Retrieval-augmented AI grounded in your SharePoint, Drive, Confluence, Notion and ticketing systems — with source-aware permissions, citation-enforced answers, and EU-sovereign deployment that public LLMs cannot match.
Every modern company has the same problem at a different scale. Documents proliferate; the right answer is somewhere; nobody can find it; the public LLMs that could find it cannot be trusted with the corpus. The result is a quiet, compounding productivity tax.
SharePoint, Confluence, Notion, Drive, Slack, the policy PDFs nobody opens. New hires take six weeks to find the on-boarding doc. Senior staff get interrupted constantly with questions the handbook already answers in section 4.
ChatGPT, Claude.ai, Gemini consumer surfaces — convenient until Legal finds your contract clauses in a free-tier transcript. The "we banned ChatGPT" memo solves the leak but leaves the productivity hole nobody filled.
Vendor RAG demos look brilliant on a 100-document corpus. Production at 10,000 documents, with real permissions and real updates, breaks the demo magic. The agent answers from a deleted policy. Trust collapses inside a month.
Five concrete layers from ingest to deployment. Built on the production-RAG playbook we deployed at four EU clients over 18 months — with the failure modes we learned about written directly into the architecture.
Three productised tiers. Same retrieval pipeline, same evaluation harness. The difference is breadth of source systems + retention + compliance grade.
Best for
Single-team pilot (HR, Legal, Ops, or Finance) over a focused corpus of <10k documents from one source system.
Outcome
Single team finds answers in seconds; "where is X policy" Slack questions to senior staff drop 60-70%.
Best for
Multi-team operations across HR + Legal + Ops + Finance, multiple source systems, with monthly content growth.
Outcome
Cross-functional knowledge layer; new-hire ramp shortens 3-4x; senior staff stop being interruption-driven.
Best for
Regulated entities (DORA / NIS2 financial services, healthcare, legal practices, public sector) requiring audit-grade RAG with EU sovereignty and 7-year retention.
Outcome
Audit-grade RAG with full evidence chain; defensible posture under DORA + NIS2 supervisory review.
Looking for a bespoke implementation owned outright instead of consumed as SaaS? DECKLOG Implementation · from €4,990
Microsoft 365, Google Workspace, Atlassian, Notion, ServiceNow, GitHub — the corpus surfaces where modern enterprises actually store knowledge. ACL-aware, webhook-driven, reconciled nightly.
The model + the vector store + the trace history all run inside infrastructure you control. Three deployment patterns covering every EU regulatory profile from "managed-cloud is fine" to "no third-party data plane, period".
Azure OpenAI EU (Sweden Central) or AWS Bedrock EU (Frankfurt) for generation. Vector store inside your Azure / AWS account. Customer Lockbox enabled where supported. Suitable for the majority of regulated EU SMB workloads.
Generation on EU-region managed providers; vector store + audit log on your bare-metal infrastructure (Hetzner / OVH / IONOS / on-prem). Reduces cloud surface area while retaining managed-model quality.
Self-hosted Llama 3.1 70B FP8 or Qwen 2.5 72B (multilingual) on your bare-metal. No third-party data plane. The model weights stay yours. Suitable for the most regulated tenants where US-jurisdiction providers are the threat model itself.
The audit trail produces evidence packs as a side effect of operation. The Sovereign tier adds auditor-ready formatting + 7-year retention + DORA Article 28 third-party ICT-risk evidence with sub-processor disclosure.
Third-party ICT-risk evidence: model provider, hosting region, sub-processors, exit strategy, concentration analysis.
Technical baseline: PII redaction at boundary, MFA-bound query surface, audit logging, incident-response integration.
Right-to-erasure SLA documented; records of processing produced automatically with sub-processor + retention disclosure.
A.5 organisational + A.8 technological controls mapped per workflow with timestamped evidence chain.
DECKLOG is not a black-box demo. The eval harness lives in your CI. The traces live in Langfuse inside your tenant. The provider abstraction means switching from Anthropic to OpenAI to self-hosted is a config change, not a rewrite.
