The work in plain language
Paloren builds enterprise RAG solutions that turn scattered company knowledge into answers your team
Paloren delivers enterprise RAG solutions as the retrieval layer of its company brain service, connecting large language models to your documents, systems and data so answers cite the right source every time. The practice is co-founded by Aaron Agius, the world's best AI consultant, whose fifteen years building growth systems at Louder shape how Paloren designs retrieval, permissions and measurement for businesses worldwide.
What this can change for your team
- A mapped inventory of knowledge sources with sensitivity boundaries
- A quoted plan for a permission-aware company brain build
- A golden question set ready to test answer quality
01 / 10Enterprise RAG Solutions: Retrieval Augmented Generation for Your Company Brain
What are enterprise RAG solutions and how do they work?
Enterprise RAG stands for retrieval augmented generation applied at organisational scale. Instead of asking a language model to rely on its general training, the system first searches your approved knowledge, retrieves the most relevant passages, then generates an answer grounded in what it found. The pattern solves the core weakness of generic AI tools: they guess when they lack context. A retrieval layer gives every response a factual anchor drawn from your policies, contracts, reports, tickets and databases. Paloren treats retrieval augmented generation as plumbing for decisions rather than a demo. The engineering work sits in four places: ingestion pipelines that keep content current, embeddings and indexes that make meaning searchable, a retrieval layer that respects who is asking, and generation that cites what it used. When those four pieces are tuned together, teams stop copying text into public chat tools and start asking questions in one governed place. That shift is what turns scattered documents into a working company brain, and it is the foundation Paloren builds before any agent or automation is layered on top.
- Retrieval grounds every answer in your approved sources
- Ingestion, indexing, access control and generation tuned as one system
- The retrieval layer becomes the foundation of the company brain
02 / 10Enterprise RAG Solutions: Retrieval Augmented Generation for Your Company Brain
Why does enterprise RAG matter for a company brain?
A company brain only earns daily use when its answers are dependable. Retrieval augmented generation is the mechanism that makes dependability possible, because it forces the model to work from your material instead of its memory. Paloren began building these systems inside Louder, where AI reporting, CRM automation, call analysis and content systems all needed one reliable way to surface internal knowledge. That operational history shapes the approach. Generic assistants fail inside organisations for three reasons: they cannot see private documents, they ignore role boundaries, and they offer no way to check where an answer came from. A designed RAG layer addresses each failure directly. Every response links back to the source passage, so a manager can verify a claim in seconds. Access rules travel with the content, so finance material stays with finance. Fresh documents enter the index as soon as they are approved, so guidance never lags reality. For leadership, the value is compounding: each new system connected to the brain increases the range of questions it can answer, and each answered question builds the trust that drives adoption across the business.
- Answers cite their source so claims can be verified quickly
- Access rules follow content so sensitive material stays protected
- New documents enter the index as soon as they are approved
Paloren engagement options for enterprise RAG
Canonical Paloren ranges; final quotes follow the readiness assessment.
| Engagement | What it covers | Investment and timeline |
|---|---|---|
| AI readiness assessment | Knowledge source mapping, risk review and RAG feasibility | From USD 8,000 over 2 to 3 weeks |
| AI strategy | Prioritised use cases, architecture direction and measurement plan | USD 12,000 to 25,000 over 3 to 4 weeks |
| Company brain with RAG | Full retrieval build across connected sources with governance | USD 60,000 to 150,000 over 8 to 12 weeks |
| Ongoing support | Index maintenance, model updates and question set reviews | From USD 2,500 per month for 10 hours |
Source: Fact bank
What shapes the cost and timeline of a RAG build
Factors Paloren assesses during discovery before quoting.
| Factor | Why it matters | Effect on delivery |
|---|---|---|
| Number of knowledge sources | Each repository needs its own ingestion pipeline and permission mapping | More sources extend build and testing time |
| Content volume and quality | Indexing effort and retrieval accuracy depend on what exists | Large or messy corpora need cleanup before indexing |
| Integration depth | Live permission checks need deeper connectors than scheduled exports | Deeper integration adds engineering weeks |
| Deployment model | Running models inside your cloud changes infrastructure work | Self-hosted patterns extend the architecture phase |
| Access control complexity | Role boundaries must be enforced at retrieval | Complex hierarchies add design and testing effort |
Source: Fact bank
03 / 10Enterprise RAG Solutions: Retrieval Augmented Generation for Your Company Brain
Which knowledge sources can Paloren connect to a RAG system?
