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Corporate Training · EU-hosted · Delivered by practitioners

Empower Your Team: Defensible Data & AI Expertise

Bespoke programmes to advance your strategic capabilities

Train with practitioners building real data and AI solutions — not theory, but battle-tested expertise. Three global programmes — Corporate Agentic AI, Salesforce Agentic, and Corporate Data — each grounded in the EU AI Act, GDPR, BCBS 239, DORA, ISO 42001, and the NIST AI Risk Management Framework. Every engagement is scoped to your organisation, your regulator, and your team.

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70+

Trainings

Scaled for Philips globally

20+

Years

In data-intensive environments

F500

Enterprise

Fortune 500 trust

Why CrawlQ

We’re not just another training vendor. We live at the intersection of AI, data, and regulated industries — and we’ve helped enterprises like Philips, Amazon, ING, and Fortune 500 companies turn their teams into data-confident operators.

We bring 20+ years of hands-on experience in data architecture, governance, and AI systems. We know what your team actually needs — because we have sat in your chair, faced your regulator, and shipped the production patterns this training teaches.

We understand your challenge

  • Your teams use different data language — and it's slowing everything down.
  • You need AI-ready analysts, but generic training won't cut it for your industry.
  • Governance can be an afterthought — it needs to be built into how your people think about data.

You shouldn’t have to choose between speed and data quality. The three programmes below build both — the governance discipline that scales to every regulator, and the operating patterns that scale to every team.

Three programmes. One governance operating model.

Programmes can be engaged independently or as a full enterprise AI enablement journey. Every programme shares the same governance spine — TRACE scoring, grounded generation, and an audit trail by construction — tuned to the organisation, the audience, and the regulator that matters.

Corporate Agentic AI

Design multi-agent AI systems that reason on a brand knowledge graph — graph-of-agents with multi-level reasoning, cross-agent verification, and an audit trail a regulator will accept.

Who this is for

CTOs, Heads of AI, platform engineers, AI governance leads, and compliance officers at banks, insurers, pharma, public sector, and critical infrastructure organisations.

Outcomes

  • Design graph-of-agents topologies with cross-agent verification — the 99.7% accuracy pattern open-sourced in GraQle
  • Apply the TRACE framework to every agent decision — Transparency, Reasoning, Auditability, Compliance, Explainability
  • Build multi-level reasoning pipelines where each agent's output is scored before the next agent consumes it
  • Operate production agent workflows with a Merkle-chain audit trail your legal and compliance teams can inspect on demand
  • Meet EU AI Act Article 52 transparency obligations by architecture rather than by policy
  • Run agents at a fraction of single-model cost through routing, graph grounding, and scored caching

Themes covered

  • Agent fundamentals — reasoning networks, graph-of-agents topology, multi-level reasoning, where single-model agents hit the ceiling
  • Governance gates — TRACE scoring, multi-agent verification, BCBS 239 risk data aggregation applied to AI outputs
  • Enterprise integration — agent-to-CRM, agent-to-ERP, agent-to-data-platform patterns with full decision logging
  • Deployment and compliance — EU AI Act conformity assessment, GDPR Article 22 automated-decision safeguards, publishing the audit trail
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Salesforce Agentic

Turn Salesforce into a governed agentic platform — Agentforce, Data Cloud, and Einstein grounded in a brand knowledge graph your regulator will accept.

Who this is for

Salesforce architects, RevOps leaders, CRM governance owners, and regulated-industry teams running Sales Cloud, Service Cloud, Marketing Cloud, or Agentforce.

Outcomes

  • Design Agentforce topologies that honour EU data residency and GDPR processor obligations
  • Ground every Einstein response in a brand knowledge graph rather than the public internet
  • Apply TRACE scoring to Salesforce AI decisions before they reach a customer
  • Wire Salesforce Data Cloud into a BCBS 239-compliant data-lineage model for regulated industries
  • Avoid the four Salesforce-agentic anti-patterns: hallucinated pipeline facts, voice drift across reps, consent-scope leakage, and audit-trail gaps

Themes covered

  • Agentforce architecture, the Einstein Trust Layer, Data Cloud — where they protect you and where they don't
  • Grounded generation — wiring a brand knowledge graph into Salesforce, scoring outputs on the BRAND Score's five dimensions
  • Regulated-industry patterns — financial services (BCBS 239, DORA), healthcare (EHDS, MDR), consent-scope enforcement, audit export to regulators
  • Operational integration — hand-off to human review, Agentforce monitoring, incident response within DORA classification rules
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Corporate Data

Enterprise data governance, lineage, and regulatory reporting — from BCBS 239 risk data aggregation to the EU AI Act's Article 10 data-governance requirements.

Who this is for

Chief Data Officers, data stewards, regulatory reporting teams, AI governance committees, boards, and risk committees.

