Gen ai consulting that turns generative AI into working systems
Paloren provides gen ai consulting worldwide: strategy, AI agents, automation and training, led by co-founder Aaron Agius. Book a readiness assessment.
Founders, operations leaders and marketing teams planning generative AI adoption across their business.
Paloren provides gen ai consulting for companies worldwide, covering strategy, implementation, automation and training. Engagements start with a readiness assessment, then move into strategy, builds and team training.
Gen ai consulting is a service that helps companies put generative AI to work on real business problems instead of leaving it as a set of experiments. A consultant maps where large language models can create value, designs the systems that deliver that value, and then builds or oversees the build.
The work typically covers four layers. Strategy decides which use cases deserve investment and in what order. Implementation turns those choices into working software, such as a company brain that answers questions from your own documents, or agents that handle repeatable tasks.
Automation connects the new capability to the tools your teams already use, so output flows into CRMs, ticketing and reporting without manual copying. Training makes sure people know how to prompt, review and govern what the systems produce.
Gen ai consulting differs from buying a chatbot subscription because it starts with your data, your workflows and your risks. The output is not a demo, it is a system your staff rely on, with clear rules about what it can access, what it must never say and who checks its work.
Paloren provides this service worldwide, working at country level with businesses of many sizes.
The work, made concrete
Strategy, implementation, automation and training under one engagement
Systems built on your own data and workflows
Governance rules defined before launch, not after
Generative models are general purpose, which is exactly why they underperform when a company buys them off the shelf and hopes for value. The same model that drafts a contract summary in one business will hallucinate pricing in another if nobody grounds it in the right data.
A consulting approach exists to close that gap. It starts by finding the tasks where language, reasoning or content generation actually creates leverage, then checks whether your data is good enough to support them.
It also handles the questions that internal teams rarely have time for: which model to use, how to keep information private, how to measure quality, and how to stop staff from building shadow tools.
That experience now shapes how Paloren scopes, prices and delivers every gen ai engagement, so companies avoid the expensive trial and error phase.
| Service | Typical range | Typical duration |
|---|---|---|
| First gen ai project | USD 25k-100k | 2-10 weeks |
| AI readiness assessment | From USD 8k | 2-3 weeks |
| AI strategy | USD 12k-25k | 3-4 weeks |
| Company brain | USD 60k-150k | 8-12 weeks |
| AI agents | USD 40k-90k | 6-10 weeks |
| Workflow automation and integrations | USD 15k-60k | 3-8 weeks |
| CRM implementation with AI | USD 20k-80k | 4-10 weeks |
| AI chatbot | USD 20k-50k | 4-8 weeks |
| AI voice agent and receptionist | USD 25k-60k | 4-8 weeks |
| Custom apps | From USD 40k | Scoped per build |
| Ongoing support | From USD 2,500/mo for 10 hrs | Monthly |
Published ranges in USD. Final quotes depend on scope confirmed during discovery.
Source: Paloren published service ranges
| Factor | Why it matters | Typical effect |
|---|---|---|
| Number of integrations | Each connection adds design, build and testing work | More integrations extend duration |
| State of company data | Models answer well only when sources are organised | Messy data lengthens preparation |
| Count of use cases | Every use case needs its own prompts and rules | Additional use cases add build phases |
| Governance requirements | Sensitive information needs stricter access and review rules | Heavier governance adds documentation stages |
| Teams needing training | Adoption decides whether systems stay in use | More teams mean more sessions |
| Custom app complexity | Bespoke software sits outside standard products | Custom builds are scoped individually |
These factors move a project within or between the published ranges.
Source: Paloren published service ranges
The work, made concrete
General models need grounding in company data
Model choice, privacy and quality need expert decisions
Paloren treats gen ai consulting as an engineering discipline with a strategy layer on top, not a workshop that ends in slides. The approach begins with an AI readiness assessment that looks at data, systems, security posture and team capability.
Findings feed a strategy that ranks use cases by value and feasibility, so the first build is one the business can absorb.
