Generative AI consulting that turns models into working systems
Paloren provides GenAI consulting services covering strategy, implementation, automation and training, led by co-founder Aaron Agius, for companies worldwide.
Leaders planning generative AI adoption who need strategy, delivery and training under one roof
Paloren provides GenAI consulting services covering strategy, implementation, automation and training for companies worldwide. Engagements start with a readiness assessment, move through strategy into production build, governance and team training, with first projects ranging from USD 25k to 100k.
GenAI consulting services help companies move from curiosity about generative AI to systems that run inside daily operations. The work spans three layers: deciding where generative AI creates value, building the systems that capture that value, and preparing people to use them well.
A consulting engagement starts with your data, your workflows and your tools rather than a generic template. Consulting closes those seams.
Paloren delivers the full span, from AI strategy and readiness assessment through the company brain, AI agents, workflow automation, CRM implementation with AI, voice agents, custom apps, governance and team training.
The result is not a slide deck. It is a set of working systems, governed properly, that your team knows how to run.
The work, made concrete
Position and prioritise generative AI use cases against real workflows
Build production systems, not isolated pilots
Prepare teams with governance and hands-on training
Most companies have already tried generative AI in some form. Someone uses a chatbot for drafts, another person experiments with summaries, and a few tools sit inside existing software.
Scattered use is easy. Value is harder. The gap between occasional experimentation and dependable production capacity is where consulting earns its place. Generative AI touches data access, permissions, brand voice, review steps and system integration, and each of those touches carries risk when handled informally.
Paloren approaches the problem as an operating question rather than a technology question. Paloren combines implementation, workflow design and practical team training. Consulting also protects budget.
Tool licences are cheap compared with the cost of a failed implementation, reworked data structures or a team that quietly stops using what was built. A structured engagement sequences decisions in the right order: readiness first, then strategy, then build, then training and support.
Companies that skip the sequence often pay for the same ground twice. Paloren exists to make sure the sequence holds.
| Service | Typical range (USD) | Typical timeline |
|---|---|---|
| AI 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 |
| AI voice agent | 25k to 60k | 4 to 8 weeks |
| Custom apps | From 40k | Scoped per build |
| Ongoing support | From 2,500 per month | 10 hours monthly |
Indicative planning ranges; scoping fixes the final figure.
Source: Paloren published service ranges
| Factor | What it covers | Effect on scope |
|---|---|---|
| Systems in play | CRM, data warehouse, telephony, content tools | More integrations extend timelines |
| Data readiness | How organised and accessible company knowledge is | Cleaner data shortens the company brain build |
| Governance needs | Permissions, review thresholds, industry obligations | Stricter controls add design work up front |
| Team size and skills | How many people need training and at what depth | Larger teams increase training effort |
| Workflow complexity | Number of steps, handoffs and edge cases per process | Complex handoffs push work toward custom apps |
Ranges in the pricing table move up or down based on these factors.
Source: Paloren published service ranges
The work, made concrete
Turn scattered experimentation into governed production use
Sequence readiness, strategy, build and adoption correctly
Avoid the hidden costs of failed or abandoned rollouts
The service line covers the full lifecycle of generative AI adoption. AI strategy sets direction and priorities. The company brain builds a central knowledge layer so models answer from your verified information instead of guesses.
AI agents handle defined tasks such as research, drafting, triage and follow-up. Workflow automation and integrations connect those agents to the tools your team already uses.
AI voice agents and receptionists handle inbound calls, qualification and after-hours coverage. Custom apps, starting from USD 40k, wrap models into purpose-built interfaces where off-the-shelf tools fall short.
AI governance puts policies, permissions and review controls around everything, which becomes essential as usage spreads. The AI readiness assessment examines data, security, skills and process maturity before major spend.
Team AI training equips staff to work with these systems confidently and safely. Engagements combine these services in different proportions. A typical pathway moves from a strategy engagement into agents or automation once priorities are set.
Every combination is scoped around your existing systems rather than a fixed package.