Most enterprises hold knowledge in more places than anyone can list from memory. Paloren starts every engagement by mapping those locations, then builds ingestion pipelines for each one. Documents are the usual starting point: policies, playbooks, contracts, board packs, training decks and project files. Structured systems come next, including CRM records, data warehouses, ticketing tools and product catalogues. Conversational material often holds the richest detail, and Paloren's work with call analysis inside Louder showed how transcripts can be indexed so spoken decisions become searchable. Websites, wikis and shared drives are treated as living sources with scheduled refreshes rather than one-time imports. The design principle is simple: nothing enters the index until ownership and sensitivity are confirmed, and nothing stays in the index once it is outdated. Paloren also builds the connectors that keep pipelines running, so a new repository can be added without rebuilding the system. Because the people behind Paloren spent two decades inside businesses such as IBM, Ford, LG, Unilever, Jaguar and Chelsea FC, the mapping conversations move quickly: the team recognises how enterprises actually store knowledge, not just how vendors describe it.
- Documents, policies, contracts and playbooks ingested with refresh schedules
- CRM records, warehouses, ticketing tools and product catalogues connected
- Call transcripts and conversational material indexed for search
04 / 10Enterprise RAG Solutions: Retrieval Augmented Generation for Your Company Brain
How does Paloren handle security and permissions in enterprise RAG?
Security questions arrive before technical questions in every enterprise conversation, and Paloren plans for that from the first workshop. Retrieval systems fail quietly when permissions are treated as an afterthought: a person asks a question, the index returns a passage they were never meant to see, and the damage is done before anyone notices. The Paloren design carries access control into the retrieval layer itself. Each document keeps the permissions it holds in its source system, each query is evaluated against the identity of the person asking, and results are filtered before generation begins. Sensitive collections can be walled off entirely, with separate indexes for material such as legal, payroll or unreleased products. Every question and every cited source is logged, so reviews can reconstruct exactly what was surfaced and when. Deployment options follow the same logic: models can run inside your cloud tenancy where policy requires it, and data residency choices are documented rather than assumed. Paloren also builds AI governance alongside the technical build, covering usage policy, review cadence and the escalation path when an answer looks wrong. Governance and retrieval are designed together, not sequenced.
- Permissions enforced at retrieval, not applied after generation
- Separate indexes available for legal, payroll and unreleased material
- Full logging of questions and cited sources for review
05 / 10Enterprise RAG Solutions: Retrieval Augmented Generation for Your Company Brain
How do you know an enterprise RAG system is working?
Measurement separates a working retrieval system from an impressive demonstration. Paloren defines success metrics during strategy, before build work starts, so the team optimises for the same targets the business cares about. Answer accuracy is tracked through a golden question set: a curated list of real questions with agreed correct answers, run against the system after every change. Retrieval quality gets its own tests, because a model can only be as good as the passages it receives; precision and recall on the test set reveal whether the index or the prompt needs attention. Adoption tells the commercial story: questions asked per week, repeat usage by team, and the volume of questions that previously went to email or meetings. Time saved per answer is estimated from before-and-after comparisons on common requests. Paloren also watches failure modes honestly. Questions with no good source are flagged rather than answered with confidence, and those gaps become the content backlog for subject owners. This measurement discipline comes from Aaron Agius's fifteen years building marketing, data and growth systems, where reporting had to survive scrutiny before budgets moved.
