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AI Innovation

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We help regulated organisations ship trustworthy, European-made AI with measurable ROI and audit-ready compliance. Operated by Lawkraft.

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1) At a glance

  • Who we serve: law firms, insurers, banks/fintech, public sector.
  • Typical goals: faster research & review, better customer service, lower operational cost, stronger governance.
  • Time to value: pilot in 6–8 weeks; audit artefacts in 1–2 weeks thereafter.
  • Delivery style: small senior team; partner-led; documentation and handover included.

Outcomes we aim for (benchmarks from comparable EU projects): - 70% faster contract review cycles
- 94% reduction in claim-triage handling time
- €10M/year lift from AI-assisted stock optimisation
Your mileage will vary — we estimate benefits conservatively before build.

2) Why choose us (non-tech summary with the right tech under the hood)

  • Strategy that reads like a business plan. We prioritise use-cases by ROI, risk, and feasibility, then stage investments.
  • Compliance you can pass. EU AI Act alignment from day one — classification, DPIA, technical file, documentation.
  • Engineering that survives production. MLOps, observability, guardrails, and rollback paths so pilots don’t stall.
  • Citations over vibes. LexGraph RAG blends a knowledge graph with hybrid retrieval (semantic + sparse + graph traversal) so answers are grounded and explainable — with sources.

LexGraph retrieval flow

Graph + hybrid retrieval (vector ⊕ sparse ⊕ graph traversal) → rerank → generate with guardrails → citations.

3) Founder & lead (credentials you can audit)

David Sanker is a lawyer and technology executive focused on AI/ML, legal tech, and financial services. He’s licensed in Germany and registered in New York (foreign counsel), combining corporate law-firm experience with hands-on engineering.

Recent roles & impact (selected):
- Legal AI Engineer, Cleary Gottlieb LLP (NY, 2024–present): built internal Graph-RAG for legal research and drafting across 100k+ documents; automated prospectus generation aligned to EU financial regulations; shipped OSINT crawler for antitrust litigation with ~99% workload reduction.
- Director of Innovation & General Counsel, Fintech Group (Malta, 2023–2024): launched legal/financial tech platforms generating thousands of leads in weeks; automated startup/investment evaluation to 5× decision throughput.
- CEO/CIO, Legal Tech GmbH (Cologne/Berlin, 2016–2022): scaled access-to-justice applications; led product/market expansion.
- Partner, Hucke & Sanker (Cologne/New York, 2014–present): capital markets, international IP, transnational criminal law, taxation; led forensic digital transformation initiatives.

Education & credentials:
- Law (First State Exam), University of Cologne; CUSL — Law of the United States; Clerkship & Second State Exam (NRW, Cologne District Court).
- UNICRI (Transnational Law & Justice); MIT Sloan (Blockchain Law & Applications); specialist solicitor certifications in Criminal Law and Corporate/M&A.
- Admissions: Federal Bar (Germany); New York State (foreign legal counsel). Languages: German (native), English (fluent).

Projects & IP (selected):
- “Mother” OS for law firms (Python; knowledge graphs, CRM/ERP, automations) · bellsfall.com (AI financial predictions) · legal 6 fintech group (prospectus automation, OSINT valuation) · morpheusmark.com (blockchain-enabled AI counterfeit detection) · cvfy.ninja (AI employment matching agent).

4) What you get (deliverables checklist)

Area Deliverable What it means to you
Strategy Use-case heat-map & ROI model Prioritised roadmap and conservative value estimate
Governance Risk register & policy kit Clear rules for data, prompts, model usage
Compliance EU AI Act classification + technical file Evidence pack for internal/external review
Privacy DPIA & data-flow diagrams GDPR alignment and accountable processing
Engineering Pilot (RAG/Graph) with citations A working assistant/case search on your data
Platform Runbooks, dashboards, logs Operable service with SLIs/SLOs and rollback
Handover Training + documentation Your teams can run and extend the system

5) How it works (step-by-step)

90-day roadmap

Discover (2 wks) → Build (6–8 wks) → Govern (1–2 wks) → Scale (4 wks). Deliverables ship continuously.

Phase A — Discover (≈ 2 weeks)

  • Stakeholder interviews; data inventory; risk appetite.
  • Use-case heat-map; cost/benefit model; prioritised backlog.
  • Go/No-Go after a partner-led review.

Phase B — Build (≈ 6–8 weeks)

  • LexGraph RAG pilot: domain knowledge graph + hybrid retrieval (semantic + sparse + graph traversal) + citations.
  • Integrations (DMS, CRM, matter systems, policy portals) via APIs.
  • Evaluations: retrieval precision@k, MRR; groundedness; latency; cost per answer.
  • Guardrails: PII filters, prompt policy checks, red-team probes.

