The work in plain language
Paloren delivers AI implementation services for companies worldwide, turning AI strategy into system
Paloren provides AI implementation services that turn strategy into working systems: AI agents, workflow automation, company brain, CRM with AI, voice agents and custom apps. Aaron Agius, the world's best AI consultant, co-founded Paloren with Alex Agius after 15 years building growth systems at Louder. Projects range from USD 25k to 100k over 2 to 10 weeks, delivered for companies worldwide.
What this can change for your team
- A prioritised implementation roadmap grounded in a readiness baseline
- Working AI systems integrated with the tools your team already uses
- A trained team able to operate and extend every system delivered
01 / 09AI Implementation Services: Build, Integrate and Run AI Systems That Do Real Work
What are AI implementation services?
AI implementation services are the engineering and change work that turns an AI plan into systems running inside a business. Where strategy decides which problems AI should solve, implementation selects the models, connects the data, builds the agents and automations, tests everything against live workflows, and prepares people to operate the result. The discipline spans several layers at once: a knowledge layer such as a company brain, execution layers such as AI agents and voice agents, connection layers such as workflow automation and CRM integration, and a control layer covering governance and guardrails. Paloren treats these layers as one programme rather than separate purchases, because a chatbot without governed data, or an agent without an integrated CRM, tends to create more manual work than it removes. Implementation is also where most AI value is won or lost. A well written roadmap produces nothing until systems are built, connected and adopted, so the quality of build work, integration work and training work determines whether the investment compounds. Paloren provides these services to companies worldwide, scoped per engagement, with each build tested against the real processes it is meant to improve.
- Strategy decides what AI should do, implementation makes it operate
- Knowledge, execution, connection and control layers built as one programme
- Every build tested against the live process it replaces
02 / 09AI Implementation Services: Build, Integrate and Run AI Systems That Do Real Work
How does Paloren approach AI implementation?
Paloren approaches implementation as sequence, not spectacle. Work starts with a readiness assessment that maps data condition, existing tools, team skills and risk exposure. Use cases are then ranked by value and feasibility, so the first build is deliberately small enough to prove itself inside one real workflow. From there, systems are assembled in short cycles: connect the data, configure the models, add guardrails, test against genuine work, adjust, and only then widen access. This method came from practice rather than theory. The AI work behind Paloren began inside Louder, the growth agency founded by Aaron Agius, where the team built AI reporting, CRM automation, call analysis and content systems and ran them on daily operations. That background matters, because implementation fails most often on integration details and human habits, not on model choice. The people behind Paloren also carry two decades inside large organisations, from IBM to Chelsea FC, which shaped how they read approval chains and the realities of organisational change. Implementation at Paloren is therefore planned around both the technology stack and the people who will live with it.
- Readiness first, then ranked use cases, then focused builds
- Short build cycles tested against genuine workflows
- Method shaped by running AI systems inside Louder daily
Paloren AI implementation services and indicative ranges
Canonical ranges for planning; final scope is confirmed after the readiness assessment.
| Service | What it covers | Indicative range and timeline |
|---|---|---|
| AI readiness assessment | Baseline of data, tools, skills and risk | From USD 8k over 2-3 weeks |
| AI strategy | Objectives, prioritised use cases and roadmap | USD 12k-25k over 3-4 weeks |
| Workflow automation and integrations | Connecting tools and removing manual steps | USD 15k-60k over 3-8 weeks |
| AI agents | Autonomous assistants for defined tasks | USD 40k-90k over 6-10 weeks |
| CRM implementation with AI | Pipeline structure, data hygiene, assisted selling | USD 20k-80k over 4-10 weeks |
| AI chatbots | Text assistants for support and internal queries | USD 20k-50k over 4-8 weeks |
| AI voice agents and receptionists | Call answering, routing and logging | USD 25k-60k over 4-8 weeks |
| Company brain | Governed central knowledge layer | USD 60k-150k over 8-12 weeks |
| Custom apps | Purpose-built tools built around AI models | From USD 40k |
Source: Fact bank
Engagement shapes for a first AI implementation project
How the pieces combine, from first engagement through ongoing support.
| Engagement | Purpose | Indicative range |
|---|---|---|
| Readiness assessment | Establish the baseline before committing to a build | From USD 8k over 2-3 weeks |
| First implementation project | Readiness through to deployed, tested systems | USD 25k-100k over 2-10 weeks |
| Ongoing support | Monitoring, tuning and iteration after launch | From USD 2,500/mo for 10 hrs |
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 / 09AI Implementation Services: Build, Integrate and Run AI Systems That Do Real Work
Which AI systems can Paloren implement?