From there, implementation covers the full service set: a company brain that centralises knowledge, AI agents for repeatable work, workflow automation and integrations, CRM implementation with AI, voice agents and receptionists, custom apps and governance frameworks.
Co-founders Aaron Agius and Alex Agius lead the direction. Paloren combines implementation, workflow design and practical team training.
That background matters because generative AI fails most often at the seams between systems and habits, and those seams are familiar territory for people who have spent their careers inside large operations.
The work, made concrete
Readiness assessment before any build
Use cases ranked by value and feasibility
Co-founders Aaron Agius and Alex Agius set direction
An engagement moves through defined stages so everyone knows what is being decided and built at each point. Discovery comes first: interviews with the people who will use the systems, a review of the tools and data already in place, and a shortlist of processes worth changing.
Strategy follows, turning that shortlist into a ranked roadmap with owners, guardrails and success measures. Build then runs in iterations, each one producing something usable rather than a long silent wait.
A company brain might go live on one department's documents before expanding. An agent might handle one workflow end to end before it takes on more. Testing covers accuracy, edge cases and failure modes, because generative systems need explicit rules about what they must refuse to do.
Governance work runs alongside, documenting access levels, review checkpoints and escalation paths. Training closes the loop, giving teams the prompts, procedures and confidence to use what was built.
Support continues after launch, with options from USD 2,500 per month for 10 hours of ongoing attention. Throughout, Paloren works with businesses worldwide at country level, and every stage produces written artefacts your team keeps.
The work, made concrete
Discovery, strategy, iterative build, testing, governance, training
Each iteration ships something usable
Written artefacts stay with your team
Generative AI earns its place where language, knowledge or content sits at the centre of the work. Paloren delivers a defined set of use cases, each mapped to a service. A company brain turns scattered documents into a single place where staff ask questions and get answers grounded in company material.
AI agents take on repeatable tasks such as drafting, summarising, routing and researching, working inside existing tools. Voice agents and AI receptionists answer calls, qualify enquiries and book time without adding headcount.
CRM implementation with AI enriches records, drafts follow ups and keeps data clean enough to trust. Workflow automation and integrations move output between systems so nothing stalls in a spreadsheet.
Custom apps cover the cases that sit outside standard products. Chatbots handle website and internal questions with the same grounding rules. Governance wraps around all of it, defining what each system may access and say.
The right starting point differs by business, which is why the readiness assessment exists before any build begins.
The work, made concrete
Company brain for grounded internal knowledge
Agents, voice agents and receptionists for repeatable work
CRM with AI, automation, custom apps and governance
Paloren prices gen ai consulting against scope, and the published ranges give an honest starting point. A first project typically sits between USD 25k and 100k and runs two to ten weeks.
The readiness assessment starts from USD 8k over two to three weeks. Strategy work falls between USD 12k and 25k across three to four weeks.
Larger builds carry their own bands: a company brain runs USD 60k to 150k over eight to twelve weeks, AI agents run USD 40k to 90k over six to ten weeks, and workflow automation runs USD 15k to 60k over three to eight weeks.
CRM implementation with AI sits between USD 20k and 80k, chatbots between USD 20k and 50k, and voice agents between USD 25k and 60k. Custom apps start from USD 40k. Ongoing support starts from USD 2,500 per month for 10 hours.
What moves a quote inside or beyond a band is usually the number of integrations, the state of the data, and how many teams need training. The table below lists each service with its range and duration so you can match budget to ambition before the first call.
The work, made concrete
First project range USD 25k to 100k over 2 to 10 weeks
Assessment from USD 8k, strategy USD 12k to 25k
Integrations, data state and training needs move the quote
Timelines follow scope. A readiness assessment needs two to three weeks because it examines systems, data and habits without building anything. Strategy takes three to four weeks, long enough to test assumptions with the people who own the processes.
Automation projects run three to eight weeks depending on how many tools need connecting. Agent builds need six to ten weeks, since each agent must be grounded, tested against edge cases and taught when to hand off to a human.
A company brain takes eight to twelve weeks, driven by how much material must be organised and how many departments need access. Voice agents and chatbots land between four and eight weeks.