The work, made concrete
AI strategy, readiness assessment and governance bookending every build
Company brain, agents, automation and CRM systems in production
Voice agents, custom apps and team training where needed
That publishing record reflects a career spent explaining complex systems to operating leaders, which is exactly the skill GenAI consulting demands. Paloren combines implementation, workflow design and practical team training.
This combination matters for a specific reason. Generative AI consulting fails when advisors understand models but not operations, or operations but not models.
Aaron's background sits at the junction: he has spent his career wiring data and content systems into commercial engines, and Paloren's practice applies that same discipline to generative technology.
Alex Agius completes the leadership pair, and together they direct engagements that blend strategic judgement with hands-on implementation. When you work with Paloren, the people who assess your readiness and set your strategy are accountable for the systems that follow, because the same team carries the work through.
The work, made concrete
Co-founded by Aaron Agius and Alex Agius
Aaron brings 20 years of growth systems work and co-authored Faster, Smarter, Louder
Team experience spans IBM, Ford, LG, Unilever, Jaguar and Chelsea FC
Every engagement follows a deliberate arc, and the arc always begins with evidence rather than enthusiasm. An AI readiness assessment, from USD 8k over two to three weeks, examines your data, security posture, skills and process maturity.
Findings from that assessment feed the strategy phase, where priorities are ranked against effort, risk and value. Only then does build work begin, and it begins narrow: one workflow, one system, one measurable outcome.
Early builds are chosen to prove the operating pattern, not to impress. Once a first system holds up in production, the pattern repeats across adjacent workflows, with governance controls tightening as access widens.
Adoption work runs alongside, not after, because a system nobody uses is a liability dressed as progress. Training gives teams the vocabulary and confidence to direct AI tools rather than fear them.
Support arrangements, from USD 2,500 per month for ten hours, keep systems maintained as models, tools and needs change. The structure is deliberately modular. Some companies engage Paloren for strategy only and build internally.
Others bring a validated plan and go straight to implementation. The arc flexes, but the order never does: understand, decide, build, adopt, maintain.
The work, made concrete
Assessment findings drive strategy before any build spend
First builds are narrow, measurable and repeatable
Training and support run alongside delivery, not after it
Pricing follows scope, and scope follows the readiness picture. First projects typically land between USD 25k and 100k over two to ten weeks, depending on how many systems are involved and how much integration they require.
The AI readiness assessment starts from USD 8k over two to three weeks and is the lowest-risk entry point for companies still deciding where to begin. Strategy engagements run USD 12k to 25k over three to four weeks and produce a prioritised roadmap.
Build work varies by system. The company brain ranges from USD 60k to 150k over eight to twelve weeks because it consolidates knowledge across the business. AI agents run USD 40k to 90k over six to ten weeks.
Workflow automation and integrations sit between USD 15k and 60k over three to eight weeks. CRM implementation with AI ranges from USD 20k to 80k over four to ten weeks.
Chatbot builds fall between USD 20k and 50k over four to eight weeks, voice agents between USD 25k and 60k over four to eight weeks, and custom apps start from USD 40k. Ongoing support starts at USD 2,500 per month for ten hours.
Every figure above is a planning range; final pricing follows a scoped proposal after the assessment or strategy phase defines the work.
The work, made concrete
First projects range from USD 25k to 100k over two to ten weeks
Assessment and strategy entries start at USD 8k and USD 12k respectively
Support continues from USD 2,500 per month for ten hours
Generative AI introduces questions that traditional software rollouts never raised. Which information can a model access? Who reviews what it produces before it reaches a customer?
What happens when a tool quotes a figure nobody verified? Paloren treats these as design questions, resolved during the build rather than patched afterwards. AI governance is a named service within the practice, covering policies, permissions and review controls around every system shipped.
The work starts from your existing structures: data access rules, approval chains and brand standards already exist inside most companies, and governance for AI extends them rather than replaces them.