Outcomes

  • Implement BCBS 239 risk data aggregation principles in a live data platform — data lineage the supervisor will accept
  • Build data lineage that satisfies both GDPR Article 30 records-of-processing and EU AI Act Article 10 data-governance
  • Operate a DORA-ready data-and-AI operational resilience programme
  • Align an ISO 42001 AI management system with an existing ISO 27001 / 27701 deployment
  • Produce board-level ESG / CSRD-ready disclosures on AI and data use the auditor will accept

Themes covered

  • Data governance foundations — ownership, stewardship, lineage, and the six data-quality dimensions (accuracy, completeness, consistency, timeliness, validity, uniqueness)
  • BCBS 239 in practice — the 14 principles, risk data aggregation capabilities, and board-level reporting
  • GDPR + EU AI Act alignment — Article 10 data-governance, Article 30 records, Article 22 automated decisions
  • DORA operational resilience + ISO 42001 AI management system
  • CSRD, ESG, and board-ready AI disclosure — producing the evidence a regulator and an assurance auditor will accept
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Seven regulatory pillars, one coherent programme

Every enterprise AI programme in the EU has to satisfy multiple regulatory regimes in parallel. Our training maps each pillar to specific architectural patterns — data residency, scored outputs, audit trails — so one governance model answers all of them simultaneously. Below is the regulatory coverage included across every programme.

EU AI Act

Enforcement begins August 2026. Most high-risk obligations phase in by August 2027. Article 10 (data governance), Article 13 (transparency), Article 14 (human oversight), Article 15 (accuracy, robustness, cybersecurity), and Article 52 (transparency for general-purpose systems) are the load-bearing sections. We train teams to meet the obligations by architecture — data residency, auditability, scored outputs — not by policy alone.

GDPR

Article 5 (principles), Article 6 (lawful basis), Article 22 (automated individual decision-making), Article 30 (records of processing), Article 35 (DPIA), and Article 44 (third-country transfers) are the clauses that determine whether an AI programme ships or stalls. We cover consent-scope enforcement, purpose limitation in training-data pipelines, and the DPIA patterns that pre-clear AI use cases with supervisory authorities.

BCBS 239

The Basel Committee's 14 principles for effective risk data aggregation and risk reporting apply to global systemically important banks and, increasingly, national SIBs and insurers under Solvency II. We translate each principle into data-lineage, scored-output, and board-reporting practice — and demonstrate where an agentic AI layer either satisfies or violates each principle.

DORA

The Digital Operational Resilience Act applies from January 2025 across EU financial services. ICT risk management, ICT incident classification, digital operational resilience testing, and ICT third-party risk are the four pillars. AI agents introduced into a regulated financial workflow fall under DORA — we train teams to build a compliant ICT register, run tabletop exercises, and classify incidents correctly the first time.

ISO 42001

The first ISO management system standard for artificial intelligence. Covers AI policy, risk and impact assessment, AI lifecycle controls, third-party relationships, and continuous improvement. We run organisations through the clause-by-clause gap analysis and hand them an implementation plan that aligns ISO 42001 with existing ISO 27001 / 27701 deployments.

NIST AI RMF

The US NIST AI Risk Management Framework (and its Generative AI profile) is the de-facto standard for US federal vendors and increasingly cited in EU procurement. We map NIST AI RMF functions (Govern, Map, Measure, Manage) onto EU AI Act articles and ISO 42001 clauses so one audit programme covers all three.

CSRD & ESG

The Corporate Sustainability Reporting Directive requires large EU companies to disclose AI and digital transformation impact under ESRS. We teach teams to produce board-ready disclosures on AI footprint (compute energy, data sourcing), AI governance maturity, and AI-assisted decision volume — evidence a CSRD assurance auditor will accept.

Built by someone who has sat in your chair

This programme is led by Harish Kumar and the Quantamix training team. Harish spent a decade inside Philips Personal Health as an enterprise AI implementation architect, shipped the reference governance architecture now referenced in the Philips case study, and has delivered AI enablement programmes across banking, pharma, public sector, and critical infrastructure over the past 20+ years.

The training is grounded in production experience, not academic theory. When a module covers BCBS 239 risk data aggregation, the worked example is an agent that actually went through a supervisory review. When a module covers EU AI Act Article 10 data governance, the worked example is a pipeline that actually cleared a DPIA. Participants leave with patterns that are survivable, not just defensible.

  • 20+ years of hands-on experience in data architecture, governance, and AI systems across Philips, Amazon, ING, and Fortune 500 engagements
  • 70+ enterprise training programmes delivered and scaled for Philips globally
  • Philips Personal Health reference architecture: content cycle consolidated from 20h to 5h per product line, €500K saved in year one, 95% brand compliance at scale
  • 2,800+ users on CrawlQ Studio — 4.4★ Capterra rating
  • Patent EP 26162901.8 — brand knowledge graph reasoning method
  • Open-source on PyPI — GraQle (2,009 tests, 201 skills), multi-agent verification at 99.7% accuracy
  • EU-central-1 (Frankfurt) by architecture — data residency and processing jurisdiction both in the European Union
  • Tested across four regulatory domains — banking, pharma, public sector, critical infrastructure

Let’s get started

Step 1

Schedule a Call

Book a scoping call on Calendly with the practitioner who will deliver your programme. No sales intermediary. We confirm fit before any commercial discussion.

