AI implementation that moves from pilot to production
Paloren delivers AI implementation worldwide: agents, automation, CRM with AI, integrations and governance, led by Aaron Agius. Projects from USD 25k.
Leaders ready to move AI from experiments into dependable production systems
Paloren provides AI implementation for companies worldwide, taking artificial intelligence from assessment and strategy through to agents, automation, integrations and CRM systems running in production. First projects range from USD 25k to 100k over two to ten weeks.
AI implementation is the work of putting artificial intelligence into daily operation inside a business.
It covers the full path from a defined use case to a live system that staff rely on: selecting the right models, wiring them into existing tools, building agents and automations around them, setting governance rules, and training the people who will use the result.
Implementation is distinct from strategy, which decides where AI should be applied, and from assessment, which establishes how prepared the organisation is. Implementation is where value appears or disappears.
A model that performs well in a demo can fail in production when it meets real data, real edge cases and real users.
Paloren treats implementation as an engineering discipline with a delivery structure: discovery of the workflow, design of the system, staged builds, integration with the CRM and other core platforms, testing against live conditions, then release with monitoring and support.
The work, made concrete
Discovery, design, build, integration, testing and release in one managed path
Governance and training included so systems stay safe after launch
Most stalled AI projects share the same failure patterns. The first is starting with the technology instead of the workflow: a team buys access to a model, then hunts for a problem to justify it.
The second is missing data foundations, where the system cannot reach the knowledge or records it needs to be useful. The third is integration debt, where the AI sits beside the CRM and core platforms instead of inside them, so staff must duplicate work to use it.
The fourth is absent ownership, where nobody is accountable for accuracy, cost or adoption once the demo ends. The fifth is skipped training, where employees receive a new tool with no guidance and quietly return to old habits.
Paloren designs against each of these patterns from day one.
Every implementation begins with the workflow that needs improvement, confirms data access before build, embeds AI into the platforms teams already use, assigns clear accountability for the running system, and includes team AI training so adoption is planned rather than hoped for.
This is why readiness assessment and strategy often precede implementation, because they remove the conditions that cause stalls before a single component is built.
| Service | Indicative range (USD) | Typical duration |
|---|---|---|
| First project | 25k to 100k | 2 to 10 weeks |
| Readiness assessment | From 8k | 2 to 3 weeks |
| AI strategy | 12k to 25k | 3 to 4 weeks |
| Company brain | 60k to 150k | 8 to 12 weeks |
| AI agents | 40k to 90k | 6 to 10 weeks |
| Workflow automation and integrations | 15k to 60k | 3 to 8 weeks |
| CRM implementation with AI | 20k to 80k | 4 to 10 weeks |
| Chatbot | 20k to 50k | 4 to 8 weeks |
| Voice agent and receptionist | 25k to 60k | 4 to 8 weeks |
| Custom apps | From 40k | Scoped at discovery |
| Ongoing support | From 2,500 per month | 10 hours monthly |
Guide ranges confirmed during discovery; every engagement is scoped before build.
Source: Paloren published service ranges
| Stall point | What it looks like | Paloren response |
|---|---|---|
| Technology first | A model is bought before a workflow is chosen | Discovery starts from the workflow and its measurable outcome |
| Data gaps | The system cannot reach the knowledge it needs | Data access is confirmed before build; readiness assessment available |
| Integration debt | AI sits beside core platforms instead of inside them | CRM, reporting and communication tools are connected during build |
| Missing ownership | Nobody answers for accuracy or cost after the demo | Accountability is assigned at design and monitored after release |
| Skipped training | Staff revert to old habits within weeks | Team AI training is included so adoption is planned |
Each stall point is addressed within the delivery sequence rather than after launch.
Source: Paloren published service ranges
The work, made concrete
Technology-first thinking produces demos without business value
Weak data access and poor integration keep AI stranded
No ownership or training means adoption fades after launch
Paloren runs implementation as a sequence of controlled stages, each with defined outputs. Work starts with discovery inside your operation: mapping the workflows, systems and data that the target process touches, and agreeing the measurable outcome the build must deliver.
Design follows, where the architecture is specified: which models, which agents, which integrations, which guardrails. Build then happens in increments, so you see working software early rather than a reveal at the end.
Integration is treated as a first-class task, connecting the AI to your CRM, reporting stack and communication tools so it operates where people already work. Testing runs against live conditions, including the awkward edge cases that demos avoid.
Release comes with monitoring, governance settings and documentation. Support continues afterwards, with retained hours available from USD 2,500 per month for ten hours, so the system keeps improving as usage grows.