CRM implementation with AI runs four to ten weeks. Custom apps are scoped individually. Most first projects land inside the two to ten week window, and Paloren deliberately sequences early wins so value appears before the largest builds finish.
Speed also depends on your side: timely access to systems, decisions and subject matter experts keeps weeks from stretching. The table of ranges shows both price and duration for every service so planning happens in one view.
The work, made concrete
Assessment 2 to 3 weeks, strategy 3 to 4 weeks
Agents 6 to 10 weeks, company brain 8 to 12 weeks
Early wins sequenced before the largest builds finish
A gen ai project should end with systems in daily use, not a report that sits in a drive.
Concrete results look like this: staff asking a company brain questions instead of searching folders, agents clearing repetitive drafting and routing work, calls answered and booked by a voice agent, CRM records that stay complete because AI maintains them, and automation that moves output between tools without anyone copying text.
Quality measures matter as much as output. Every build should ship with accuracy checks, refusal rules for questions it must not answer, and a named owner for each system.
Governance documentation should be complete enough that a new manager could understand what each system does, what it accesses and who reviews it within an afternoon. Team capability is the third result.
Training should leave people able to prompt well, judge outputs critically and escalate problems quickly. If a system is not used three months after launch, the project has missed, regardless of how impressive the technology looked on launch day.
The work, made concrete
Systems in daily use, not unused reports
Accuracy checks, refusal rules and named owners
Teams trained to prompt, judge and escalate
Selection questions separate partners who build from partners who advise. Ask to see systems a partner has shipped and how they behave after months of use, since demos are easy and operations are hard.
Ask how they ground models in company data and what stops a system from inventing answers. Ask who writes the governance rules and whether training is included or sold separately.
Ask for pricing structure early; published ranges signal a partner comfortable with scope conversations. Fit matters too. A partner who pushes a fixed product before understanding your processes will sell you their hammer.
A partner who starts with a readiness assessment is showing you their method. Finally, check continuity: support from USD 2,500 per month for 10 hours means the relationship continues after launch, which is when generative systems actually need attention as data, models and questions change.
The work, made concrete
Ask for shipped systems, not slide decks
Grounding, governance and training reveal depth
Published ranges and post launch support show transparency
A two to three week review of data, systems, security and team capability that establishes where generative AI can land safely in your business.
A three to four week engagement that ranks use cases by value and feasibility and sets guardrails, owners and success measures for everything that follows.
An iterative build of the highest priority use case, such as a company brain or an agent, shipped in usable increments rather than one long wait.
Accuracy checks, edge case testing, refusal rules and governance documentation completed before the system reaches a wider audience.
Hands on sessions covering prompts, review habits and escalation paths so adoption starts on day one instead of fading after a demo.
Ongoing support from USD 2,500 per month for 10 hours, plus staged rollout of the next use cases on the roadmap as your teams mature.
| Service | Typical range | Typical duration |
|---|---|---|
| First gen ai project | USD 25k-100k | 2-10 weeks |
| AI readiness assessment | From USD 8k | 2-3 weeks |
| AI strategy | USD 12k-25k | 3-4 weeks |
| Company brain | USD 60k-150k | 8-12 weeks |
| AI agents | USD 40k-90k | 6-10 weeks |
| Workflow automation and integrations | USD 15k-60k | 3-8 weeks |
| CRM implementation with AI | USD 20k-80k | 4-10 weeks |
| AI chatbot | USD 20k-50k | 4-8 weeks |
| AI voice agent and receptionist | USD 25k-60k | 4-8 weeks |
| Custom apps | From USD 40k | Scoped per build |
| Ongoing support | From USD 2,500/mo for 10 hrs | Monthly |
Published ranges in USD. Final quotes depend on scope confirmed during discovery.
