Every question a customer can answer on their own saves time for both sides. We find your most common questions and build clear answers and guided steps into the product.
What’s included.
Three steps.
Find
We rank the questions customers ask most.
Build
We write answers and guided steps.
Measure
We track what gets solved without help.
Recognise the challenge?
Start with the problem you see. Explore what we can change, then open the implementation notes for the detail.
01Multilingual customer-support knowledge agent
What you may be seeing
- Support answers the same questions in several languages.
- Translated help articles disagree with the current policy.
- The chatbot answers confidently from outdated documents.
- Customers cannot get a useful answer outside office hours.
- The assistant cannot hand an unresolved question to the right staff.
We connect customer questions to approved support knowledge, with appropriate language handling and a clear route to a person.
Implementation & verification
We connect approved, maintained support content to a multilingual question-answering agent with explicit supported topics. We configure a managed support agent or code retrieval and response logic when the existing platform cannot handle the required sources or workflows. We preserve source references, separate account-specific lookups from general guidance and route unsupported answers to staff. We evaluate common questions in each supported language, including stale-policy, ambiguity and escalation cases.
How we verify the work
- Version the knowledge sources and assign ownership for changed policies and translations.
- Check retrieved evidence and prevent general answers from exposing account information.
- Escalate low-evidence, repeated-failure and explicit human requests with useful context.
- Measure correct resolved answers, reopened issues and handover quality by language.
Tools considered for this work
Delivery: support-platform configuration; development for custom sources, retrieval or actions. Managed option: Intercom Fin with approved knowledge and handover workflows. Custom option: Claude API (a controlled connection between applications) with Elasticsearch/Qdrant retrieval. Integration: existing help centre, ticketing/account APIs (controlled connections between applications) and multilingual content workflow. QA: language-specific question sets and resolution/handover review.
02Guided customer troubleshooting for a defined issue
What you may be seeing
- Customers wait for staff to repeat simple troubleshooting steps.
- Help articles are too broad for the actual problem.
- Customers repeat failed steps because progress is not remembered.
- A chatbot suggests irrelevant or potentially damaging actions.
- Staff cannot see what the customer already tried.
We guide customers through a defined troubleshooting process, checking the outcome at each step.
Implementation & verification
We turn a defined, harmless diagnostic procedure into a guided troubleshooting flow. We code state tracking, approved checks and decision branches so the next step depends on observed results. We limit automatic actions to supported safe operations and hand unresolved cases to staff with the completed steps and evidence. We test known fault scenarios and stop conditions before presenting the flow as self-service support.
How we verify the work
- Define the supported issue and explicit safe checks, stop conditions and escalation route.
- Choose branches from actual outcomes and preserve previous attempts.
- Avoid broad model-generated troubleshooting beyond the approved procedure.
- Measure verified resolution, repeated failed steps and useful context delivered to staff.
Tools considered for this work
Delivery: workflow/frontend/backend development; optional support-platform configuration. Flow: deterministic decision tree or LangGraph-backed state machine. Platform option: existing Intercom/Zendesk help flow where adequate. Evidence: permitted status/diagnostic endpoints and customer-provided observations. QA: known fault/branch fixtures and staff-handover tests.
03Customer-approved AI-to-human handoff packet
What you may be seeing
- Customers repeat everything when the chatbot transfers them.
- Staff cannot see which steps the assistant already tried.
- The bot sends irrelevant or inaccurate summaries to support.
- Customer details are passed along without useful controls.
- Transfers create duplicate tickets or lose the conversation.
We prepare a useful handover for a human support team, with the customer reviewing what will be shared.
Implementation & verification
We build a structured handover packet containing the customer's supported request, verified context and attempted steps. We code or configure the transfer workflow so the customer can review relevant shared details and staff receive the original evidence where needed. We preserve conversation and ticket identifiers across the transfer and route the case to the correct supported queue. We test packet accuracy, failure recovery and duplicate-transfer behavior before using it for live assistance.
How we verify the work
- Separate verified account facts, customer statements and the assistant's attempted actions.
- Show relevant transferable information and exclude unnecessary conversation content.
- Create or update one durable ticket and preserve the conversation link on retries.
- Measure repeated questions, packet corrections and time to useful human assistance.
Tools considered for this work
Delivery: support-platform configuration + custom integration when needed. Managed option: Intercom Fin workflow/human handover. Custom option: LangGraph interrupt/review step and ticketing API (a controlled connection between applications). Packet: typed request/context/attempt fields with transcript references. QA: transfer replay, permission/content review and ticket deduplication checks.
04Customer-facing explanation of business results
What you may be seeing
- Customers receive a report but do not understand the result.
- Staff spend calls explaining inputs and changes.
- An explanation uses different figures from the actual report.
- AI invents reasons for a change in business results.
- Customers do not know which next action is supported.
We explain business results using approved records and definitions, distinguishing confirmed evidence from missing information.
Implementation & verification
We connect explanations to the same verified records and definitions used to produce the customer-facing result. We code deterministic comparison and evidence retrieval, then use approved templates or a grounded assistant to explain the supported findings. We distinguish observed changes from proven causes and present only relevant next actions permitted by the business workflow. We test factual consistency against reference results and let customers reach staff when the explanation cannot answer their question.
How we verify the work
- Compute changes outside the language model and pass exact evidence into the explanation.
- Show inputs, definitions and unknown causes instead of inventing a narrative.
- Keep customer access and allowed next actions aligned with the underlying result system.
- Measure factual agreement, customer comprehension and avoidable explanation-related support calls.
Tools considered for this work
Delivery: reporting/backend + explanation/UI development. Facts: existing result/report API (a controlled connection between applications) and governed metric definitions. Explanation option: approved templates; Claude tool use for supported conversational questions. Evidence: source records, rule version and calculated comparisons. QA: reference report/explanation pairs and access-boundary checks.
Make self-service useful.
We connect the assistant to approved support knowledge and permitted account information. It cites evidence, follows defined troubleshooting steps and prepares a customer-reviewed handover when a person is needed.
Explore the technical toolkit
Selected around your existing stack, licences, permissions and agreed scope.
Define what better means.
Resolution
Supported tasks completed correctly without repeat contact.
Control
Verified access boundaries, confirmations and handover quality.
Effort
Manual steps and staff handling time for the agreed workflow.
Targets follow the baseline. These are measurement areas, not promised results.
Engineering terms, explained
- Grounded AI / RAG
- An assistant retrieves relevant approved evidence before generating an answer. Access controls and evaluations still need to be engineered and tested.
- Idempotency
- Retrying the same operation does not create an extra order, payment or record.
- Controlled experiment
- A concurrent comparison of assigned variants, with a defined outcome, observation window and decision rule.
- Core Web Vitals
- Measures of main-content loading, responsiveness to interaction and visual stability.
Technical references: Web Vitals · Azure document access · Structured data guidelines.
Quick answers.
Will it replace our support team?
No. It frees them for problems that need a person.
Can an AI assistant do this?
Where it suits, yes, using only answers you have approved, with a clean handover to a person.