Institutional Memory
Company Knowledge Layer
Meetings, decisions, and project documents flow into one searchable company memory. Ask “what did we agree with that vendor in March?” and get the answer with the source — even after the person who knew has left.
The knowledge you need to run your business already exists — it's trapped in Slack threads, half-finished docs, and senior heads. This turns all of it into one answerable, sourced memory.
Potential value on day one
+$214,500/yr freed
Example — 20 people × $55/hr × 5 hrs × 52 wks × 75% straight-through processing.
Model this with your own headcount below ↓
The value math
Before, after, and what it's worth.
On the left is what it costs to keep doing this by hand. On the right is what happens after the package goes live — and what that frees up on day one.
Today
Today · Hunting for context
How your team finds answers today
- 1
Someone needs an answer — a client's history, a past decision, a spec sheet
The question surfaces in Slack, in a meeting, or mid-project. There's a right answer somewhere in the company.
- 2$
They search Slack, Notion, Confluence, drives — most give partial or stale hits
Each system has its own search box. Old versions surface first. The good answer is buried three folders deep.
- 3$$$
They interrupt colleagues who 'might remember' — burning two people's time
Slack DMs, hallway questions, calendar holds with the SME. Every interruption costs both sides context.
- 4$
They rebuild the context from fragments — sometimes duplicating work already done
Piece together what past-them decided. Sometimes recreate a whole analysis that's sitting on someone's laptop.
- 5
They finally get to work — or the moment has passed
The question is answered. Or the customer moved on. Or the deadline hit and it went out with best-guess.
Human in the loop
Every person, every day, breaking flow to hunt for context that already exists somewhere.
The hidden cost — knowledge drift
The same question gets answered slightly differently by different people. Decisions drift, standards splinter, past commitments get repeated or contradicted. Onboarding cost balloons because every new hire has to reconstruct what everyone else already knows. SME departures become crises — the knowledge leaves with them.
Yearly expense — today
−$286,000/yr
100% payroll burn on manual data entry.
20 people × $14,300 fully-loaded per seat.
After DEBCOR AI
With DEBCOR AI · Company memory that answers
How your team finds answers after the package ships
- 1
The same question — same channel, Slack, Teams, or web
No behavior change for the asker. They ask the way they already ask.
- 2
AI searches every indexed source at once — decisions, docs, tickets, transcripts
One retrieval pass across every source. What used to take five separate searches is one.
- 3
The answer comes back in seconds, with the source cited
Around 75% of questions resolve instantly. The asker keeps their flow, the SME never gets pinged.
- 4$
Ambiguous queries route to the domain expert — but only once
The remaining 25% land with the right person, with context attached. Their answer becomes canonical — future asks resolve instantly.
- 5
Every clarification compounds — recall climbs, exception rate shrinks month over month
The system gets better on its own. What was ambiguous last month is instant this month.
Human in the loop
Only the domain expert, only for genuinely novel questions.
Knowledge drift disappears
One canonical answer, every time, sourced. New hires start productive on day one — they have the same context a five-year veteran does. SME departures stop being crises because the knowledge is already captured. Decisions across teams stay consistent because everyone's asking the same source.
Yearly savings — with DEBCOR AI
+$214,500/yr
75% of your $286,000/yr labor pool redirected to real work.
Range: $185,900–$243,100/yr at 65–85% STP.
Directional example, not a quote. Fully-loaded rate assumes $55/hr — swap in your own if it's different. Freed capital = payroll cost the process stops burning, before package fees. Actual value depends on document mix and your straight-through target, set in the scope phase.
What's inside
Everything the package ships with.
Connectors to every source your knowledge already lives in — Google Drive, Notion, Confluence, SharePoint, Slack, Zendesk, Gmail, Zoom, Loom, and more
One governed knowledge layer with a vector store — every source indexed, chunked, and re-embedded on a cadence
A chat interface that answers with sources cited — deployed in Slack, Teams, and web
Access control honoring your existing IdP — people see only what they're allowed to see
A full audit log of every question, answer, and source citation
A domain-expert review loop so ambiguous queries teach the system permanently
Sizing & published pricing
Three sizes. One architecture.