The work, made concrete
Discovery, design, incremental build, integration, testing, release and support
Working software shown early through staged builds
Retained support from USD 2,500 per month for ten hours
Implementation covers the full Paloren service set, and most engagements combine several. AI agents handle bounded tasks such as research, drafting, qualification and internal Q&A, built on your company brain so answers stay grounded in your own knowledge.
Workflow automation and integrations connect the tools you already run, moving information between systems without manual re-entry. CRM implementation with AI places intelligence directly inside the sales and service platform, from automated logging to next-step guidance.
AI voice agents and receptionists answer calls, capture detail and route conversations at any hour. Custom apps, built from USD 40k, deliver purpose-built interfaces when off-the-shelf tools cannot match the process.
AI governance wraps the whole estate with policies, permissions and review controls, which matters most once multiple systems are live. Chatbots, ranging from USD 20k to 50k, extend service and internal support around the clock.
The selection is not a menu to order from blindly: discovery determines which combination fits the workflow, the data and the budget. Many programmes start with one system, prove it in production, then extend to the next, which keeps risk contained and value visible from the first release.
The work, made concrete
AI agents, company brain, workflow automation and integrations
CRM with AI, voice agents and receptionists, chatbots
Custom apps, AI governance and team AI training
Implementation succeeds when it starts from an accurate picture, which is why Paloren offers the readiness assessment and AI strategy as precursors.
The readiness assessment, from USD 8k over two to three weeks, examines your data, systems, skills and governance posture, then reports where AI can land safely today and where foundations need work first.
Strategy, from USD 12k to 25k over three to four weeks, turns that picture into a prioritised roadmap: which workflows to automate, in what order, with which expected outcomes.
Implementation then executes the roadmap without re-litigating decisions mid-build. Businesses can enter directly at implementation when their foundations are already sound, and discovery includes a light check of data access and system compatibility before build begins.
The sequence matters because implementation inherits every weakness upstream: a strategy built on guesswork produces builds aimed at the wrong workflows, and an assessment skipped entirely can surface blockers mid-project, when they cost the most to fix.
Companies that complete the full path, assessment to strategy to build, generally reach production with fewer surprises, because questions about data quality, permissions and adoption were answered before engineering time was committed.
The work, made concrete
Readiness assessment from USD 8k over two to three weeks
Strategy from USD 12k to 25k over three to four weeks
Direct entry to implementation is possible when foundations are sound
The company brain is Paloren's central knowledge layer, and it changes what implementation can achieve. Without it, each agent or automation depends on whatever context you manually paste into it, which produces inconsistent output and repeated setup work.
With a company brain in place, every implemented system draws from one governed source: your documents, records, policies and procedures, organised so models can retrieve accurate context at the moment of use.
A sales agent quoting from current pricing, a voice receptionist answering policy questions and a chatbot resolving support queries all rely on the same foundation, which keeps answers consistent across channels.
The company brain is a substantial build, from USD 60k to 150k over eight to twelve weeks, because it involves structuring knowledge, setting permissions, establishing refresh cycles and testing retrieval quality.
For organisations not ready for that scope, individual implementations can begin with narrower knowledge scoped to one workflow, then expand toward the full brain as confidence grows.
The principle holds at any scale: AI implemented without controlled access to company knowledge produces confident guesses, while AI implemented on top of a maintained knowledge layer produces answers a business can defend.
The work, made concrete
One governed knowledge source feeding every implemented system
Consistent answers across agents, chatbots and voice systems
Full build from USD 60k to 150k over eight to twelve weeks
Delivery sits with the Paloren team co-founded by Aaron Agius and Alex Agius. Paloren combines implementation, workflow design and practical team training.
That blend matters for implementation specifically, because the work demands both engineering depth and operational judgement: knowing how a CRM behaves under real sales pressure, how reporting is consumed by executives, and how change lands with teams.
Paloren serves businesses worldwide, and engagements are structured so delivery works across time zones without depending on a single location. Engagements are scoped, built and released by the same senior team, so knowledge does not get lost between the first conversation and the final release.
For organisations comparing providers, the question worth asking is who exactly will be in the room, and at Paloren the answer is the senior team from the first call.
The work, made concrete
Co-founded by Aaron Agius and Alex Agius
Team experience inside IBM, Ford, LG, Unilever, Jaguar and Chelsea FC
Senior involvement from scoping through release
First projects with Paloren range from USD 25k to 100k and run two to ten weeks, with the figure driven by scope: how many systems are in play, how much integration is required and how complex the underlying data is.