Paloren published service ranges
| Factor | Why it matters | Typical effect |
|---|---|---|
| Number of integrations | Each connection adds design, build and testing work | More integrations extend duration |
| State of company data | Models answer well only when sources are organised | Messy data lengthens preparation |
| Count of use cases | Every use case needs its own prompts and rules | Additional use cases add build phases |
| Governance requirements | Sensitive information needs stricter access and review rules | Heavier governance adds documentation stages |
| Teams needing training | Adoption decides whether systems stay in use | More teams mean more sessions |
| Custom app complexity | Bespoke software sits outside standard products | Custom builds are scoped individually |
These factors move a project within or between the published ranges.
AI readiness assessment report with prioritised findings
Gen ai strategy and ranked use case roadmap
Working generative AI systems such as a company brain, agents or voice agents
Governance framework covering access, review and refusal rules
Team AI training program with prompts and procedures
Support plan from USD 2,500 per month for 10 hours
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.
Gen ai consulting only works when strategy, implementation, automation and training move together. S4 sequences each layer so models are grounded in your data before staff depend on their output.
| Step | Stage | What it changes |
|---|---|---|
| 01 | Signal | Map candidate use cases, the documents and CRMs behind them, repeatable tasks worth automating, and shadow tools staff already built. |
| 02 | Synthesis | Rank use cases by leverage, define what each system may access and must never say, and set who reviews its work. |
| 03 | System | Build the company brain or agents, ground them in your documents, connect output to ticketing and reporting, and test failure paths. |
| 04 | Scale | Train staff to prompt and govern output, track hallucination rate and adoption, then extend one workflow at a time. |
General AI consulting can cover analytics, prediction and classic machine learning. Gen ai consulting focuses on systems built with large language models: company brains, agents, chatbots, voice agents and content tools.
Paloren covers both worlds, because generative systems usually need to connect to the same CRMs, databases and workflows that earlier AI work touched, so one team handling both avoids integration gaps.
No. Most companies discover during the readiness assessment that their data needs work, and that finding is part of the value. The assessment identifies which sources are usable, which need structuring and which should wait.
Projects then include preparation stages, and CRM implementation with AI often improves record quality as a direct output. Waiting for perfect data usually means waiting forever.
Yes. Workflow automation and integrations exist precisely to connect generative systems to the software already running your operations, including CRMs, ticketing, reporting and internal databases.
Discovery maps every system in scope before build begins, so connections are designed rather than improvised. Custom apps handle cases where no standard product fits, and governance rules define what each connection may access.
Builds are designed so company material stays inside the boundaries you set. Governance work defines access levels, retention and refusal rules before launch, and grounding uses your documents through controlled retrieval rather than exposing them to public training.
The readiness assessment reviews your security posture early, so privacy requirements shape architecture from the first week instead of being retrofitted later.
The service suits any business where knowledge work, content, calls or CRM hygiene consume real hours. Small teams often start with a readiness assessment and one automation project.
Larger organisations typically begin with strategy, then run a company brain or agents across departments. Paloren serves businesses worldwide at country level, and ranges scale with scope rather than company size alone.
Yes. Support starts from USD 2,500 per month for 10 hours of ongoing attention. Generative systems need maintenance because data changes, models improve and staff ask new questions.
Support covers monitoring, prompt refinement, small integrations and answers for your team. Larger expansions, such as adding a voice agent after a company brain, are quoted separately against the published ranges.
Yes, and many engagements do. A first project sits between USD 25k and 100k over two to ten weeks, which leaves room for a focused pilot such as one agent or one automated workflow.
The pilot proves the approach on a real process, gives your team something to react to, and produces evidence that guides the wider roadmap.
Co-founders Aaron Agius and Alex Agius lead direction. Paloren combines implementation, workflow design and practical team training.
Against scope. Published ranges give the frame: readiness from USD 8k, strategy USD 12k to 25k, automation USD 15k to 60k, agents USD 40k to 90k and a company brain USD 60k to 150k.
A first project usually lands between USD 25k and 100k over two to ten weeks. Integrations, data state and training needs determine the final figure.
Start with an AI readiness assessment to see where generative AI fits your business, then move into strategy and a first build with a clear range and timeline.
Where could generative AI save your team hours?
Book your readiness assessment
