Logiciel implements enterprise data observability across pipelines, warehouses, lakehouses and AI workloads. Monte Carlo, Soda, Bigeye, Datadog or open-source patterns, plus the SLAs, lineage and incident response practice around them. We work alongside data platform, analytics and reliability teams to make data incidents visible, fixable and accountable.

Book an Intro Call Common Questions

5 steps

Framework from discovery to operate and improve

3 models

Engagement models for enterprise observability

4 tools

Monte Carlo, Soda, Bigeye, Datadog supported

Why Logiciel

Why Enterprise Data Observability Is Hard to Get Right.

Why Logiciel · 01

Tooling decisions get made before the operating model is agreed.

Why Logiciel · 02

Coverage is patchy, with critical data products outside the observability platform.

Why Logiciel · 03

Alert fatigue takes over within weeks because thresholds were never tuned.

Why Logiciel · 04

Data incidents have no clear owner across data engineering, analytics and product.

Why Logiciel · 05

Lineage stops at the warehouse boundary, missing AI and operational use.

Why Logiciel · 06

The platform reports problems but nobody actually responds.

What you get

What You Get When You Work With Logiciel on Data Observability.

We give enterprise data platform teams an observability practice they can actually run.

A clear definition of what is in scope

including critical data products, AI workloads and operational data flows

An observability platform implementation tuned

to your enterprise stack

SLAs per data product,

with freshness, volume, schema and quality thresholds tied to business impact

Lineage

across pipelines, warehouses, lakehouses and AI workloads

An incident response practice

with named owners across data engineering, analytics and product

A documented operating model

with reviews, dashboards and KPIs

What we build

Enterprise Data Observability Solutions Built for Production.

Data Observability Platform Implementation

Implementation of Monte Carlo, Soda, Bigeye, Datadog or open-source observability platforms, tuned to your stack.

Data Product SLAs

SLA design per critical data product, including freshness, volume, schema and quality thresholds tied to business impact.

Pipeline and Warehouse Observability

Observability across pipelines, warehouses and lakehouses on Snowflake, Databricks, Redshift, BigQuery and lakehouse architectures.

Streaming and CDC Observability

Observability for streaming and CDC pipelines on Kafka, Kinesis, MSK, Pub/Sub and Flink.

AI Workload Observability

Observability for AI workloads, including data and feature inputs, retrieval quality and downstream model behaviour.

Lineage and Impact Analysis

End-to-end lineage from source to consumption, including AI workloads, with impact analysis for incidents and changes.

Data Incident Response

Incident response practice with named owners, runbooks, post-mortems and KPIs across data engineering, analytics and product.

Engagement

Engagement Models Designed for Data Observability Solutions for Enterprise Delivery.

Dedicated Data Observability Squad

A long-running team of data engineers, reliability engineers and platform engineers embedded in your data platform function.

Engagement

Data Observability Advisory and Staff Augmentation

Senior data reliability engineers who reinforce your in-house team during specific phases.

Engagement

Outcome-Based Observability Engagements

Fixed-scope work, for example an observability platform implementation, a data SLA rollout or an incident response practice setup.

Engagement

Under the hood

Enterprise Data Observability Services We Deliver.

Data Observability Platform Implementation

What it meansImplementation of Monte Carlo, Soda, Bigeye, Datadog or open-source observability platforms.

Data Product SLA Design

What it meansSLAs per critical data product, with thresholds tied to business impact.

Pipeline, Warehouse and Lakehouse Observability

What it meansObservability across pipelines, warehouses and lakehouses on Snowflake, Databricks, Redshift, BigQuery and lakehouse architectures.

Streaming and CDC Observability

What it meansObservability for streaming and CDC pipelines on Kafka, Kinesis, MSK, Pub/Sub and Flink.

AI Workload Observability

What it meansObservability for AI workloads, including data and feature inputs, retrieval quality and downstream model behaviour.

Lineage and Impact Analysis

What it meansEnd-to-end lineage from source to consumption, including AI workloads, with impact analysis.

Data Incident Response Practice

What it meansIncident response practice with named owners, runbooks, post-mortems and KPIs.

Data Observability Operating Model

What it meansRoles, processes, cadences and KPIs for an enterprise data observability practice.

What we build

Data Observability Solutions for Enterprise Insights & Frameworks.

Patterns from our delivery teams that have run through real enterprise deployments.

↳ What we build

Enterprise Data Observability Operating Model

A reference for roles, processes, cadences and KPIs for an enterprise data observability practice.

↳ What we build

Data Product SLA Framework

A practical framework for SLA design per data product, with thresholds tied to business impact.

↳ What we build

What we build

Our Data Observability Solutions for Enterprise Framework.

Discovery and Scoping

We map data products, AI workloads, operational data flows and current observability gaps.

Operating Model and SLA Design

We design the operating model, SLAs per data product and incident response practice.

Platform Implementation

We implement the observability platform, integrate with pipelines and warehouses and tune alerts.

Rollout and Incident Practice

We roll out across data products, establish on-call and run the first incident reviews.

Operate and Improve

We move into a steady-state operating model with reviews, dashboards and KPIs.

Selected work

Tailored engineering for your industry.

Zeme · Real Estate Cut development costs 50% and launched 3× faster with dedicated dev teams.

Real Estate

Cut development costs 50% and launched 3× faster with dedicated dev teams.