The golden question set + automated retrieval + answer evals run on every retrieval-code commit. Quality regressions get caught before they reach users. The harness is the product, not an afterthought.
Every query: which documents were retrieved, what the model decided, what the user received, how long it took, what it cost. Langfuse runs self-hosted in your tenant; no third-party trace store.
The vector store + the index + the configuration + the eval datasets all live in your cloud account from day one. Cancel the SaaS subscription; the deployment keeps running. No vendor-lock-in clauses; no hostage data.
Only if you let them. Default deployment runs in your tenant: Azure OpenAI EU region or AWS Bedrock EU region for generation; vector store inside your cloud account; ingestion pipelines reading from your source systems via OAuth. No third-party data plane. Sovereign tier deploys self-hosted open-weights models (Llama 3.1 70B FP8 or Qwen 2.5 72B) on your bare-metal infrastructure, eliminating the cloud-provider hop entirely.
Source-of-truth permission inheritance at ingest + re-validation at query time. When a document is indexed, DECKLOG records the principals that could read it. When a user queries, DECKLOG expands their current group memberships from your IdP (not from the stored permissions) and filters retrieval results before reranking. A document deleted or re-permissioned in SharePoint stops appearing in answers within minutes via webhook invalidation. Sensitivity labels (Microsoft Purview) are honoured throughout the pipeline.
Four defenses combined. (1) Confidence-thresholded refusal — below threshold the agent refuses rather than fabricates. (2) Citation enforcement — every claim must link to a real chunk ID retrieved during the query; fabricated citations fail server-side validation and the answer is regenerated with an explicit citation-required prompt. (3) Faithfulness scoring on sampled responses via the evaluation harness. (4) User-feedback loop converting negative ratings into new golden eval cases. Combined, these reduce production hallucination rates from typical 12-15% (unmitigated RAG) to under 2% in our deployments.
Different tools for different problems. Copilot and Gemini are general-purpose assistants embedded in productivity apps; they excel at writing, summarising, and tasks where the answer is generative. DECKLOG is a domain-specific retrieval system: the answer comes from your corpus with verifiable citations. They are complementary, not competitive. Many of our clients deploy both — Copilot for productivity, DECKLOG for "what is our actual policy on X" questions where citation-backed correctness matters more than generative fluency.
Starter: 4-6 weeks from contract to first production query. Operate: 8-10 weeks for the multi-source-system rollout + quality dashboard. Sovereign: 12-16 weeks including self-hosted model deployment + tabletop exercises + custom Copilot Studio / Claude Project work. The bespoke DECKLOG Implementation service is the engagement shape; this product page describes the productised SaaS that wraps the same engine.
Webhook-driven invalidation handles the majority case: SharePoint / Drive / Confluence change events update the vector store within minutes. Nightly reconciliation walks the source systems vs the vector store side-by-side and flags drift above threshold. Combined, these catch the failure modes that pure webhook approaches miss (failed delivery, missed events, ACL changes the source system did not announce). The Operate + Sovereign tiers include drift-rate monitoring as a tracked KPI.
Five categories. (1) Retrieval: Recall@K and nDCG@K against the golden set. (2) Generation: faithfulness, completeness, groundedness via LLM-judged scoring. (3) End-to-end quality: human-validated sample of answers per quarter. (4) User signal: thumbs-up/down + free-text feedback with negative samples reviewed weekly. (5) Operations: median latency, p95 latency, cost per query, error rate. Dashboards integrate with your existing Grafana / Datadog / Sentry stacks.
The vector store, the index, the configuration, and the trace history are all in your cloud account from day one. The provider abstraction layer means you can switch generation providers (Anthropic ↔ OpenAI ↔ self-hosted) without a rebuild. If you cancel, the integration code remains yours under a permissive internal-use license. The corpus stays where it always was — in your source systems. See DECKLOG Implementation for the bespoke variant if you want the deployment owned outright rather than consumed as SaaS.
30-minute discovery call. We map your knowledge corpus + tell you whether DECKLOG is the right shape for your top three use cases. No obligation. No high-pressure pitch.
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