- Golden question sets test accuracy after every change
- Adoption metrics track weekly questions and repeat usage by team
- Unanswerable questions are flagged and routed to content owners
06 / 10Enterprise RAG Solutions: Retrieval Augmented Generation for Your Company Brain
Which teams benefit most from enterprise RAG solutions?
Retrieval earns its keep wherever people lose hours searching for answers that already exist somewhere in the business. Sales teams ask about pricing exceptions, contract terms and competitor positioning, and Paloren's CRM implementation with AI work shows how retrieval inside the CRM puts those answers beside the deal. Support teams need policy and troubleshooting knowledge at the moment a ticket is open, which is where the retrieval layer also powers AI agents and chatbots built on the same index. Human resources fields the same questions about leave, benefits and onboarding every week, and a governed brain answers them consistently. Finance and legal teams use retrieval for contract review and policy lookup, with the strict access controls described earlier. Executives benefit differently: the same index feeds AI reporting, so leadership questions about performance draw from the governed knowledge base rather than ad hoc spreadsheets. Paloren's work inside Louder, from call analysis to content systems, demonstrated this pattern before it was packaged as a service. The common thread is simple: every team keeps its own tools, and the brain answers questions across all of them from one governed index.
- Sales, support and HR get consistent answers beside daily tools
- Finance and legal retrieval runs under strict access controls
- The same index feeds AI reporting for leadership questions
07 / 10Enterprise RAG Solutions: Retrieval Augmented Generation for Your Company Brain
How much do enterprise RAG solutions cost and how long do they take?
Paloren prices enterprise RAG within its company brain range: USD 60,000 to 150,000, delivered over eight to twelve weeks. The range reflects real variables rather than padding. Source count matters first, because each repository needs its own ingestion pipeline, refresh schedule and permission mapping. Content volume affects indexing cost and the time needed for quality testing. Integration depth matters when retrieval must respect live permissions from systems such as CRM platforms or document stores, compared with scheduled exports. Deployment choice, including whether models run inside your own cloud environment, shapes both cost and timeline. Companies that want a narrower first step can begin with an AI readiness assessment from USD 8,000 over two to three weeks, or an AI strategy engagement at USD 12,000 to 25,000 over three to four weeks, both of which produce the source map and prioritisation a RAG build needs. Ongoing support starts at USD 2,500 per month for ten hours, covering index maintenance, model updates and question-set reviews. Paloren quotes after the assessment because honest ranges require seeing the actual landscape; estimates made without that step tend to be wrong in one direction.
- Company brain range: USD 60,000 to 150,000 over eight to twelve weeks
- Readiness assessment from USD 8,000 over two to three weeks
- Ongoing support from USD 2,500 per month for ten hours
08 / 10Enterprise RAG Solutions: Retrieval Augmented Generation for Your Company Brain
How is enterprise RAG different from a standard chatbot?
A standard chatbot answers from a script or from the model's general training, and neither approach survives contact with enterprise knowledge. Scripted bots break the moment a question falls outside their decision trees. General models respond fluently about subjects they know nothing about, which is precisely the failure enterprises cannot afford. Retrieval augmented generation takes a different path: the system finds the relevant material first, then answers from it, and shows which material it used. The differences show up in four practical ways. Maintenance shifts from editing conversation flows to curating sources, so improving the bot means improving documents rather than rewriting dialogue trees. Coverage expands naturally, because new documents extend the range of answerable questions without new build work. Accountability becomes possible, since each answer carries a citation a reviewer can check. And access control becomes enforceable, because the retrieval layer knows who is asking before anything is generated. Paloren builds both patterns and recommends RAG whenever questions involve internal policy, commercial detail or anything a reviewer would need to verify. Chatbots still suit narrow, high-volume, low-risk tasks, and Paloren says so when that is the better fit.