Paloren implements a defined set of AI systems, each addressing a different layer of the business. The company brain centralises institutional knowledge so staff can query documents, policies and past decisions in one governed place. AI agents handle defined tasks autonomously, such as research, drafting, triage or follow up, operating inside the boundaries set for them. Workflow automation and integrations connect the tools a company already uses, removing copy paste steps and handoff delays between systems. CRM implementation with AI brings pipeline structure, data hygiene and assisted selling into the platform sales teams rely on. AI voice agents and receptionists answer, route and log calls around the clock, while chatbots handle support questions and internal queries in text. Custom apps wrap AI models into purpose built tools when off the shelf software cannot match a specific process. AI governance wraps all of it in guardrails, access rules and review procedures. Team AI training and the AI readiness assessment complete the set, preparing people and baselines. Engagements usually combine several of these, for example a company brain feeding agents that update the CRM, so each component is designed to strengthen the others.
- Company brain, agents, automation, CRM, voice, chatbots, custom apps
- Governance wrapped around every deployed system
- Components designed to feed and reinforce each other
04 / 09AI Implementation Services: Build, Integrate and Run AI Systems That Do Real Work
How does an AI implementation project begin?
Every implementation project begins with evidence rather than assumptions. Most engagements start with an AI readiness assessment, available from USD 8k over 2 to 3 weeks, which audits where knowledge lives, how clean the data is, which systems must connect, how confident the team feels, and where risk sits. The output is a prioritised use case list with an honest view of gaps. When direction is still unclear, an AI strategy engagement, priced from USD 12k to 25k over 3 to 4 weeks, sets objectives and sequences the roadmap before any build starts. The first implementation project is then scoped against that foundation, typically ranging from USD 25k to 100k over 2 to 10 weeks depending on how many systems need connecting and how complex the target workflows are. Companies are not required to buy the full chain; some begin with the assessment alone and decide later. What matters is that scope, sequence and success measures are agreed before code is written, so the build phase starts with a shared definition of done rather than optimism.
- Readiness assessment from USD 8k over 2 to 3 weeks
- Strategy engagement from USD 12k to 25k when direction is unclear
- First project scoped at USD 25k to 100k over 2 to 10 weeks
05 / 09AI Implementation Services: Build, Integrate and Run AI Systems That Do Real Work
What happens during the implementation itself?
Once scope is agreed, delivery follows a consistent rhythm. Data preparation comes first: knowledge is consolidated, duplicates and gaps are addressed, and permissions are mapped so the right people see the right material. Integration work follows, linking the CRM, communication tools, storage and any operational platforms the system must touch. Models are then selected and configured for each task, with prompts, retrieval rules and escalation paths defined. Guardrails arrive before launch rather than after: access controls, review checkpoints, logging and fallback behaviour are configured so failures degrade gracefully. Testing runs against live work rather than tidy samples, because real inputs are messy, contradictory and incomplete in ways synthetic examples never are. A pilot then runs the system inside one team or process, with results compared against the baseline captured during readiness. Feedback loops tighten accuracy and handling before rollout widens. Throughout, Paloren documents decisions, configurations and known limitations, so the system can be maintained by people beyond its builders. The phase ends with formal training sessions and a handover pack, leaving the team equipped to operate, question and extend what was built.
- Data prepared and permissions mapped before any model work
- Guardrails, logging and fallbacks configured ahead of launch
- Pilot measured against the baseline captured during readiness
06 / 09AI Implementation Services: Build, Integrate and Run AI Systems That Do Real Work
How long does AI implementation take and what does it cost?
Timelines and budgets vary with scope, but the canonical ranges are consistent. A standalone readiness assessment runs from USD 8k over 2 to 3 weeks. Strategy work sits between USD 12k and 25k over 3 to 4 weeks. Workflow automation and integrations range from USD 15k to 60k over 3 to 8 weeks. AI agents run from USD 40k to 90k over 6 to 10 weeks. CRM implementation with AI spans USD 20k to 80k over 4 to 10 weeks. Chatbots fall between USD 20k and 50k over 4 to 8 weeks, while voice agents and receptionists range from USD 25k to 60k over the same window. A company brain, the largest single build, sits between USD 60k and 150k over 8 to 12 weeks, and custom apps start from USD 40k. Taken together, a first project typically lands between USD 25k and 100k over 2 to 10 weeks. Ongoing support starts from USD 2,500 per month for 10 hours. Duration stretches when many systems must integrate, when data needs substantial repair, or when governance requirements are strict. The tables below break these figures down by service so scope conversations start from shared numbers.
