Live implementationGrounded AI assistant (RAG)

Sales Knowledge Assistant

One assistant that answers product and service questions from every manual and file, with the source for each answer.

99%answer accuracyReported
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Sales Knowledge Assistant
How it works

Technical answers in seconds, not hours of chasing.

Industrial and technical product companies, distributors, and B2B sales and support teams with large catalogues and deep documentation.

01
✉ EmailPDFXLS

Ask

A rep types the customer's question.

02
Approved ✓

Search

The assistant finds the most relevant passages across all documents.

03

Answer

It writes a precise answer from those passages only.

04
Approved ✓

Cite

Every answer shows the document and section it came from.

05
Approved ✓

Improve

Rep feedback flags weak answers and improves the next one.

The business workflow

What happens, and where people decide.

◎What starts it

A sales rep types a customer's question, in plain language.

⇣What goes in

Product manuals, datasheets, technical files, service documents and approved business knowledge.

✕Manual work it replaces

Emailing other departments, searching shared drives and waiting for an expert to reply.

✦What it produces

A precise answer with the document and section it came from, ready to use with the customer.

☺Where a person decides

Reps and subject experts correct or approve answers. That feedback improves the next answer. Sources are always shown so the rep can check.

Before
Hourschasing answers across departments
Terabytesof manuals searched by hand
Inconsistentanswers by person
→
After
Secondsto a cited answer
Oneplace to ask, for every rep
Consistentanswers from the same sources
High-level architecture

From input to outcome.

Full architecture pack
1

Inputs

  • Product manuals and datasheets
  • Technical and service documents
  • Approved business knowledge
2

Ingestion

  • Document parsing and chunking
  • Embeddings generated per passage
3

Retrieval

  • Vector search index
  • Most relevant passages selected per question
4

AI layer

  • Large language model writes the answer from retrieved passages only
  • Model chosen per deployment
5

Human review

  • Reps and experts rate and correct answers
  • Feedback loop into the knowledge base
6

Outcome

  • Cited answer for the customer
  • Faster lead qualification
  • Usage and feedback logs
IntegrationsDocument stores and shared drives (export or connector)Optional: CRM, to log questions against opportunities
Design principle

Grounded, not guessing. The assistant answers from the company's own documents and shows its sources. If the documents don't hold the answer, it says so.

Technology stack

What it runs on, and why.

PatternRetrieval-augmented generation (RAG) with source citations

Answers come only from retrieved passages of the customer's own documents.

AI modelLarge language model, chosen per deployment

Reference implementation's model to be confirmed by the technical owner. Our Knowledge Base product uses large language models; a large language model on a managed cloud AI service is also supported.

EmbeddingsText embedding model

Turns each passage into a vector so meaning-based search works.

Data layerVector index plus the source document store

Keeps passages, vectors and links back to the original page.

FeedbackRep and expert ratings and corrections

Improves retrieval and answers over time.

SecurityAccess limited to the customer's team

Documents stay in the agreed environment; hosting options include the customer's own cloud.

Business value and evidence

Every number, labelled.

Live implementation. Running for a customer or as a public product.

FigureWhat it measuresLabelSourceLimits
99%answer accuracy across technical and support questionsReportedReference customer in industrial products (name withheld).Evaluation method and question set to be confirmed before external use.
Hundredsof products and 30+ services coveredReportedReference customer scope.Scope description, not a performance metric.
Hours → secondstime to an answerTargetedDesign goal, consistent with customer feedback.Not timed in a controlled study.
Proven on a hard case

Hundreds of products, 30+ services and terabytes of manuals. Reps used to email different departments for each answer. Now they ask once and get a cited answer, and the team qualifies leads sooner.

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