Introducing Deeplinq for AML

Platform / Knowledge

Answers with evidence, not guesswork.

Turn files, websites, and spreadsheets into governed knowledge. Deeplinq retrieves only from authorized datasets and returns citations to the passages behind an answer.

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Evidence path

The answer stays connected to source.

Authorized sources converge at retrieval. The response carries bounded citations, not a promise that the model remembered correctly.

Evidence sufficient

  • Policy library

    12 documents

  • Approved website

    84 pages

  • Reference table

    4,820 rows

Response

Cited answer

3 passages attached

What changes

Make private knowledge usable and accountable.

The retrieval path preserves source identity, screens retrieved content, and fails closed when the knowledge capability is unavailable.

  1. 01

    Ground every answer

    Answers cite bounded excerpts from the selected documents. When evidence is insufficient, the agent says so.

  2. 02

    Preserve access rules

    Dataset grants are evaluated for the caller. An agent cannot use a knowledge source its user cannot access.

  3. 03

    Work across formats

    Upload documents, crawl approved websites, and query tabular files through one resilient ingestion pipeline.

Engine evidence

The evidence path stays visible.

Every stage retains the identifiers needed to explain where an answer came from without putting document contents into the audit log.

Control recordVerified
Dataset
Policy library / read access
Ingestion
12 documents ready, 1 processing
Retrieval
Selected datasets only
Screening
Retrieved chunks passed policy
Answer
3 bounded citations attached
Audit
Decision metadata only, no content

Knowledge operations

A pipeline designed to survive real documents.

Ingestion checkpoints each stage, exposes document progress, and can retry a resource without rebuilding the whole dataset.

  • Document, website, and tabular ingestion
  • Content hash change detection for crawled pages
  • Per-resource status and auditable re-ingestion

Memory

Conversations can become durable, sourced context.

Deeplinq can distill conversations into user and project memory. Facts retain provenance and validity windows, and deletion follows the source conversation or project.

  • Native PostgreSQL and Weaviate memory backend
  • User opt-out plus list, add, and delete controls
  • Right-to-forget flow for conversation and project memory

Next step

Put the control plane in front of your first production workflow.

We will map your models, data boundaries, approval points, and evidence requirements in one working session.

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