Individual service lines carry their own ranges. Agents run USD 40k to 90k over six to ten weeks. Workflow automation runs USD 15k to 60k over three to eight weeks. CRM implementation with AI runs USD 20k to 80k over four to ten weeks.
Chatbots run USD 20k to 50k over four to eight weeks. Voice agents and receptionists run USD 25k to 60k over four to eight weeks. Custom apps start from USD 40k, and the company brain spans USD 60k to 150k over eight to twelve weeks.
Ongoing support starts from USD 2,500 per month for ten hours, covering monitoring, refinement and iteration after release. Ranges are guides rather than quotes: discovery produces a fixed scope and a figure attached to it.
Businesses with tighter budgets often begin with a readiness assessment or a single automation, then expand once the first system proves itself in production.
The work, made concrete
First projects USD 25k to 100k over two to ten weeks
Service-level ranges from automation at USD 15k to company brain at USD 150k
Support from USD 2,500 per month for ten hours
Paloren frames measurement around the workflow the build was meant to change, not around model statistics. During discovery, the baseline is recorded: how long the task takes today, how many people touch it, where errors appear and what the current cost of delay looks like.
After release, the same measures are tracked against that baseline, so the conversation is grounded in movement rather than impressions. Adoption is treated as a measure in its own right, because a technically sound system that staff avoid has not changed anything.
Usage logs, exception rates and handoff volumes show whether the AI is genuinely absorbing work. Governance measures sit alongside: accuracy sampling, permission audits and review of escalations, which keep the system trustworthy as it matures.
Where a build feeds a CRM, pipeline and service metrics connect directly to the AI activity, closing the loop between the technology and the commercial outcome it was funded to deliver.
The work, made concrete
Baseline captured during discovery, tracked after release
Adoption and usage treated as core measures
Preparation shortens timelines more than any other factor. The most valuable contributions are practical: a named decision-maker with authority to approve scope, access to the systems the build will touch, and a shortlist of workflows with honest detail about how they run today.
Documentation helps, but imperfect documentation is workable; what matters is that someone who performs the process can walk the team through it. Data location should be known, even if the data itself is untidy, because discovery can plan around structure that exists.
Existing tooling should be listed, including the CRM in use, reporting platforms and communication tools, since integration design depends on it. Internally, it helps to agree what success looks like in one sentence, because that sentence becomes the reference point for every build decision that follows.
Paloren does not require a completed AI strategy before starting; discovery includes the checks needed to confirm the ground is solid.
Teams that arrive with a clear owner, system access and a defined first workflow typically move from kickoff to working software faster, and spend more of the budget on building rather than on waiting.
The work, made concrete
A named decision-maker with authority over scope
System access and a list of current tooling
One defined workflow with an honest description of how it runs
Map the target workflow, confirm data access and system compatibility, agree the measurable outcome and produce a fixed scope with timeline.
Specify models, agents, integrations and governance controls, and define how the system will be tested against live conditions.
Construct the system in stages, connecting the CRM, reporting stack and communication tools so working software appears early.
Run the build against real edge cases, apply governance settings, document the system and release with monitoring in place.
Refine the system as usage grows, with retained hours from USD 2,500 per month for ten hours available for ongoing improvement.
| Service | Indicative range (USD) | Typical duration |
|---|---|---|
| First project | 25k to 100k | 2 to 10 weeks |
| Readiness assessment | From 8k | 2 to 3 weeks |
| AI strategy | 12k to 25k | 3 to 4 weeks |
| Company brain | 60k to 150k | 8 to 12 weeks |
| AI agents | 40k to 90k | 6 to 10 weeks |
| Workflow automation and integrations | 15k to 60k | 3 to 8 weeks |
| CRM implementation with AI | 20k to 80k | 4 to 10 weeks |
| Chatbot | 20k to 50k | 4 to 8 weeks |
| Voice agent and receptionist | 25k to 60k | 4 to 8 weeks |
| Custom apps | From 40k | Scoped at discovery |
| Ongoing support | From 2,500 per month | 10 hours monthly |
Guide ranges confirmed during discovery; every engagement is scoped before build.
Paloren published service ranges
| Stall point | What it looks like | Paloren response |
|---|---|---|
| Technology first | A model is bought before a workflow is chosen | Discovery starts from the workflow and its measurable outcome |
| Data gaps | The system cannot reach the knowledge it needs | Data access is confirmed before build; readiness assessment available |
| Integration debt | AI sits beside core platforms instead of inside them | CRM, reporting and communication tools are connected during build |
| Missing ownership | Nobody answers for accuracy or cost after the demo | Accountability is assigned at design and monitored after release |
| Skipped training | Staff revert to old habits within weeks | Team AI training is included so adoption is planned |
Each stall point is addressed within the delivery sequence rather than after launch.
