Zolesco

Optional AI / RAG Assistants

Controlled support assistants for approved business, product and service knowledge

For suitable client projects, Zolesco can scope a support assistant that retrieves from an approved knowledge base and answers bounded questions about services, products, documentation or internal workflows. This is an optional client deliverable, not functionality embedded in the Zolesco website.

Discuss the assistant use case Inspect related work

Best fit

Who this is built for

  • Businesses with repeat service or product questions
  • SaaS products that need documentation assistance
  • Customer-support teams with a controlled knowledge base
  • Internal teams that need retrieval across approved operational content

Business problem

What it helps solve

  • Customers repeatedly ask questions already answered in approved documentation
  • Teams need faster access to internal or product knowledge
  • A generic chatbot would be too broad or difficult to control
  • The business needs explicit fallbacks and human escalation

Desired outcomes

What should change after the project

  • Answer approved questions from a controlled knowledge source instead of an unconstrained public prompt.
  • Define refusal, escalation and privacy boundaries before an assistant reaches users.
  • Connect an assistant to a parent website or software product only where the use case is justified.
  • Keep human ownership for decisions that should not be delegated to an automated response.

Price drivers

What changes the final quote

  • Knowledge-source volume, structure, update frequency and retrieval requirements.
  • Model/provider selection, usage limits and the parent product integration surface.
  • Authentication, privacy, logging, escalation and guardrail requirements.
  • Evaluation depth, source display, multilingual support and specialized workflow actions.

Client inputs

What we need from the client

  • The approved knowledge sources and a clear list of questions the assistant may and may not answer.
  • Privacy requirements, escalation owner and any prohibited data or decisions.
  • Access to the parent product and authorized AI/provider accounts when required.
  • Representative test questions and acceptance examples for evaluation.

Proof you can inspect

Fintech Operations Dashboard

This portfolio entry is labelled Concept. It demonstrates relevant interface and product thinking without being presented as commissioned client work.

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What you receive

A focused deliverable, not a vague bucket of hours.

Use-case, risk and knowledge-boundary definition
Approved knowledge ingestion and retrieval architecture
RAG-based answer flow with source-aware context
Guardrails, refusal and escalation behaviour
Integration into the approved client website or software
Evaluation plan and handover documentation

Scope boundaries

Clear expectations before development starts.

  • The assistant is not represented as infallible and should not be used as the sole authority for high-stakes medical, legal, financial or safety decisions.
  • Model/API usage, vector storage and third-party platform charges remain separate where applicable.
  • Knowledge quality, permissions, privacy and escalation responsibilities must be agreed before launch.

How we deliver

A visible path from scope to handover.

  1. 01

    Define the exact questions, users, approved knowledge and prohibited use cases.

  2. 02

    Prepare retrieval, grounding, privacy and escalation architecture.

  3. 03

    Build and integrate the assistant into the parent client project.

  4. 04

    Evaluate representative questions, failure modes and handoff procedures before release.

After launch

Ownership, deployment and after launch

  • AI/RAG remains an optional client-project feature; Zolesco itself does not operate an internal public AI advisor runtime.
  • Model/API usage, provider availability and metered costs remain subject to the chosen third-party service.
  • Knowledge updates, evaluation and ongoing monitoring require a defined maintenance owner.
  • The assistant must not be presented as a substitute for qualified human judgment in high-stakes use cases.

Delivery terms

The operating expectations are documented before work expands.

The delivery policy explains written scope, milestones, revisions, change control, acceptance, confidentiality, third-party dependencies, source-code handover and optional post-launch support.

Review delivery & ownership

Capability map

The core service behind this page

AI / RAG Support Assistants

Optional client-project assistants that answer approved questions from controlled business, product or service knowledge.

Quoted with the parent software scope

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Plan before you enquire

Buyer planning links

Review the planning hub, published pricing context and delivery responsibilities before starting a conversation. These pages explain scope drivers, ownership and handover without forcing a full specification into the first inquiry.

Planning resourcesPricing guidanceDelivery & handover

FAQ

Questions before you scope it.

The final proposal is based on the actual workflow, responsibilities and acceptance criteria—not assumptions.

Does this mean the Zolesco website itself uses an AI chatbot?

No. This page describes a service Zolesco can build for client projects. The Zolesco website does not require an AI advisor, chatbot, model runtime or RAG knowledge system.

Can the assistant answer only from approved content?

The architecture can be designed around a controlled knowledge base with retrieval, grounding and explicit behaviour when approved information is missing.

Can a human escalation path be included?

Yes. Escalation to a human, contact form, support channel or other approved workflow should be defined as part of the assistant's operating boundaries.

AI feature next step

Start with the approved knowledge and the questions the assistant should handle.

Share the parent product, knowledge sources, user questions, privacy limits and escalation path. We will determine whether RAG or another simpler workflow is justified before adding an AI dependency.

Discuss the assistant use case Review pricing context