Applied AI, data & decision systems
Enterprise AI and data systems built to survive operational reality.
Data Crunch Limited designs AI assistants, LLM workflows, usable data layers, agentic automation and decision-support systems for teams that need usable outcomes, control layers and disciplined delivery from day one.
Enterprise AI
Data systems
Analytics / SQL
LLM systems
Agentic automation
NoCode / n8n
Tool orchestration
Decision support
Data & decision systems
The data layer is now visible in every serious AI initiative
Credible AI systems rely on trustworthy data flows, readable metrics and outputs that help teams prioritise, arbitrate and run operations with more control.
Analytics & KPI automation
Automate KPI production, alerts and structured summaries that business teams can act on.
Open KPI demo →SQL copilots & decision support
Turn a business question into queries, executive memos, prioritisation logic and action plans.
Open SQL Copilot →Trusted metrics & semantic layer
Stabilise definitions, shared metrics and BI readiness before dashboard drift starts.
Open analytics engineering item →What the market buys
An offer aligned with what companies actually pay for in 2026
The market is no longer buying a generic “AI chatbot”. It is buying a useful system tied to an operational flow, equipped with guardrails and strong enough to survive real usage.
01
Enterprise knowledge assistants
Internal support copilots, document-heavy workflows and RAG systems built for real teams.
02
LLM workflows, NoCode & AI automation
Document intake, support, enrichment, human review and directly usable low-code operational outputs.
03
Agentic systems & AI tool orchestration
API-first orchestration, knowledge layers, multi-tool routing and multi-step agents with explicit control points.
04
Evaluation & control layers
Quality measurement, business guardrails and explicit readiness criteria for high-accountability use.
05
Analytics copilots & decision support
Analytics copilots, prioritisation, executive memos and action-oriented queries for operators and leadership.
06
Data platform discipline
Quality, ownership, observability, release readiness and governance for enterprise environments.
Go deeper
Two useful pages if you want a faster read of the offer behind the home page
The home page stays intentionally restrained. These two complementary pages make the offer more concrete without making the main surface heavier.
01
Use Cases
A concrete selection of use cases across AI assistants, data systems, automation and decision support.
Open page →02
Engagement Models
The preferred working logic: short, well-framed and directly useful engagements, with fit criteria and expected outputs.
Open page →Market 2026
What recent openings are actually asking for
Synthesis based on signals reviewed on April 20, 2026 across France, Switzerland and the US East Coast, with specific weight on enterprise demand from New York and surrounding markets.
France
- Python, FastAPI, RAG, agents and multimodal APIs.
- Delivery from ideation through production.
- Evaluation and measurable business impact.
- Robust data platforms behind GenAI systems.
Switzerland
- MLOps, Kubernetes, observability and lifecycle management.
- LLMs, RAG, tool integration and secure AI platforms.
- Strong demand for reliability, governance and real business value.
- AI combined with software engineering and data discipline.
USA East
- Agentic RAG, KG-RAG, MCP integrations and enterprise cloud workloads.
- Hands-on production experience with LLM systems and evaluation loops.
- Scalable APIs, Python full-stack and end-to-end technical ownership.
- The expected level is enterprise-grade and control-oriented.
The common thread is straightforward: the market rewards profiles able to combine GenAI, data discipline, automation, engineering discipline, evaluation and business impact.
View sources and market notes →Proof
Visible evidence, not just positioning
The commercial layer is backed by a structured proof surface with use cases, deliverables and technical review paths.
Primary proof surface
Executive Applied AI Showcase
Demonstration hub structured around RAG workflows, SQL copilots, n8n automations, analytics, LLM evaluation, governance and agentic routing.
n8n Lead Enrichment
Business qualification and enrichment workflow with score, next step and CRM payload.
Open demo →Cloud Data Platform Governance
Strong signal for demanding environments: quality, contracts and release readiness.
Open item page →OpenClaw + n8n Agentic Ops
Agent supervision with controlled reasoning, n8n handoffs, approvals and execution traces.
Open agentic console →AI readiness & value-risk
Assess which agentic use cases deserve a pilot, which need controls and which should wait.
Open assessment →Execution
A short, clear, outcome-oriented engagement logic
The right sequence is simple: frame quickly, build just enough, measure, then stabilise what creates defensible value.
Framing
Business need, target flow, data, risks, confidentiality and integration constraints.
Useful prototype
First testable system, first concrete deliverable, first trade-offs and initial evaluation.
Pilot
Business feedback, human-in-the-loop, instrumentation, guardrails and adoption logic.
Stabilisation
Documentation, ownership, governance, monitoring and readiness for the next stage.
Market position
What no longer convinces, and what now matters
An AI website is credible today when it proves constrained delivery, not just model demos.
No longer enough
- a generic chatbot without architecture,
- a GenAI promise with no evaluation frame,
- a demo with no integration or governance logic.
Now decisive
- a precise use case,
- tooled integrations,
- reliability discipline,
- readable deliverables,
- execution that speaks to both technical and business stakeholders.
Contact
If you need to frame an enterprise AI use case, start from the real workflow and the decision it supports.
The most useful starting point is usually a real operational bottleneck, a knowledge-intensive workflow or a decision path worth instrumenting.