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Workspace Memory

Your AI agent remembers every ticket, every customer, every conversation.

Workspace-scoped AI memory: every ticket, customer note, KB article, voice transcript — embedded, searchable, and grounded in citations. Your data, your region, never shared.

SCROLL TRACERAG / LIVE
Where's my last invoice?
ticket #4821KB articlecustomer profilecustom fieldvoice transcriptbilling event

I found the recovered invoice flow and your last billing ticket. The invoice was reissued after the card update, and the download link is safe to resend.

[Ticket #4821][KB: Invoice Recovery]
§ 01

Not just search — context.

Beamdesk turns workspace history into grounded support memory, so the agent sees the customer, the account, and the proof behind an answer.

Search finds documents

Classic RAG retrieves a handful of KB passages and stops there.

Memory finds patterns

Workspace Memory connects tickets, replies, notes, fields, and calls around the customer.

Agents act with proof

Every generated reply carries citations your team can inspect before sending.

§ 02

Data sources we embed

Support knowledge is broader than help center articles. Beamdesk indexes the operational record that agents actually need.

Tickets
Replies
KB articles
Customer profiles
Custom fields
Voice transcripts
CRM notes
Billing events
Web pages
Macros
Agent feedback
Tool results
§ 03

How it works

A compact retrieval pipeline keeps the agent grounded without mixing one workspace with another.

01

Ingest workspace history

Tickets, articles, calls, and customer records stream into a scoped memory index.

02

Retrieve with tools

The agent calls search_similar_tickets and customer_pattern before drafting.

03

Answer with citations

Replies include source chips, confidence signals, and feedback loops.

§ 04

Seven moats

Workspace Memory compounds because it is built from the work your team already does every day.

Full-history context

Embeds every ticket and reply, not just KB articles.

EU sovereignty

Frankfurt region controls and DSAR deletes keep customer data clean.

Six languages native

Polish-first retrieval quality via Voyage AI and multilingual indexing.

Cited answers

Every reply links back to the source ticket, article, or transcript.

Compounds over time

Team feedback becomes a stronger retrieval signal every week.

Tool-augmented retrieval

Agents invoke search_similar_tickets and customer_pattern as needed.

Cheaper at scale

Modeled around roughly $0.50 per seat plus light usage, not $50 per agent.

§ 05

Built for memory, not just deflection

The core difference is workspace scope: Beamdesk retrieves the support record, not only polished articles.

FeatureBeamdeskIntercom FinDecagonSierra
Workspace historyTickets, replies, notes, callsMostly help center + inbox contextEnterprise knowledge layerEnterprise knowledge layer
CitationsSource chips on every answerAvailable by configurationAvailable in enterprise flowsAvailable in enterprise flows
EU regionFrankfurt-firstPlan dependentContract dependentContract dependent
Language postureSix native localesBroad multilingualEnterprise multilingualEnterprise multilingual
Cost model$0.50/seat + usageAbout $50/seatEnterprise contractEnterprise contract
§ CALC

Cost calculator

Estimate the monthly delta between workspace memory pricing and a seat-heavy AI agent model.

Beamdesk
$63
Intercom Fin
$1,250
You save
$1,188
EU hosted
GDPR-clean deletes
SOC 2 controls
Workspace scoped
No shared training
§ 06

Questions before rollout

The feature is designed for controlled deployment: scoped data, inspectable answers, and clear costs.

Is this separate from the knowledge base?
Can agents inspect sources?
How is data isolated?
Does it replace human agents?
How fast can we start?