Enterprise RAG consulting grounded in your company knowledge
Paloren provides enterprise RAG consulting that connects your company knowledge to AI systems with retrieval designed, tested and governed for accuracy.
Operations, knowledge, IT and data leaders who need AI answers grounded in trusted company information
Paloren provides enterprise RAG consulting for companies worldwide, designing retrieval systems that let AI answer from your own knowledge sources. Engagements cover data readiness, retrieval architecture, evaluation, governance and team training, with first projects ranging from USD 25k to 100k over two to ten weeks.
Retrieval-augmented generation, usually shortened to RAG, connects a language model to a defined set of company sources so its answers come from your material rather than general internet patterns.
Enterprise RAG consulting is the discipline of making that connection dependable: choosing which documents and records feed the system, preparing and indexing them, matching questions to the right passages, and checking that every answer stays faithful to what the sources say.
Companies reach for this work when generic chat tools give vague or off-target responses, when staff lose hours digging through drives, inboxes and wikis for policy details, or when knowledge sits scattered across departments with no agreed point of truth.
An enterprise system also carries responsibilities a demonstration never shows: permissions that decide who sees what, freshness rules that keep answers current, and evaluation that shows whether quality holds before anyone relies on it.
Paloren provides this consulting for companies worldwide as a standalone build or as the start of a broader company brain, with scope confirmed through a readiness assessment before any construction begins.
The work, made concrete
Grounds AI answers in your approved documents and records
Cuts time lost searching drives, inboxes and wikis
Fits regulated or complex knowledge generic tools handle poorly
Paloren treats retrieval as an engineering discipline with a business outcome attached, not a model demo. Engagements begin with an AI readiness assessment, from USD 8k over two to three weeks, which maps your sources, permissions, content quality and the questions staff actually need answered.
Strategy work follows where needed, from USD 12k to 25k over three to four weeks, setting the architecture and priorities. Build then covers ingestion, indexing, retrieval and generation, with each stage tested against question sets drawn from real tasks.
Every engagement closes with governance documentation and team AI training, so the system keeps serving accurate answers after handover.
Where companies want retrieval to anchor a wider knowledge layer, the work extends naturally into the company brain, Paloren's connected foundation for chat, agents, voice and reporting surfaces.
| Engagement | What it covers | Range and timeline |
|---|---|---|
| Readiness assessment | Source, permission and gap review before any build | From USD 8k over 2-3 weeks |
| AI strategy | Direction, priorities and retrieval architecture decisions | USD 12k-25k over 3-4 weeks |
| Company brain | Governed knowledge layer with retrieval at its core | USD 60k-150k over 8-12 weeks |
| AI agents | Task-focused systems acting on retrieved knowledge | USD 40k-90k over 6-10 weeks |
| Workflow automation | Retrieval-connected steps across everyday tools | USD 15k-60k over 3-8 weeks |
| Chatbot | Question answering grounded in company sources | USD 20k-50k over 4-8 weeks |
| Voice agent | Spoken answers drawn from the same knowledge layer | USD 25k-60k over 4-8 weeks |
| Custom apps | Purpose-built interfaces on top of retrieval | From USD 40k |
| Ongoing support | Monitoring, tuning and source updates | From USD 2,500 per month for 10 hours |
Ranges are indicative; final scope is set after a readiness assessment.
Source: Paloren published service ranges
| Factor | Why it matters | Effect on scope |
|---|---|---|
| Number of sources | Each source needs ingestion, cleanup and refresh rules | More sources extend preparation time |
| Permission complexity | Answers must respect who may see what | Deeper permission mapping adds design work |
| Content quality | Duplicated or outdated material weakens retrieval | Cleanup becomes a defined workstream |
| Evaluation depth | Question sets and scoring take time to build | Broader coverage lengthens testing |
| Integration surface | Connecting chat, CRM and voice adds interfaces | More surfaces expand the build |
| Governance requirements | Audit and review needs vary by industry | Stricter rules add configuration steps |
Scope drivers are assessed during readiness.
Source: Paloren published service ranges
The work, made concrete
Readiness assessment maps sources, permissions and gaps first
Retrieval is tested against questions your teams actually ask
Governance documentation and team training close every engagement
The company brain is Paloren's name for a governed knowledge layer that serves every AI surface a business runs: chat interfaces, AI agents, voice agents, reporting and custom apps.