Working AI systems live in production, integrated with your existing platforms
Documented architecture, governance settings and testing records
Team AI training so staff adopt the new systems with confidence
AI reporting tracking performance against the discovery baseline
A support arrangement for monitoring and refinement after release
Signal → Synthesis → System → Scale
Find where intelligence creates value.
Understand the business to identify and prioritize the opportunities with the greatest measurable impact.
Translate complexity into a clear design.
Bring together the people, workflows, data, and technology to define how intelligence should work.
Turn the design into a working capability.
Build the solution that embeds intelligence into how work and decisions happen.
Compound what works.
Measure impact, optimize performance, maintain reliability, and unlock greater leverage.
From signal to scale.
Implementation is where AI value appears or disappears. Paloren turns defined use cases into live systems staff rely on: agents, CRM automation, integrations and governance, tested against real users.
| Step | Stage | What it changes |
|---|---|---|
| 01 | Signal | Map the workflow needing improvement, list the data sources and CRM records required, and record where staff duplicate work today. |
| 02 | Synthesis | Design the system: model selection, agent tasks, governance rules, accountability owners and the training plan for the employees who will use it. |
| 03 | System | Build in stages, wire AI into the CRM and core platforms, and test against live data, edge cases and missing-source failure paths. |
| 04 | Scale | Release with monitoring and support, track accuracy, cost and adoption, and assign named ownership for the running system. |
First projects run two to ten weeks depending on scope. A single automation can complete in three to eight weeks, agents in six to ten weeks, and a company brain in eight to twelve weeks.
Discovery produces the definitive timeline before build begins, because duration is driven by how many systems need integration and how prepared the underlying data is.
No. Businesses with clear workflows and sound data foundations can enter directly at implementation, and discovery includes checks on data access and system compatibility.
Where the picture is uncertain, a readiness assessment from USD 8k or a strategy engagement from USD 12k to 25k removes ambiguity first, so build time is spent on the right workflows rather than on corrections.
Support starts from USD 2,500 per month for ten hours. It covers monitoring of live systems, refinement of prompts and automations, adjustments as usage patterns emerge, and iteration on the build as new needs surface.
Support keeps an implemented system improving after release instead of leaving it frozen on the day it launched.
Yes. CRM implementation with AI ranges from USD 20k to 80k over four to ten weeks and places intelligence directly inside the platform your teams already use. Work includes automated logging, AI reporting, guidance for sales and service conversations, and integrations connecting the CRM to the wider tool stack.
The Paloren team co-founded by Aaron Agius and Alex Agius scopes, builds and releases the work. The senior people who scope your project stay involved through release.
Scattered data is a common starting point rather than a blocker. Discovery maps where information lives and confirms what the build can reach before engineering begins. Where foundations need work, the readiness assessment from USD 8k identifies the gaps, and integrations are implemented to connect sources.
A company brain can later unify knowledge, but individual systems can run on narrower scoped data first.
AI governance is one of the Paloren services and is built into implementations rather than added afterwards. Policies, permissions and review controls are defined during design, applied at release, and monitored through accuracy sampling and escalation reviews.
Governance matters most once multiple systems are live, so it scales alongside the estate as agents, chatbots and automations are added.
Paloren serves businesses worldwide, and engagements are structured to run across time zones without depending on a single location. Delivery combines remote working sessions with structured stages, so discovery, build and release progress on a predictable rhythm wherever your team sits.
Scope, pricing and timelines are confirmed in the same way for every engagement, regardless of geography.
Strategy decides where AI should be applied and in what order; implementation builds and releases those systems. Strategy at Paloren runs USD 12k to 25k over three to four weeks and produces a prioritised roadmap.
Implementation executes it, from discovery and design through build, integration, testing and release. Many businesses complete both, while others arrive with clarity and start at implementation directly.
Yes, and it is often the sensible path. A single automation from USD 15k to 60k or one agent build from USD 40k to 90k proves the approach in production with contained risk.
Once the first system runs reliably, the pattern extends to further workflows using the same governance and knowledge foundations, which keeps each subsequent build faster than the last.
Start with a readiness assessment or go straight to discovery. Paloren will map your first workflow, confirm the scope and return a fixed plan with timeline and investment.
Which workflow should AI run first?
Book an implementation call