Phase C — Govern (≈ 1–2 weeks)

  • EU AI Act technical file: intended purpose, data governance, evaluations, risk controls, monitoring.
  • DPIA, model cards, eval cards, runbooks, and monitoring plan.

Phase D — Scale (≈ 4 weeks, optional)

  • Kubernetes inference, autoscaling, canary releases, drift detection.
  • SSO, mTLS, secrets, audit trails, observability dashboards.
  • Handover/training; support options if desired.

6) Architecture & model choices (non-exclusive)

Platform & MLOps

Kubernetes-native inference with guardrails, observability (metrics/logs/traces), SSO/OIDC, and drift/cost monitoring.
  • EU cloud (managed) — fastest to start, compliant regions, cost-efficient.
  • On-prem / private cloud — full control; strict data residency.
  • Hybrid — sensitive data on-prem; stateless compute in EU cloud.

Model options (vendor-neutral):
Open-source (Llama, Mistral) · EU vendors (e.g., Aleph Alpha) · hyperscalers when latency or specialised models are needed.
We design an abstraction layer so you can swap models later without rewriting your apps.

7) Security & privacy baseline

  • Network: zero-trust; mTLS; least privilege; IP allow-lists.
  • Data: encryption in transit/at rest; retention policies; no shadow copies.
  • Secrets: vault-managed; never in code/CI logs.
  • Access: SSO/SAML/OIDC; role-based access; admin break-glass.
  • Logging: immutable audit logs; prompt/response trails; redaction as required.
  • Change control: code reviews, CI checks, controlled releases, rollbacks.
  • Monitoring: traces, metrics, logs; alerts on drift, cost spikes, failures.

8) Quality & evaluations (how we prove it works)

  • Retrieval: precision@k, recall, MRR (mean reciprocal rank) on labelled sets.
  • Generation: factuality, faithfulness, citation coverage; human spot-checks.
  • Safety: PII/PHI protection, jailbreak resistance, bias probes.
  • Ops: latency (TP90/TP95), error rate, throughput, cost per answer.
    We set targets (SLOs) up front, and track them after go-live.

9) Data & integrations

Common systems:
- Legal — DMS, matter management, eDiscovery repositories
- Insurance/Banking — claims, policy DBs, CRM, knowledge bases
- Search & Content — Elasticsearch/OpenSearch, SharePoint, Confluence
- Warehouses/Lakes — Postgres, BigQuery, S3-compatible stores

Data stays in your environment. We avoid moving data unless required for processing, and delete transient copies after use.

10) 90-day roadmap (example)

Days 1–14 — discovery, data inventory, risk/governance framing
Days 15–56 — pilot build: LexGraph RAG, connectors, guardrails, evaluations
Days 57–70 — technical file, DPIA, documentation
Days 71–90 (optional) — scale-out: SSO, SLOs, autoscaling, drift monitoring

Deliverables ship continuously; you don’t wait until the end.

11) Case snapshots (anonymised)

  • Litigation boutique (EU): LexGraph RAG for case law + internal memos → ~60% faster research; adoption driven by citations & provenance.
  • Retail insurer: claim triage assistant with policy DB + CRM → reduced manual routing and cycle time; explanation UI for disputes.
  • Bank (EU AI Act readiness): risk classification, DPIA, technical file → unblocked a customer-support copilot from pilot to controlled rollout.

References on request; NDAs respected.

12) EU AI Act mapping (high-level)

EU AI Act — Technical File Checklist

What auditors expect: intended purpose, risk class, governance & evidence; post-market monitoring after go-live.
AI Act theme What we provide
Intended purpose & use Written scope, boundaries, de-scoping of non-goals
Data & governance Source inventory, quality checks, lineage, retention
Risk management Risk register, mitigations, acceptance criteria
Technical documentation (file) Architecture, components, model info, evaluations, monitoring
Transparency User notices, capabilities/limitations, escalation paths
Human oversight Review flows, approvals, override/stop procedures
Post-market monitoring Metrics, alerts, incident handling, change logs

Depth is tailored to your risk class and standards.

13) Glossary (plain language, minimal jargon)

  • RAG (Retrieval-Augmented Generation): looks up relevant documents and then drafts an answer, usually with citations.
  • Knowledge graph: a structured map of entities (people, cases, clauses) and relationships for more precise retrieval.
  • Hybrid retrieval: combining semantic vectors with keyword/symbolic signals and graph traversal.
  • mTLS (mutual TLS): both client and server prove identity; blocks impostors.
  • DPIA: Data Protection Impact Assessment — required for high-risk processing.
  • SLO: Service Level Objective — the reliability/latency target you hold the system to.

14) Start here

Operated by Lawkraft and Hucke & Sanker Lawfirm. Founder details (roles, education, admissions, languages, projects) verified by the owner.