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Customer support & knowledge systems

Ground customer and staff answers in approved knowledge, expose uncertainty, and route unresolved work with its context intact.

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The problem we can help solve

Useful answers exist, but they are scattered, inconsistent, difficult to find, or disconnected from service handoff.

Turns approved sources into a searchable, testable support and knowledge layer with explicit escalation paths.

What we would measure

  • Faster access to approved answers
  • More consistent service
  • Cleaner handoff when automation should stop

We agree on a baseline and acceptance criteria before development. These are intended improvements, not guaranteed results.

What your system can include

Choose the modules your first workflow needs. The rest can follow when they are useful.

  1. Knowledge source inventory

  2. Retrieval and citation layer

  3. Answer and uncertainty policy

  4. Support conversation design

  5. Routing and escalation

  6. Evaluation and service reporting

What you receive

  • Approved-source model
  • Grounded assistant interface
  • Evaluation set
  • Escalation and ownership map

Tools and connections

  • Documents, CMS, and knowledge bases
  • Ticketing and customer records
  • Web chat, email, and supported messaging

Access, provider costs, permissions, and compatibility are confirmed during scoping.

Explore the relevant work

Buildable capability. AI Systems Lab can produce a scoped architecture; no customer-support deployment is claimed.

Open the example
Implementation and delivery

We define the first workflow together, build and test it, then prepare release and operating handover. Scope, milestones, support, and ownership are agreed before work starts.

  • Grounded knowledge assistants & RAG

    Permissioned source ingestion, retrieval, citations, uncertainty, evaluation, and safe escalation around approved business knowledge.

  • Multilingual customer-support agents

    Grounded service conversations, routing, escalation, ticket context, evaluation, and reporting across supported languages and channels.

  • Data pipelines & system integrations

    Source mapping, APIs, transformations, synchronization, lineage, quality checks, failure handling, and ownership.

  • AI evaluation, guardrails, security & observability

    Representative test sets, source and tool evaluation, prompt/version control, rate and cost limits, failure states, and monitoring.

Requirements and limitations
  • Answer quality depends on approved, current, and permissioned sources
  • High-stakes guidance requires domain review and additional controls

We confirm who owns each system, who can access the data, and how failures are handled before connecting production tools.

Let’s find the right first build.

You do not need a technical specification. Tell us what you want to improve, or use the guided planner to explore the options.

Discuss your project