Retrieval is the mechanism at its heart, which is why enterprise RAG consulting and the company brain pillar belong together. Built standalone, a RAG system answers questions from a defined set of sources.
Built as a company brain, that same retrieval layer is designed from the start to serve many surfaces at once, so an agent completing a task, a receptionist handling a call and a dashboard summarising performance all draw on one consistent body of approved knowledge.
This consistency matters: answers stop contradicting each other across tools, permissions are managed once rather than per surface, and new sources strengthen everything connected to the layer.
A company brain build ranges from USD 60k to 150k over eight to twelve weeks, and many organisations start with a narrower retrieval project first, then extend it once retrieval quality is demonstrated.
Either way, the architecture decisions made early, such as how content is prepared and how permissions travel, determine how easily the system grows later.
The work, made concrete
RAG is the retrieval engine inside the company brain
One knowledge layer serves chat, agents, voice and reporting
Standalone retrieval can extend into a full company brain
Most enterprises hold far more answerable knowledge than they realise. Document stores carry policies, playbooks, proposals, contracts and onboarding material. CRMs hold account histories, deal notes and service records.
Ticket queues, call transcripts and chat logs capture what customers actually ask and how teams respond. Intranet pages, spreadsheets and internal databases add operational context that generic tools never see.
Paloren's engagements typically begin by inventorying these sources, then judging each one for cleanliness, duplication, ownership and how often it changes, because retrieval quality reflects source quality.
Paloren combines implementation, workflow design and practical team training. Preparation is where much of the effort goes: consolidating duplicates, retiring outdated versions, tagging ownership and setting refresh schedules.
Permissions are mapped at the same stage so restricted material stays restricted once the system goes live. The result is a source map that states exactly what the system can answer from today, and what needs work first.
The work, made concrete
Documents, CRMs, tickets and call transcripts all feed retrieval
Source condition shapes answer quality more than model choice
Permissions are mapped before build so restricted content stays restricted
Testing separates a reliable retrieval system from an impressive demonstration. Paloren builds evaluation sets from the questions your teams genuinely ask, gathered during discovery from support tickets, onboarding sessions and interviews with the people who will use the system.
Each question carries an expected answer and the sources that should inform it. During evaluation, two things get scored separately: whether retrieval surfaces the right passages, and whether generated answers stay faithful to those passages without inventing detail.
The system is also tested on refusal, meaning it should say when the knowledge base lacks an answer rather than guessing. Where quality falls short, the fix is usually structural: adjusting how documents are split, blending keyword and semantic search, adding reranking, or improving source preparation.
Scores are re-run after each change until performance holds across the full question set. This process continues after launch, with live questions feeding the evaluation set so the system keeps pace with new content and new patterns of asking.
The evaluation report delivered at handover records what was tested, what was achieved and where the known limits sit, giving leadership an honest baseline rather than a showcase.
The work, made concrete
Evaluation sets come from real questions, not invented samples
Retrieval accuracy and answer faithfulness are scored separately
The system learns to refuse when knowledge is missing
Governance is where enterprise retrieval differs most from a hobby project. Because answers draw on company records, the system must respect the same rules the records obey.
Paloren configures permission mirroring so content restricted in its source stays restricted in every answer, meaning a person only ever receives knowledge they are entitled to see.
Audit trails record what was asked, which sources informed the answer and who accessed them, creating accountability for sensitive topics such as HR policy, contracts or financial figures.
Where questions touch high-stakes territory, review paths route the exchange to a human instead of an automated reply. Data handling decisions are documented too: where content is processed, how long it is retained and which models touch it.
Paloren offers AI governance as a dedicated service, and every retrieval engagement ships with a governance guide that explains these controls in plain language. This matters beyond compliance.
Teams trust a system more when they can see where an answer came from and know that restricted material stays protected. Leadership gains a defensible record of how AI is used across the organisation.
Governance designed at the start costs far less than controls retrofitted after an incident.
The work, made concrete
Source permissions carry through to every generated answer
Audit trails record questions, sources and access
Sensitive topics route to human review automatically
Paloren prices retrieval work against scope confirmed in discovery. A first project ranges from USD 25k to 100k over two to ten weeks, with the span explained by how different two companies' starting points can be.