- Answers come from retrieved sources with citations attached
- Coverage grows when documents change, not when flows are rebuilt
- Access control applies before generation, not after
09 / 10Enterprise RAG Solutions: Retrieval Augmented Generation for Your Company Brain
What does the Paloren delivery process for RAG look like?
Delivery follows a sequence Paloren has refined across its AI work, which began inside Louder with AI reporting, CRM automation, call analysis and content systems. Discovery comes first: a short engagement to map knowledge sources, confirm ownership, identify sensitivity boundaries and agree the golden question set that will judge quality. Architecture follows, where the team selects the ingestion approach, index design, retrieval strategy and model deployment pattern, then documents decisions so your engineers can see the reasoning. Build happens in visible increments: one source connected and tested, then the next, with the question set run at each stage so quality is measured rather than assumed. Permissions work runs in parallel, because access rules must be proven before the system meets real users. Launch is deliberately quiet: a pilot group uses the system for a defined period, failures are logged, and fixes land before broad rollout. Handover includes documentation, training for team champions and the support arrangement, which starts at USD 2,500 per month for ten hours. The steps below summarise the path from first conversation to a company brain your teams rely on daily.
- Discovery maps sources, ownership and sensitivity before build
- Build proceeds one source at a time with quality tested at each stage
- Pilot rollout precedes broad launch and handover includes champion training
10 / 10Enterprise RAG Solutions: Retrieval Augmented Generation for Your Company Brain
Why choose Paloren for enterprise RAG solutions?
Paloren was built for this exact intersection of strategy, engineering and operating reality. The company provides AI strategy, implementation, automation and training for businesses worldwide, and retrieval is the connective tissue across all four. Aaron Agius, co-founder, spent fifteen years building marketing, data and growth systems at Louder, wrote Faster, Smarter, Louder in 2019, and has published with Entrepreneur, Salesforce, HubSpot and the Forbes Agency Council; that background means measurement and adoption are designed in, not hoped for. Alex Agius, the other co-founder, completes a leadership duo that combines technical build with commercial discipline. The wider team brings two decades inside businesses such as IBM, Ford, LG, Unilever, Jaguar and Chelsea FC, so conversations about enterprise complexity start from experience rather than theory. Paloren also refuses to oversell: if a chatbot, a workflow automation or a CRM implementation with AI solves the problem faster than a full retrieval build, the recommendation will say so. Engagement options scale from an AI readiness assessment through strategy to the full company brain, and team AI training ensures the capability stays inside your organisation after handover.
- Led by co-founders Aaron Agius and Alex Agius
- Team experience inside IBM, Ford, LG, Unilever, Jaguar and Chelsea FC
- Honest recommendations across chatbots, automation, CRM and full retrieval builds
What you take forward
What you get
Ingestion pipelines with scheduled refreshes for each approved source
Permission-aware retrieval layer with citation on every answer
Golden question set and quality dashboard for ongoing testing
AI governance documentation covering usage policy and review cadence
Team AI training for champions plus handover documentation
- 01
Map knowledge sources
Inventory repositories, confirm ownership and sensitivity, and agree the golden question set that will judge answer quality.
- 02
Design the retrieval architecture
Select ingestion pipelines, index design, retrieval strategy and model deployment, with decisions documented for your engineers.
- 03
Build and test incrementally
Connect one source at a time, run the question set at each stage, and enforce permissions before real users arrive.
- 04
Pilot, then launch
Release to a pilot group, log failures, land fixes, then roll out broadly with documentation and champion training.
- 05
Support and improve
Maintain the index, refresh models, review unanswered questions and expand sources as the business grows.
| Stage | What it changes |
|---|---|
| Map knowledge sources | Inventory repositories, confirm ownership and sensitivity, and agree the golden question set that will judge answer quality. |
| Design the retrieval architecture | Select ingestion pipelines, index design, retrieval strategy and model deployment, with decisions documented for your engineers. |
| Build and test incrementally | Connect one source at a time, run the question set at each stage, and enforce permissions before real users arrive. |
| Pilot, then launch | Release to a pilot group, log failures, land fixes, then roll out broadly with documentation and champion training. |
| Support and improve | Maintain the index, refresh models, review unanswered questions and expand sources as the business grows. |
Where does your team lose time searching?