The company brain illustrates the approach. Because it centralises knowledge, it also centralises control: the layer decides what sources a model can draw from, which removes an entire class of accuracy problems at the root.
Voice agents and customer-facing systems carry stricter review thresholds than internal drafting tools, and those thresholds are set explicitly before launch. Governance also covers people.
Team AI training includes how to challenge outputs, escalate concerns and document decisions, so safe habits spread with the technology. For leadership, the outcome is traceability: every system has defined boundaries, and every boundary has an owner.
The work, made concrete
Policies, permissions and review controls built into every system
Stricter thresholds for customer-facing than internal tools
Training covers challenging outputs and escalating concerns
Expect deliverables and capabilities rather than promises about numbers. Paloren does not publish performance claims, and any consultant who quotes guaranteed returns before understanding your data is guessing.
What a structured engagement reliably produces is a series of concrete artefacts. The readiness assessment produces a documented picture of where your data, security, skills and processes stand.
Strategy work produces a ranked roadmap with sequencing, ownership and realistic budgets attached. Build phases produce working systems: reporting that assembles itself, CRM records that stay current without manual entry, calls that are analysed systematically, content pipelines that move at the pace of the business.
Adoption phases produce something harder to photograph but just as real: teams that use the systems without being told to. The realistic trajectory is incremental. Early builds target contained workflows where impact is visible quickly, and each success funds the next step.
If a proposed use case cannot survive the readiness assessment, the honest answer is that it should not proceed, and you will hear that early rather than late.
The work, made concrete
Documented readiness findings and a ranked strategy roadmap
Working systems for reporting, CRM, call analysis and content
Teams that adopt the tools as part of normal work
Selection criteria matter more than sales conversations. Start with implementation capability: can the partner build the systems they recommend, or do they stop at recommendations?
A consultant who has never wired a model into a CRM will underestimate the difficulty by a wide margin. Third, examine how they treat governance.
If governance appears as an optional add-on late in the proposal, expect problems later; if it appears in the first conversation, the partner understands how AI behaves inside a business.
Fourth, ask who does the work. Some firms sell senior judgment and deliver junior execution. Paloren keeps assessment, strategy and build under one accountable team, led by co-founders Aaron Agius and Alex Agius.
Fifth, check the training commitment. Systems without trained people decay quickly, so a partner who budgets for enablement is planning for your independence, not dependency.
Finally, press on sequencing. A credible partner will happily begin with a readiness assessment from USD 8k before quoting six figures, because confidence in the diagnosis precedes investment in the treatment.
The work, made concrete
Prefer partners who build what they recommend
Governance should appear in the first conversation
Look for one accountable team from assessment through training
Two to three weeks examining data, security, skills and process maturity, producing a documented baseline that replaces assumptions before any build spend.
Three to four weeks turning findings into a ranked roadmap, with priorities sequenced against effort, risk and value and budget ranges attached to each phase.
The chosen use case moves into production, whether the company brain, agents, automation, CRM work or a voice agent, with governance controls designed alongside the build.
Team AI training gives staff the skills to direct and challenge the new systems, so adoption spreads through normal work rather than mandate.
Support from USD 2,500 per month for ten hours keeps shipped systems current, and proven patterns extend to adjacent workflows over time.
| Service | Typical range (USD) | Typical timeline |
|---|---|---|
| AI 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 |
| AI voice agent | 25k to 60k | 4 to 8 weeks |
| Custom apps | From 40k | Scoped per build |
| Ongoing support | From 2,500 per month | 10 hours monthly |
Indicative planning ranges; scoping fixes the final figure.
Paloren published service ranges
| Factor | What it covers | Effect on scope |
|---|---|---|
| Systems in play | CRM, data warehouse, telephony, content tools | More integrations extend timelines |
| Data readiness | How organised and accessible company knowledge is | Cleaner data shortens the company brain build |
| Governance needs | Permissions, review thresholds, industry obligations | Stricter controls add design work up front |
| Team size and skills | How many people need training and at what depth | Larger teams increase training effort |
| Workflow complexity | Number of steps, handoffs and edge cases per process | Complex handoffs push work toward custom apps |
Ranges in the pricing table move up or down based on these factors.