Scope compresses down, not features. S-tier is playbook-driven — a single senior engineer delivers against pre-built accelerators inside a fixed configuration boundary. M-tier extends the same systems into landscapes with multiple sources, custom rules, and real integration complexity. L-tier is the full enterprise engagement: dedicated environments, SOC 2 wrap, roadmap co-development, and — for SAP customers — clean-core BTP integration. Same architecture at every size.
Band
≤ 500 employees · one system, one team
Timeline
4–6 weeks
Published price
from $18k
Scope at this tier
One team, up to 5 sources, playbook-driven configuration by a single senior engineer.
Band
500–5,000 employees · multiple sources or rules
Timeline
8–12 weeks
Published price
$70–120k
Scope at this tier
Multiple teams, 10+ sources, custom permissions, tuned RAG for your domain.
Band
5,000+ employees · enterprise scope, SOC 2, roadmap co-development
Timeline
12–16 weeks
Published price
$220–400k
Scope at this tier
Enterprise-wide rollout, SOC 2 wrap, custom UI surfaces, and — for SAP customers — Memoria integration on BTP.
Published bands are directional. Final commercial terms are per-client SOW. Managed operations are quoted separately.
Timeline
8–12 weeks from scope to live.
Every package follows the same four-phase delivery. What changes at each tier is the count of sources, rules, and integration surface — not the shape of the work.
Scope
Weeks 1–2
Inventory sources, map access rules, agree governance and personas.
Build
Weeks 3–6
Deploy connectors, populate the vector store, tune retrieval against a golden set.
Validate
Week 7
Golden-set tests with your team — measure recall and citation accuracy.
Live
Weeks 8–12
Launch chat surfaces (Slack, Teams, web), measure adoption, expand sources.
For SAP customers · Bridge to SAP-native
Runs stack-agnostic today — Google Workspace, Microsoft 365, Notion, any combination. When you're ready, the same knowledge layer grounds DEBCOR Memoria and Joule Studio 2.0 agents inside SAP — same architecture, same sources, no re-indexing. See how DEBCOR extends this into SAP →
Package FAQ
Common questions.
Slack, Google Drive, Notion, Confluence, SharePoint, Zendesk, Gmail, Zoom, Loom transcripts, plus any REST or file-source you point us at. Connectors are additive — start with 3–5, add more as adoption grows.
No. The access control layer honors your existing identity provider (Okta, Azure AD, Google Workspace). If a user can't read a document in the source system, they can't retrieve it through the chat interface either. Every answer respects the asker's permissions.
The agent tells you it doesn't know rather than hallucinating. Genuinely-missing answers route to the domain expert with the original question — their response is captured, indexed, and cited for the next asker. The knowledge base grows on its own.
Per-query inference cost is small through a tiered model architecture. A frontier reasoning model tunes retrieval and the ranking heuristics during scoping — a one-time cost. In production, retrieval against your own vector store handles most of the work with no LLM call at all. When an answer needs synthesis, a high-efficiency low-cost model handles it at fractions of a cent per query. The frontier model is only re-engaged for genuinely novel or complex synthesis — rare after the first month. At production volumes, AI cost is a rounding error against the knowledge-worker time it saves.
Yes. The same knowledge layer powers our DEBCOR Memoria product on SAP BTP. Sources you index today carry forward when you extend into Joule Studio 2.0 or SAP-native agent workflows — same architecture, no re-indexing.
Scope this package
Fixed scope, fixed price. Tell us the document types and the systems, and we'll come back with a one-page proposal.
Start scopingNot sure this is the one?
Book a 30-minute fit call with a senior architect. We'll tell you if this is the right package, a different one, or nothing at all.
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