Ten clean documents behind a single chat interface sit at one end; a dozen sources, layered permissions and deep evaluation sit at the other. When retrieval anchors a full company brain, the build ranges from USD 60k to 150k over eight to twelve weeks.
Related scopes carry their own ranges: chatbot builds from USD 20k to 50k over four to eight weeks, AI agents from USD 40k to 90k over six to ten weeks, voice agents from USD 25k to 60k over four to eight weeks, and custom apps from USD 40k.
The readiness assessment, from USD 8k over two to three weeks, is the most direct way to turn an unclear budget question into a firm number, because it establishes source condition, permission complexity and evaluation depth before any build is quoted.
Ongoing support starts at USD 2,500 per month for ten hours, covering monitoring, tuning and source updates once the system is live.
The work, made concrete
First projects range from USD 25k to 100k over two to ten weeks
Company brain builds range from USD 60k to 150k over eight to twelve weeks
Support starts at USD 2,500 per month for ten hours
Engagements are led by Paloren's co-founders. Alex Agius co-founded Paloren, and the two lead its work together. Paloren combines implementation, workflow design and practical team training.
On a typical retrieval engagement, founders set direction and quality standards, specialists handle ingestion, indexing and integration, and a training lead prepares staff for launch.
Decisions stay close to the people doing the work, so questions about permissions or sources get answered in days rather than weeks. Companies worldwide work with Paloren on this basis, engaging directly with the people who build and stand behind the system rather than through layers of account management.
The work, made concrete
Co-founders Aaron and Alex Agius lead engagements directly
Team experience spans IBM, Ford, LG, Unilever, Jaguar and Chelsea FC
Decisions happen in days, not through layers of account management
Launch is a milestone, not a finish line. Content changes, teams ask new questions and sources grow, so retrieval needs steady attention to stay accurate.
Paloren offers ongoing support from USD 2,500 per month for ten hours, covering performance monitoring, retrieval tuning, source updates and adjustments to evaluation sets as real usage reveals new patterns.
Support also covers the human side: refreshers for new staff, guidance on asking better questions and reviews of refused answers that might point to a content gap. As confidence in the retrieval layer grows, companies often extend it.
AI agents can act on retrieved knowledge, handling tasks rather than just answering questions, with builds ranging from USD 40k to 90k over six to ten weeks. Voice agents can answer spoken questions from the same knowledge layer, from USD 25k to 60k over four to eight weeks.
Workflow automation can pull retrieval into everyday processes, from USD 15k to 60k over three to eight weeks. Each extension reuses the governed foundation already built, which is why early architecture decisions matter.
The roadmap is agreed together, sequenced by value, and reviewed against the evaluation baseline set at launch.
The work, made concrete
Support starts at USD 2,500 per month for ten hours
New sources and questions extend the evaluation set over time
Retrieval can extend into agents, voice and automation
Map sources, permissions, content quality and gaps, then confirm which questions a retrieval system can answer reliably on day one.
Define ingestion, indexing, search and answer rules around the questions your teams ask, including how permissions travel with every passage.
Construct the system and score retrieval accuracy and answer faithfulness against real question sets, refining until quality holds across the board.
Apply access rules, audit trails and human review paths, document them in a governance guide, then release the system to trained teams.
Monitor performance, refresh sources and extend retrieval into agents, voice surfaces and automation as confidence grows.
| Engagement | What it covers | Range and timeline |
|---|---|---|
| Readiness assessment | Source, permission and gap review before any build | From USD 8k over 2-3 weeks |
| AI strategy | Direction, priorities and retrieval architecture decisions | USD 12k-25k over 3-4 weeks |
| Company brain | Governed knowledge layer with retrieval at its core | USD 60k-150k over 8-12 weeks |
| AI agents | Task-focused systems acting on retrieved knowledge | USD 40k-90k over 6-10 weeks |
| Workflow automation | Retrieval-connected steps across everyday tools | USD 15k-60k over 3-8 weeks |
| Chatbot | Question answering grounded in company sources | USD 20k-50k over 4-8 weeks |
| Voice agent | Spoken answers drawn from the same knowledge layer | USD 25k-60k over 4-8 weeks |
| Custom apps | Purpose-built interfaces on top of retrieval | From USD 40k |
| Ongoing support | Monitoring, tuning and source updates | From USD 2,500 per month for 10 hours |
Ranges are indicative; final scope is set after a readiness assessment.