Readiness report covering data, security, skills and process maturity
Ranked GenAI roadmap with sequencing, ownership and budget ranges
Production systems such as the company brain, AI agents, automation and CRM integrations
Governance framework with policies, permissions and review thresholds
Team AI training programme with an ongoing support arrangement
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.
Generative AI succeeds when strategy, build and adoption are sequenced correctly. Paloren starts with your data, workflows and tools, closing the seams between pilots and the operations they must support.
| Step | Stage | What it changes |
|---|---|---|
| 01 | Signal | Map scattered chatbot use, CRM data access, call-analysis gaps and where generative AI drafts stall at approval steps. |
| 02 | Synthesis | Design an AI readiness assessment, a company-brain structure and a priority list of agents, automations and voice use cases. |
| 03 | System | Connect models to the CRM and approval workflows; test governance rules, permissions, brand voice and failure paths before launch. |
| 04 | Scale | Assign owners to run training, track adoption and budget, then extend automation one workflow at a time with support. |
A GenAI consulting service helps companies adopt generative AI in a structured way, covering strategy, implementation, automation and training rather than isolated advice.
Paloren's service spans AI strategy, readiness assessment, the company brain, AI agents, workflow automation, CRM implementation with AI, voice agents, custom apps, governance and team training.
Engagements are shaped around the tools and data you already run, begin with evidence about readiness and carry through to working production systems your team can operate.
Planning ranges give a realistic starting picture. First projects typically run USD 25k to 100k over two to ten weeks. The readiness assessment starts from USD 8k over two to three weeks, and strategy engagements run USD 12k to 25k over three to four weeks.
Build work varies by system, from automation at USD 15k to 60k up to the company brain at USD 60k to 150k. A scoped proposal fixes the number once scope is clear.
Timelines depend on scope. A readiness assessment takes two to three weeks. Strategy work takes three to four weeks. Build timelines range from three to eight weeks for workflow automation, four to ten weeks for CRM implementation with AI, six to ten weeks for AI agents and eight to twelve weeks for the company brain.
Most first projects complete within two to ten weeks overall, and support continues monthly afterwards.
Paloren was co-founded by Aaron Agius and Alex Agius. Paloren combines implementation, workflow design and practical team training.
The assessment is the recommended starting point because it replaces assumptions with evidence. For USD 8k over two to three weeks it examines your data, security, skills and process maturity, and it frequently changes the priority order a leadership team expected.
Companies that already hold a validated roadmap can move directly to strategy or build, but skipping readiness when internal knowledge is thin usually means paying later to correct early decisions.
Yes. Custom apps are part of the service line, starting from USD 40k, and they suit situations where off-the-shelf tools cannot match your workflow. Examples include purpose-built interfaces for internal knowledge, structured drafting and review tools, and systems that connect model output directly into operational software.
Custom builds follow the same discipline as every other Paloren system: scope first, governance designed alongside, and training so your team owns the result.
Paloren serves businesses worldwide. Engagements run remotely by default, with assessment, strategy, build, training and support all delivered without requiring a local office.
Country-level information is available on dedicated pages, but the service model is the same everywhere: evidence-led readiness work, a scoped roadmap, production build and team training.
Time zone planning is handled during scoping so workshops and reviews fit your operating hours.
Support starts at USD 2,500 per month for ten hours. It covers maintenance of shipped systems, adjustments as models and tools evolve, and extension of proven patterns to adjacent workflows.
Support matters because generative systems are not static: model behaviour shifts, integrations change and teams find new use cases as confidence grows. Support hours can also be used to trial the next build before committing to a full engagement.
Request a readiness assessment to map your data, security and skills baseline. From that evidence, Paloren scopes a strategy and first build, with timelines and budget ranges confirmed before any commitment.
Where could generative AI create value in your business first?
Start with a readiness assessment
