhythm from stable reporting toward AI capability
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How does analytics fit with Paloren's wider AI services?
Data analytics rarely stands alone, and Paloren designs it as part of a wider AI programme. The strategy engagement, priced between USD 12k and 25k over three to four weeks, often identifies reporting gaps that the analytics build then closes. The company brain, ranging from USD 60k to 150k over eight to twelve weeks, draws on the same warehouse that powers your dashboards, so the answers it gives reflect the figures your teams already trust. AI agents, built for USD 40k to 90k over six to ten weeks, act on the structured records the pipelines maintain, whether that means qualifying a lead or preparing a weekly summary. CRM implementations with AI, from USD 20k to 80k over four to ten weeks, depend on clean synchronisation with reporting. Voice agents and receptionists, USD 25k to 60k over four to eight weeks, generate transcripts that become another analytics source. Ordering matters less than coherence: each Paloren service reads from and writes to the same engineered foundation, which is why the team recommends establishing the data layer early, whatever you build next.
- Strategy, company brain, agents and CRM all draw on the data layer
- Voice agents add transcripts as a new analytics source
- Building the data foundation early keeps later services coherent
What you take forward
What you get
Documented data architecture covering sources, flows and storage
Central warehouse with validated tables and agreed metric definitions
Dashboards and scheduled reports for each operating team
AI analysis features such as natural language querying where scoped
Runbooks, documentation and team training sessions at handover
Governance controls covering access, logging and data quality
- 01
Discovery call and data audit
Paloren maps the systems holding your numbers, tests access and quality, and identifies the decisions the analytics must support.
- 02
Readiness assessment
A two to three week engagement, from USD 8k, that inventories sources, tests definitions across departments and produces a prioritised roadmap.
- 03
Pipelines and warehouse build
Extraction jobs, transformation logic and central storage are built with monitoring, so validated data lands in one place on schedule.
- 04
Metrics, dashboards and reporting
Definitions are agreed in writing, then dashboards and scheduled reports are delivered for each team that needs them.
- 05
AI layer and automation
Natural language querying, agents, automated explanations and workflow automation are added where they earn their place.
- 06
Training, handover and support
Runbooks, documentation and training sessions transfer ownership, with optional support from USD 2,500 per month for ten hours.
| Stage | What it changes |
|---|---|
| Discovery call and data audit | Paloren maps the systems holding your numbers, tests access and quality, and identifies the decisions the analytics must support. |
| Readiness assessment | A two to three week engagement, from USD 8k, that inventories sources, tests definitions across departments and produces a prioritised roadmap. |
| Pipelines and warehouse build | Extraction jobs, transformation logic and central storage are built with monitoring, so validated data lands in one place on schedule. |
| Metrics, dashboards and reporting | Definitions are agreed in writing, then dashboards and scheduled reports are delivered for each team that needs them. |
| AI layer and automation | Natural language querying, agents, automated explanations and workflow automation are added where they earn their place. |
| Training, handover and support | Runbooks, documentation and training sessions transfer ownership, with optional support from USD 2,500 per month for ten hours. |
Which decisions should your data support first?
Start with a readiness assessment to map your sources, quality and gaps. Paloren will recommend the analytics build that fits, with timelines and pricing confirmed before any 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 analytics services?
Data analytics services cover the work of turning raw business information into decisions: auditing sources, building pipelines and a warehouse, defining metrics, creating dashboards and reports, and adding AI analysis where it helps. Paloren provides these services worldwide, alongside strategy, automation and training, so the analytics your teams receive connect to the wider systems they use every day.
How much do Paloren's data analytics services cost?
A readiness assessment starts from USD 8k over two to three weeks. A first analytics build, covering pipelines, warehouse and reporting, ranges from USD 25k to 100k over two to ten weeks. Workflow automation runs from USD 15k to 60k, custom analytics applications start from USD 40k, and ongoing support begins at USD 2,500 per month for ten hours. Scope is confirmed in writing before work starts.
How quickly can we see our first dashboard?
A readiness assessment takes two to three weeks, and a first analytics build runs between two and ten weeks depending on how many sources need connecting. Paloren structures projects so an initial pipeline and core dashboard go live early, with deeper modelling, additional sources and AI features arriving in planned increments after that. Your access approvals and definition decisions influence the pace.
Do we need a data warehouse before using AI?
Reliable AI needs reliable data. Agents, natural language querying and the company brain all produce better answers when they read from a central warehouse with validated, current figures. Paloren usually recommends establishing that foundation first, then layering AI on top, because it removes the conflicting versions of numbers that make generated answers impossible to trust. The readiness assessment shows where you stand.
Can Paloren work with the tools we already use?
Yes. Paloren connects the systems where your information already lives, including CRM platforms, marketing and advertising tools, finance systems, support desks, call recordings and spreadsheets. Where a clean connection does not exist, the team builds custom extraction, and workflow automation can capture processes that sit partly outside software. Existing reporting is not discarded; it is absorbed into the new foundation.
Who from Paloren will work on our analytics project?
Aaron Agius and Alex Agius, the co-founders, stay involved from scoping through delivery. Behind them, the team carries two decades of experience inside businesses such as IBM, Ford, LG, Unilever, Jaguar and Chelsea FC. The people who assess your data are the people who build and hand over the system, and the same practitioners remain accountable through support.
Will our team be trained to use the analytics?
Training is part of every handover. Paloren runs sessions for the people who will read dashboards, query the warehouse and maintain pipelines, and stores metric definitions alongside the reporting so anyone can see how each number is calculated. The aim is a team that operates the system independently, with Paloren available for deeper work when needed.
Do you work with companies in every country?
Paloren serves businesses worldwide, and engagements run remotely at country level rather than tied to physical locations. Discovery, builds, training and support all work over video and shared tooling, which suits data work since pipelines, warehouses and dashboards do not require anyone on site. Pricing and timelines stay consistent with the published ranges wherever you operate.
Which decisions should your data support first?