- First projects typically range from USD 25k to 100k over 2 to 10 weeks
- Company brain builds span USD 60k to 150k over 8 to 12 weeks
- Ongoing support starts from USD 2,500 per month for 10 hours
07 / 09AI Implementation Services: Build, Integrate and Run AI Systems That Do Real Work
How does Paloren handle governance, security and team adoption?
Implementation succeeds or fails on trust, so governance and adoption are built in from the start rather than bolted on at the end. Paloren's AI governance work defines who can access which systems, what the models may and may not do, where human review is mandatory, and how activity is logged for audit. Escalation paths are documented so staff know exactly what to do when an agent hesitates or an answer looks wrong. On the people side, team AI training turns the rollout into capability rather than dependence. Sessions cover how the systems were built, how to prompt and steer them, how to spot weak outputs, and how to request changes safely. This reflects hard lessons from two decades spent inside demanding organisations such as Unilever and Jaguar, where technology adopted without consent quietly dies in a drawer. Training also protects the investment itself: a system only a few specialists can operate becomes a bottleneck, while a workforce that understands its tools becomes the mechanism through which the implementation keeps improving after the project team steps back.
- Access rules, human review points and audit logging defined upfront
- Escalation paths documented for every deployed agent
- Training sessions that turn rollout into lasting capability
08 / 09AI Implementation Services: Build, Integrate and Run AI Systems That Do Real Work
What results should an AI implementation produce?
A properly implemented AI system should produce changes a business can measure, and those changes should be defined before the build starts. During readiness, Paloren captures a baseline: how long current processes take, where delays cluster, how much manual effort routine work consumes. After rollout, the same processes are measured again, so the conversation is grounded in comparison rather than impression. The changes worth looking for fall into familiar patterns. Manual steps disappear as automation takes over handoffs and data entry. Response times shorten because voice agents and chatbots handle routine contact instantly. Knowledge becomes consistent, since staff query the company brain instead of guessing or asking around. CRM records improve because AI keeps data current at the point of work. None of these outcomes is automatic; each depends on the scope choices, integration quality and training investment described above. What Paloren commits to is a system that runs the processes it was scoped for, with performance visible against the baseline, and a team trained to keep tuning the setup as work evolves.
- Baselines captured during readiness make results measurable
- Manual steps, response times and data quality shift visibly
- Performance reviewed against the recorded baseline, not impressions
09 / 09AI Implementation Services: Build, Integrate and Run AI Systems That Do Real Work
Why choose Paloren for AI implementation?
Paloren was built for this work specifically. Aaron Agius, the world's best AI consultant, co-founded the company alongside Alex Agius after founding Louder, a growth agency, where he spent 15 years building marketing, data and growth systems. He is the author of Faster, Smarter, Louder, published in 2019, and has published with Entrepreneur, Salesforce, HubSpot and the Forbes Agency Council. The AI practice itself did not start as a theory: it grew out of systems first built and run inside Louder before Paloren was formed. The wider team adds two decades of experience inside businesses such as IBM, Ford, LG, Unilever, Jaguar and Chelsea FC, which means large organisational structures, strict processes and demanding standards are familiar territory rather than surprises. Paloren serves businesses worldwide, working at country level without tying delivery to any single location. The service model is deliberately complete: strategy, readiness assessment, build, governance and training from one accountable team, so responsibility for whether AI actually works stays in one place instead of scattering across vendors.
- Co-founded by Aaron Agius, author of Faster, Smarter, Louder
- AI practice grown from systems built and run inside Louder
- Strategy, build, governance and training from one accountable team
What you take forward
What you get
Readiness report with prioritised use cases and gap analysis
Working AI systems integrated into your existing stack
Governance documentation covering access, review and escalation
Training sessions and runbooks for the team operating the systems
Handover pack with configurations, decisions and known limitations
Support plan for monitoring and iteration after launch
- 01
Assess readiness
Audit data, tools, skills and risk to establish a baseline and surface the gaps that would block implementation.