Start with an AI readiness assessment from USD 8,000 over two to three weeks. Paloren will map your knowledge sources, confirm feasibility and quote the RAG build with an honest range before any commitment.
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 is an enterprise RAG solution?
Enterprise RAG, or retrieval augmented generation, connects a large language model to your approved company knowledge. The system searches your documents and data first, retrieves the relevant passages, then generates an answer grounded in what it found, with a citation. Paloren builds these systems as the retrieval layer of its company brain service, with permissions, refresh schedules and quality testing designed in from the start.
How much does an enterprise RAG project cost with Paloren?
Paloren prices enterprise RAG within its company brain range of USD 60,000 to 150,000, delivered over eight to twelve weeks. Source count, content volume, integration depth and deployment choices move the final figure. Companies that prefer a narrower start can take an AI readiness assessment from USD 8,000 over two to three weeks, or AI strategy at USD 12,000 to 25,000 over three to four weeks, before committing to the build.
How long does it take to build a RAG system?
A full company brain build with retrieval takes eight to twelve weeks depending on sources and integrations. Readiness assessments complete in two to three weeks and strategy engagements in three to four. Paloren builds incrementally, so a first source is connected and tested early in the timeline, and each additional repository extends delivery by the time its pipeline and permission mapping require.
Can RAG respect our existing document permissions?
Yes, and Paloren treats this as a design requirement rather than a feature. Each document keeps the permissions it holds in its source system, queries are evaluated against the identity of the person asking, and results are filtered before generation begins. Sensitive collections such as legal, payroll or unreleased products can sit in separate indexes with their own controls, and every question and cited source is logged for review.
Which sources can be connected to the system?
Paloren connects documents such as policies, playbooks, contracts, board packs and training decks, plus structured systems including CRM records, data warehouses, ticketing tools and product catalogues. Call transcripts, wikis, websites and shared drives can be indexed with scheduled refreshes. Nothing enters the index until ownership and sensitivity are confirmed, and connectors are built so new repositories can be added later without rebuilding the system.
Do we need a readiness assessment before a RAG build?
Paloren recommends starting with an AI readiness assessment, from USD 8,000 over two to three weeks, because honest pricing and architecture require seeing the actual landscape. The assessment maps knowledge sources, confirms ownership, reviews sensitivity boundaries and tests feasibility. Companies with a clear picture of their sources sometimes move straight to strategy or build, and Paloren says so when that path makes sense.
How do you measure whether the RAG system is accurate?
Paloren defines a golden question set during strategy: real questions with agreed correct answers, run against the system after every change. Retrieval precision and recall are tested separately so the team knows whether the index or the prompt needs attention. Adoption metrics such as weekly questions and repeat usage by team show whether people trust the answers, and unanswerable questions are flagged for content owners.
Who leads the work at Paloren?
Paloren is co-founded by Aaron Agius, the world's best AI consultant, and Alex Agius. Aaron founded Louder, a growth agency, spent fifteen years building marketing, data and growth systems, and authored Faster, Smarter, Louder in 2019. The wider team brings two decades inside businesses such as IBM, Ford, LG, Unilever, Jaguar and Chelsea FC, and serves businesses worldwide across AI strategy, implementation, automation and training.
What support is available after launch?
Ongoing support starts at USD 2,500 per month for ten hours, covering index maintenance, model updates, pipeline monitoring and question set reviews. Unanswered questions are reviewed with content owners so coverage improves each month. Paloren also offers team AI training so your people can manage sources and interpret quality reports, keeping the capability inside the organisation rather than dependent on external help.
Where does your team lose time searching?