How do you handle AI hallucinations in production?

Through architecture, not hope. We implement RAG (Retrieval-Augmented Generation) to ground outputs in your verified data, add validation layers, human-in-the-loop checkpoints, and observability tooling to monitor accuracy over time.

How do we measure ROI on GenAI projects?

We define measurable KPIs before building anything: hours saved, error rates reduced, throughput increased. Our POC phase includes baseline measurement so you can quantify impact from week one.

Do you work with a specific AI technology stack?

No. We are technology-agnostic. We select the right LLM provider, orchestration framework, and deployment model based on your enterprise constraints.

How long does a typical GenAI engagement take?

A discovery sprint takes 2–4 weeks. A POC takes 4–8 weeks. Full production hardening depends on scope, but most enterprises see first value within 3 months.

How do you handle AI hallucinations in production?

Through architecture, not hope. We implement RAG (Retrieval-Augmented Generation) to ground outputs in your verified data, add validation layers, human-in-the-loop checkpoints, and observability tooling to monitor accuracy over time.

How do we measure ROI on GenAI projects?

We define measurable KPIs before building anything: hours saved, error rates reduced, throughput increased. Our POC phase includes baseline measurement so you can quantify impact from week one.

Do you work with a specific AI technology stack?

No. We are technology-agnostic. We select the right LLM provider, orchestration framework, and deployment model based on your enterprise constraints.

How long does a typical GenAI engagement take?

A discovery sprint takes 2–4 weeks. A POC takes 4–8 weeks. Full production hardening depends on scope, but most enterprises see first value within 3 months.

Generative AI & Agentic Workflows for Enterprise Operations

Technology-agnostic GenAI architecture that fits your constraints — not the other way around. From LLM integration to production-grade agentic workflows.

MAKR designs and implements generative AI solutions for enterprise operations, with a focus on manufacturing and logistics. Rather than prescribing a single technology stack, Lead Architect Romain Grimmonpré selects the right tools for each client's constraints — from LLM providers like OpenAI and Gemini, to orchestration frameworks, RAG architectures, and workflow engines. MAKR's agentic workflow implementations have automated complex cognitive tasks including document processing, ticket triage, and compliance reporting for enterprises across Europe.

Who This Is For

COOs and CIOs in manufacturing, logistics, and financial services who are stuck with manual cognitive tasks:

Manual ticket triage and routing consuming engineering hours
Document processing still done by hand at scale
Compliance checks and reporting requiring constant manual intervention
AI POCs that never reach production

Use Cases We Deliver

Document Copilots

Automate extraction, classification, and synthesis of unstructured documents — contracts, reports, inspection records — with accuracy you can audit.

Ticket Triage & Routing

Classify, prioritize, and route incoming tickets — support, maintenance, compliance — without human intervention on routine cases.

Operational Assistants

Internal AI assistants grounded in your knowledge base — for field teams, planners, or analysts who need fast, accurate answers from your proprietary data.

Compliance Automation

Automated checks, audit trails, and regulatory report generation — so compliance doesn't require a dedicated analyst for every cycle.

Our Approach

1

Discovery

Map your processes, data, and compliance constraints. Identify the highest-value automation targets.

2

Data & Knowledge Audit

Classify which data can flow through external LLMs, which requires on-premise models, and which stays out entirely.

3

POC

Build a working proof-of-concept with baseline measurement so you can quantify ROI from week one.

4

Hardening & Monitoring

Production-grade deployment with validation layers, observability tooling, and human-in-the-loop where needed.

Technology-Agnostic by Design

We select the right tools for your constraints — not the other way around.

OpenAI Google Gemini Anthropic Claude Azure AI Foundry RAG Pipelines Langtrace On-premise LLMs

How We Handle the Hard Parts

Hallucinations

RAG grounds outputs in your verified data. Validation layers catch errors before they reach users.

Security & Data Privacy

Data classification determines what flows where. Sensitive data stays on-premise.

Compliance

Audit trails, explainability, and human-in-the-loop checkpoints built in from day one.

ROI Measurement

Baseline metrics defined before we build. You see impact from week one.

Frequently Asked Questions

Ready to Move from POC to Production?

Book a free 20-minute discovery call. We'll scope your highest-value automation target and tell you if GenAI is the right tool.

Prefer email? romain.grimmonpre@makr.tech