Leap · Construction Scaled to 7-figure ARR with AI-augmented software teams.

Construction

Scaled to 7-figure ARR with AI-augmented software teams.

KW · Real Estate 56M+ workflows automated, saving agents 30% time with AI-powered tasks.

Real Estate

56M+ workflows automated, saving agents 30% time with AI-powered tasks.

Resources

Insights for tech leaders.

Framework · PDF

Bad Data Costs $12.9m A Year. Reverse ETL Syncs It Into The CRM.

Whitepaper · PDF

Building AI-Ready Healthcare Data Infrastructure: The 90-Day Roadmap from Staging Bottleneck to Production

Whitepaper · PDF

Building AI-Ready Data Foundations in Healthcare

In their words

What our clients say.

Teams that needed to ship fast, and did. Here's what partnering with Logiciel felt like from the inside.

I would highly recommend them to anyone looking to scale quickly or needing support in engineering, product, or QA.

Patrick Fingles

CEO, Leap

We don't just call them Logiciel; they're part of the Zeme team. Within the first week they were contributing meaningfully to our codebase.

Elior Alayev

Founder & CEO, Zeme

The Logiciel team worked tirelessly and built everything we needed, with security and best practices across our entire platform. It allowed us to become #1 in our industry, and we couldn't have done it without them.

David Buzzelli

Co-Founder, JobProgress

Questions

Frequently asked questions.

What does Data Observability Solutions for Enterprise include?

We cover strategy, architecture, build, deployment and operations for Data Observability Solutions for Enterprise, aligned with your business priorities and operating constraints.

How long does Data Observability Solutions for Enterprise typically take?

Most engagements reach a working pilot within 4-8 weeks, while larger rollouts run across phased waves over several months.

Can Logiciel integrate Data Observability Solutions for Enterprise with our existing systems?

Yes. We integrate with cloud platforms, CRMs, ERPs, EHR, OT systems, analytics tools and other operational infrastructure depending on the use case.

Do you offer fixed-cost engagements for Data Observability Solutions for Enterprise?

Yes. We offer milestone-based pricing once scope, KPIs and delivery requirements are agreed.

Who owns the deliverables from a Data Observability Solutions for Enterprise engagement?

You retain ownership of all workflows, integrations, prompts, infrastructure, systems and implementation assets.

How do you handle governance and compliance for Data Observability Solutions for Enterprise?

We implement governance frameworks, observability, access controls, audit trails and compliance-aligned deployment practices.

How do you optimize cost for Data Observability Solutions for Enterprise?

We tune infrastructure, automate resource management, optimise deployment workflows and report operational cost back to teams and product lines.

Do you support ongoing operations after launch for Data Observability Solutions for Enterprise?

Yes. We run managed operations with SRE, observability, on-call and continuous improvement.

Let's build

Accelerate Data Observability Solutions for Enterprise.

Ready to treat Data Observability Solutions for Enterprise as production engineering instead of a side project? Partner with Logiciel to design, build and operate Data Observability Solutions for Enterprise that engineering, security and business teams can all defend.

Plan your data observability programme

Data Observability Solutions for Enterprise.

See Logiciel in action.

Why Enterprise Data Observability Is Hard to Get Right.

Tooling decisions get made before the operating model is agreed.

Coverage is patchy, with critical data products outside the observability platform.

Alert fatigue takes over within weeks because thresholds were never tuned.

Data incidents have no clear owner across data engineering, analytics and product.

Lineage stops at the warehouse boundary, missing AI and operational use.

The platform reports problems but nobody actually responds.

What You Get When You Work With Logiciel on Data Observability.

A clear definition of what is in scope

An observability platform implementation tuned

SLAs per data product,

Lineage

An incident response practice

A documented operating model

Enterprise Data Observability Solutions Built for Production.

Data Observability Platform Implementation

Data Product SLAs

Pipeline and Warehouse Observability

Streaming and CDC Observability

AI Workload Observability

Lineage and Impact Analysis

Data Incident Response

Engagement Models Designed for Data Observability Solutions for Enterprise Delivery.

Dedicated Data Observability Squad

Data Observability Advisory and Staff Augmentation

Outcome-Based Observability Engagements

Enterprise Data Observability Services We Deliver.

Data Observability Platform Implementation

Data Product SLA Design

Pipeline, Warehouse and Lakehouse Observability

Streaming and CDC Observability

AI Workload Observability

Lineage and Impact Analysis

Data Incident Response Practice

Data Observability Operating Model

Data Observability Solutions for Enterprise Insights & Frameworks.

Enterprise Data Observability Operating Model

Data Product SLA Framework

Our Data Observability Solutions for Enterprise Framework.

Discovery and Scoping

Operating Model and SLA Design

Platform Implementation

Rollout and Incident Practice

Operate and Improve

Tailored engineering for your industry.

Cut development costs 50% and launched 3× faster with dedicated dev teams.

Scaled to 7-figure ARR with AI-augmented software teams.

56M+ workflows automated, saving agents 30% time with AI-powered tasks.

Insights for tech leaders.

Bad Data Costs $12.9m A Year. Reverse ETL Syncs It Into The CRM.

Building AI-Ready Healthcare Data Infrastructure: The 90-Day Roadmap from Staging Bottleneck to Production

Building AI-Ready Data Foundations in Healthcare

What our clients say.

Frequently asked questions.

Accelerate Data Observability Solutions for Enterprise.