Paloren published service ranges
| Factor | Why it matters | Effect on scope |
|---|---|---|
| Number of sources | Each source needs ingestion, cleanup and refresh rules | More sources extend preparation time |
| Permission complexity | Answers must respect who may see what | Deeper permission mapping adds design work |
| Content quality | Duplicated or outdated material weakens retrieval | Cleanup becomes a defined workstream |
| Evaluation depth | Question sets and scoring take time to build | Broader coverage lengthens testing |
| Integration surface | Connecting chat, CRM and voice adds interfaces | More surfaces expand the build |
| Governance requirements | Audit and review needs vary by industry | Stricter rules add configuration steps |
Scope drivers are assessed during readiness.
Readiness findings covering sources, permissions, quality and gaps
Retrieval architecture blueprint with ingestion and indexing design
Working RAG system connected to approved company sources
Evaluation report scoring retrieval accuracy and answer faithfulness
Governance guide covering access, audit and review paths
Team training sessions for everyday use and escalation
It covers the full path from company knowledge to reliable AI answers: assessing sources and permissions, designing ingestion and indexing, building retrieval and generation, testing quality against real questions, and setting governance.
Paloren delivers this as a service for companies worldwide, either as a standalone retrieval build or as the foundation of a broader company brain. Training is included so teams use the system well.
A standard chatbot answers from general patterns or fixed scripts, while enterprise RAG grounds every answer in your approved documents and records. That grounding brings permissions, freshness and traceability into play, which generic tools do not handle on their own.
Paloren builds both, and a grounded chatbot project ranges from USD 20k to 50k over four to eight weeks.
First projects range from USD 25k to 100k over two to ten weeks, with scope set by source count, permission complexity and evaluation depth. A full company brain build ranges from USD 60k to 150k over eight to twelve weeks when retrieval anchors a wider knowledge layer.
Every engagement is scoped after a readiness assessment, which starts at USD 8k over two to three weeks.
Paloren recommends starting there. The assessment, from USD 8k over two to three weeks, maps your sources, permissions, content quality and gaps, and confirms which questions a retrieval system can answer reliably from day one.
Skipping it risks building on duplicated, outdated or restricted content. The findings also feed the strategy and architecture decisions that follow, so the build starts on solid ground.
Yes. Retrieval design mirrors the permissions in your source systems, so a person asking a question only receives answers drawn from content they are entitled to see. Access rules, audit trails and human review paths are configured during the build, and the governance guide documents how they work. This matters most for HR, finance, legal and contract material.
Expect a system that answers company questions from approved sources, declines when information is missing, and shows which documents informed each answer. Paloren does not promise fixed outcomes before measuring your content; the readiness assessment and evaluation report establish what quality is achievable.
The practical goal is staff finding trusted answers in seconds instead of searching across drives, inboxes and wikis.
Yes. Paloren implements CRM with AI and builds workflow automation, so retrieval can draw on CRM records and feed answers into the tools teams already use. CRM implementation with AI ranges from USD 20k to 80k over four to ten weeks.
Simple grounded builds fit inside the two to ten week window of a first project, while a company brain with many sources and surfaces takes eight to twelve weeks. Timing depends on source condition, permission complexity and evaluation depth.
The readiness assessment produces a realistic schedule, so the timeline is clear before budget is committed to construction.
Data handling is agreed during scoping and documented in the governance guide. Paloren configures retrieval so content stays within the environment your organisation approves, covering where documents are processed, how long they are retained and which models can touch them.
Source permissions and audit trails are part of the same design, so security posture is recorded rather than assumed. Companies with strict requirements should raise them at the readiness stage so the architecture reflects them from the start.
Yes. Team AI training is part of every engagement, covering how to ask questions well, when to trust an answer and how to escalate topics that need human review. Sessions are practical and role based.
Training matters because retrieval quality improves when people understand what the system draws from and where its limits sit.
Start with a readiness assessment to map your sources, permissions and gaps. Paloren will confirm scope, timeline and the right retrieval design before any build begins.
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