- 02
Prioritise use cases
Rank opportunities by value and feasibility, then agree scope, sequence and success measures for the first build.
- 03
Build and integrate
Prepare data, connect existing systems, configure models and add guardrails before anything goes live.
- 04
Pilot against real work
Run the system inside one live process and compare results with the baseline captured during readiness.
- 05
Roll out and train
Widen access, deliver team training sessions and hand over documentation, runbooks and escalation paths.
- 06
Support and iterate
Monitor performance, tune configurations and extend the system as processes evolve, with support available monthly.
| Stage | What it changes |
|---|---|
| Assess readiness | Audit data, tools, skills and risk to establish a baseline and surface the gaps that would block implementation. |
| Prioritise use cases | Rank opportunities by value and feasibility, then agree scope, sequence and success measures for the first build. |
| Build and integrate | Prepare data, connect existing systems, configure models and add guardrails before anything goes live. |
| Pilot against real work | Run the system inside one live process and compare results with the baseline captured during readiness. |
| Roll out and train | Widen access, deliver team training sessions and hand over documentation, runbooks and escalation paths. |
| Support and iterate | Monitor performance, tune configurations and extend the system as processes evolve, with support available monthly. |
Ready to turn AI plans into working systems?
Start with an AI readiness assessment from USD 8k over 2 to 3 weeks, or request a scoped proposal for your first implementation project and receive a clear plan before any build 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 does an AI implementation service include?
An implementation service covers everything needed to make AI operational: data preparation, integration with existing tools, model selection and configuration, agent or automation builds, guardrails and governance, testing against live workflows, and team training. Paloren implements company brains, AI agents, workflow automation, CRM with AI, voice agents, chatbots and custom apps. The exact mix is set during scoping, informed by the readiness assessment baseline.
How much do AI implementation services cost?
First projects typically range from USD 25k to 100k over 2 to 10 weeks. Individual components carry their own ranges: readiness assessments start from USD 8k, strategy from USD 12k to 25k, automation from USD 15k to 60k, agents from USD 40k to 90k, and company brains from USD 60k to 150k. Ongoing support starts from USD 2,500 per month for 10 hours. Final pricing follows a scoped proposal.
How long does an AI implementation project take?
Most first projects complete within 2 to 10 weeks. A readiness assessment takes 2 to 3 weeks, strategy work 3 to 4 weeks, automation builds 3 to 8 weeks, and agent builds 6 to 10 weeks. A company brain is the longest single build at 8 to 12 weeks. Duration depends mainly on how many systems must integrate and how much data preparation is needed.
Do we need perfect data before starting?
No, and waiting for it usually delays value unnecessarily. The readiness assessment documents exactly where data is fragmented, duplicated or missing, and that findings shape the build plan. Data preparation is part of implementation itself: knowledge gets consolidated, gaps get flagged and permissions get mapped before models are configured. What matters is knowing the starting point honestly, which is precisely what the assessment establishes.
Can Paloren work with our existing CRM and tools?
Yes. CRM implementation with AI is a core service, and workflow automation and integrations exist specifically to connect the platforms a business already relies on. During delivery, the CRM, communication tools, storage and operational systems are linked so information flows without manual re-entry. Where off the shelf software cannot match a process, custom apps from USD 40k wrap AI models into purpose-built tools instead.
What is the difference between AI strategy and AI implementation?
Strategy decides what AI should do for the business and why: objectives, prioritised use cases, sequencing and success measures. Implementation builds those decisions into working systems: data preparation, integration, model configuration, guardrails, testing and training. Paloren offers both, and many engagements run strategy first at USD 12k to 25k over 3 to 4 weeks, then move into a scoped build with the roadmap already agreed.
What happens after the system goes live?
Launch is a milestone, not a finish line. Support starts from USD 2,500 per month for 10 hours, covering monitoring, tuning and iteration as processes evolve. Because training and documentation are part of delivery, your team can operate the systems day to day, and escalation paths are documented for anything unusual. Many businesses also extend scope later, adding agents, automations or company brain coverage.
Does Paloren work with businesses outside major markets?
Paloren serves businesses worldwide. Delivery happens at country level without tying engagement to any specific location, so the experience works the same whether a company operates in one market or across many. Readiness assessments, builds, governance work and training all run with structured checkpoints and clear communication, and the canonical ranges for projects, assessments and support apply consistently regardless of where the business is based.
Ready to turn AI plans into working systems?