Step 2

Confirm Details

Finalise the programme — audience, industry context, regulatory focus, location, and cadence. Agenda is tailored to your stack; the governance frame is consistent.

Step 3

Launch Training

Delivered on-premises, at Quantamix's Amsterdam base, or via EU-hosted virtual classrooms. Every engagement produces scored artefacts — governance models, data-lineage diagrams, TRACE-scored decisions — participants take home.

Ready to elevate your team’s capabilities?

A scoping call with the practitioner who will deliver your programme.

No sales intermediary. We confirm regulatory scope, agenda, cohort, and logistics on the call. If we are not the right fit for the engagement we will tell you on the same call and point you to a team who is.

Book on CalendlyLearn about CrawlQ Studio

Calendar link: https://calendly.com/crawlq-ai-demo/book-a-demo-call

Frequently asked questions

Who delivers Corporate AI Training?

Every engagement is delivered by Harish Kumar (founder, CrawlQ / Quantamix Solutions) and the Quantamix training team. Harish has 20+ years of hands-on experience in data architecture, governance, and AI systems — including a decade as lead implementation architect at Philips Personal Health where the reference governance architecture consolidated the content cycle from 20h to 5h per product line and saved €500K in year one. Every trainer is a practitioner building real data and AI solutions, not a career educator.

How is this programme scoped and priced?

Every engagement begins with a scoping call. Scope, duration, audience, and format are tailored to the organisation — cohort size, in-person vs virtual, single programme vs multi-wave enablement, language, industry context, and regulatory focus all shape the engagement. Pricing follows scope. Book the scoping call directly on the founder's calendar at https://calendly.com/crawlq-ai-demo/book-a-demo-call.

Where is the training delivered, and is it EU-compliant by default?

Delivery runs on-premises at the customer site within the EU, at Quantamix's Amsterdam base, or via EU-hosted virtual classrooms (AWS eu-central-1 / Frankfurt). All training materials, sandboxed labs, and participant data stay within the European Union by architecture. For regulated industries this satisfies data-residency obligations under GDPR Article 44 and the EU AI Act's data-governance expectations without separate assurance work.

How is this different from generic AI bootcamps or vendor-led training?

Generic bootcamps teach prompting and model APIs. Vendor-led training teaches a specific product. Our training teaches a governance-first operating model — the TRACE framework (Transparency, Reasoning, Auditability, Compliance, Explainability), multi-agent verification, scored outputs, and regulatory alignment — applicable across every AI tool a team will use for the next decade. Vendors change; governance patterns compound.

What does graph-of-agents with multi-level reasoning cover?

The Corporate Agentic AI programme teaches graph-of-agents as the structural pattern for reliable AI — each agent is a node in a reasoning graph, cross-agent verification catches hallucinations, and scored outputs flow up through reasoning levels rather than down through a single chain. Multi-level reasoning means the graph can reason about its own conclusions, retract weak claims, and escalate to human review when confidence drops. The pattern is implemented in GraQle (open-source on PyPI) at 99.7% accuracy on governance questions.

How does the training cover BCBS 239 specifically?

BCBS 239 is covered in depth in the Corporate Data programme and surfaces again in the Corporate Agentic AI governance module. We walk through each of the 14 principles — data aggregation capabilities, risk reporting practices, supervisory review and tools — and demonstrate how an agentic AI layer either satisfies the principle (grounded generation, audit trail, multi-agent verification) or violates it (single-model hallucination, missing lineage, unreviewed decisions). Participants leave with a BCBS 239 self-assessment tailored to their own AI stack.

Can the programme be customised to our industry or regulatory regime?

Yes. Every engagement begins with a scoping call where we confirm your primary regulators (EBA, ESMA, EIOPA, EMA, ENISA, national DPAs), your AI use cases, and your existing ISO/SOC posture. Agendas are then tuned — an insurance engagement emphasises Solvency II + DORA + EU AI Act; a pharma engagement emphasises EMA AI guidelines + EU AI Act high-risk classification + GxP; a bank engagement emphasises BCBS 239 + DORA + MiFID II. The governance frame is consistent; the examples are specific to your stack.

Do you provide certification on completion?

Yes. Every programme carries a CrawlQ / Quantamix practitioner certification recognised by our enterprise client network. Certifications reference the TRACE framework and the specific regulatory domains covered. The certification is evidence-backed — participants ship a scored artefact (a governance model, a data-lineage diagram, a set of TRACE-scored agent decisions) and the certificate references the artefact.

How do I book a scoping call?

Book a scoping call directly on the CrawlQ founder's calendar at https://calendly.com/crawlq-ai-demo/book-a-demo-call. You will speak with the trainer who will deliver your programme — not a sales intermediary. We confirm fit, agenda, and logistics before any commercial discussion.

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