The verdict in three sentences
A RAG (Retrieval-Augmented Generation) assistant over a consulting firm's document estate costs 20,000 to 50,000 EUR in Singapore in 2026, excluding usage-based API costs. It cuts time spent hunting for a deliverable, a methodology or a reference by 30 to 50%, and every answer cites its internal sources. The critical issue is not the model but governance: who accesses what, and how you stop the AI from making things up.
2026 cost structure
Price depends on document volume, granularity of access rights and traceability requirements. Here are the line items.
| Line item | 2026 range (EUR) | Detail |
|---|---|---|
| Scoping & access governance | 3,000 - 7,000 | Rights matrix, confidentiality |
| Vector indexing pipeline | 5,000 - 14,000 | Ingestion, chunking, embeddings |
| Search + generation engine | 6,000 - 15,000 | Answers with citations |
| Hallucination control | 3,000 - 7,000 | Guardrails, confidence thresholds |
| Interface & integration (SharePoint, Drive) | 2,000 - 6,000 | Connectors, SSO |
| UAT & go-live | 1,500 - 3,000 | Tests, reference sets |
Usage cost and measured gains
Recurring cost depends on query volume and the size of indexed documents. 2026 estimate for a firm of 30 to 80 consultants.
| Metric | Before | After | Detail |
|---|---|---|---|
| Doc-search time / week | 5 h/consultant | 2.5 - 3.5 h | -30 to -50% |
| Answers with verifiable source | 0% | 95% | Mandatory citations |
| Monthly API cost | - | 300 - 900 EUR | By volume |
| Indexed documents | - | 20,000 - 100,000 | Deliverables, methods |
| Target hallucination rate | - | < 2% | With guardrails |
| Reuse of past deliverables | Low | +40% | Knowledge capture |
Mini case study
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Wei, partner at a 40-consultant firm in Singapore, estimates each consultant loses 5 h/week searching references in past deliverables. At a loaded cost of 60 EUR/hour, that is 480,000 EUR/year of non-billable time. After a 38,000 EUR RAG assistant (plus 600 EUR/month of API), search time falls by 40%, i.e. 2 hours saved per consultant per week: ~192,000 EUR/year recovered. The project pays back in under 3 months.
FAQ
How is client-deliverable confidentiality guaranteed? Access governance is wired from the start: each consultant only sees documents they are entitled to, via SSO and a permissions matrix. Sensitive data can stay on dedicated regional infrastructure.
Can RAG hallucinate? The risk exists but the RAG architecture limits it: the model only answers from retrieved documents, with citations, and stays silent or escalates below a confidence threshold. A reference test set keeps the rate under 2%.
How long to deploy? Expect 8 to 14 weeks in 2026 depending on document volume and connectors (SharePoint, Google Drive, DMS). A pilot on a narrow scope can ship in 4 to 6 weeks.
Do we need to clean our documents first? Minimal triage helps (obsolete versions, duplicates), but the pipeline handles heterogeneity. We prioritize high-value documents first: methodologies, recent deliverables, client references.
Which AI model is used? We select the model based on your confidentiality and cost requirements: strong cloud models for general use, or self-hostable models when data must not leave. The choice is neutral and documented.
Let's scope your project. Share your document volume, storage tools and confidentiality requirements; we will price a custom RAG assistant with an indicative budget of 20,000 to 50,000 EUR plus API. Detailed quote within 48 h. WhatsApp +221 77 596 93 33.
Mohamed Bah
Fondateur, Kolonell
Passionate about digital and entrepreneurship in Africa, Mohamed has been helping Sénégalese businesses with their digital transformation since 2020. Founder of Kolonell, he believes every SME deserves a professional and accessible online présence.
