S.01◉
Applied AI & ML Systems
Custom model pipelines, RAG architectures, and inference infrastructure engineered for production reliability, not demo theatre.
[ Life & Brain GmbH / est. software engineering ]
Software
engineered
like infrastructure.
We build resilient platforms, data systems, and applied AI for teams operating where biology, research, and industry converge. No theatrics — just software that runs, scales, and keeps running.
- Years compounded expertise
- 12+
- Production systems shipped
- 40
- Countries served
- 18
- Aggregate uptime
- 99.98%
01About the Company
An engineering studio for systems that must not fail.
Life & Brain GmbH was founded on the conviction that the most consequential software of the next decade will be built at the borders — between disciplines, industries, and regulatory regimes.
We assemble small, senior teams around a shared operational grammar: measured decisions, transparent tradeoffs, and code you can still read at 2AM. Our work spans genomics platforms, clinical infrastructure, applied AI systems, and the quiet plumbing that keeps modern organizations functional.
Based in Germany, delivering globally, deliberately mid-sized so senior people stay close to the work.
02Our Mission
To make complex systems legible, resilient, and worth trusting.
We believe software should be an instrument of clarity, not a source of it. Our mission is to deliver systems that the organizations depending on them can understand, operate, and evolve — long after our engagement ends.
03Why Choose Us
Six commitments we operationalize on every engagement.
// Senior by default
The people you meet in the pitch are the people who write the code.
// Written architecture
Every non-trivial decision is documented, dated, and reviewable.
// Observability first
You should never learn about production from a customer.
// Reproducibility
Deterministic environments, versioned data, replayable pipelines.
// Regulatory literacy
We speak GDPR, HIPAA, and ISO fluently — as engineers, not lawyers.
// Boring on purpose
We choose the smallest interesting solution that could work.
04Services Overview
Six practices, one engineering standard.
S.02◉
Data Platforms
Warehouses, lakehouses, and streaming pipelines that turn scattered signal into governed, queryable evidence.
S.03◉
Bioinformatics Engineering
Reproducible genomics, imaging, and multi-omics workflows built on modern orchestration and containerized compute.
S.04◉
Product Engineering
Full-stack web platforms, internal tools, and clinical dashboards designed for demanding daily operators.
S.05◉
Cloud & DevOps
Infrastructure as code, zero-downtime delivery, and observability that turns operations into a boring, predictable rhythm.
S.06◉
Security & Compliance
GDPR, HIPAA, and ISO-aligned engineering with auditable trails baked into the architecture from day one.
05Development Process
A six-phase loop, not a waterfall.
- 01Diagnose
Deep discovery with stakeholders, data audits, and system archaeology.
- 02Blueprint
Architecture proposal, RFCs, and a shared vocabulary for the build.
- 03Prototype
Vertical slice in weeks; real code, real data, real feedback loops.
- 04Harden
Testing, observability, security review, and load characterization.
- 05Deliver
Progressive rollout with instrumentation from the first release.
- 06Operate
Handover with runbooks or continued partnership under an SLA.
06Technologies We Use
A curated stack.
We choose tools we can defend, operate, and hand over. No language wars — just fit for purpose.
TypeScriptPythonRustGoReactNext.jsNode.jsPostgreSQLClickHouseKafkaAirflowNextflowPyTorchJAXKubernetesTerraformAWSGCPAzureSnowflakedbtGraphQLElixirRedis
07Industries We Serve
Domains where the details matter.
01
Biotech & Pharma
Lab automation, ELN integrations, discovery platforms.
02
Digital Health
Clinical workflow tools, patient-facing apps, connected devices.
03
Research Institutes
Grant-funded platforms, data commons, reproducibility tooling.
04
MedTech
Device firmware companion apps, regulatory-ready pipelines.
05
Insurance & Finance
Actuarial platforms, underwriting AI, risk observability.
06
Industrial IoT
Edge collection, telemetry, and predictive maintenance.
08Featured Projects
Recent work, characterized.
Genomics Platform
Northlab Variant Explorer
Interactive variant analysis over 40M records with sub-second cohort queries.
Clinical SaaS
Meridian Study Console
Multi-site trial console orchestrating consent, capture, and quality review.
AI Infrastructure
Kairo Inference Mesh
Low-latency inference routing across 6 regions with autoscaled GPU fleets.
Data Platform
Aureus Signal Lake
Unified lakehouse ingesting 22 upstream systems into one queryable model.
09Case Studies
-64%
pipeline latency
Rebuilt a genomics preprocessing pipeline on Nextflow + Kubernetes, cutting end-to-end runtime from 18h to 6.5h per cohort.
×3.4
throughput
Replaced an aging monolith with an event-driven core; ingest throughput tripled while operational cost stayed flat.
0
critical incidents
Introduced SLO-driven observability across an FDA-regulated product line — twelve months incident-free since launch.
10Client Testimonials
“
They act like an internal team with an external perspective. The rigor they brought to our data infrastructure changed how the whole company operates.
“
Precise, calm, and unusually good at translating research constraints into shippable software. A rare combination.
“
We stopped talking about technical debt and started talking about roadmap again. That's the outcome I was hired to produce.
11Team
A small bench of senior operators.
We stay deliberately small so the people writing the RFC are the people writing the deploy. Every engagement is staffed from the same bench.
- Marie FischerManaging Director
- Jonas ReiterHead of Engineering
- Priya AnandLead AI Architect
- Lukas WeberPrincipal Data Engineer
- Elena CostaBioinformatics Lead
- Tomás HerreraHead of Platform
12FAQ
Common questions, answered plainly.
- How do engagements typically start?
- With a short paid discovery — usually one to three weeks — where we co-write a scoped architecture document and a phased delivery plan.
- Do you work under regulated frameworks?
- Yes. GDPR is native to how we operate, and we routinely deliver under HIPAA, ISO 27001, and GxP-adjacent constraints.
- Can you augment an existing team?
- Regularly. We embed for defined missions with clear entry and exit criteria, then hand back a stronger internal team than we found.
- What size of engagement is a good fit?
- From a focused six-week engineering sprint through to multi-year platform partnerships. Our smallest useful unit is a two-person squad.
- Do you use off-the-shelf AI models?
- When they are the right tool. We are equally comfortable fine-tuning, self-hosting, or building custom architectures where the data justifies it.
13Latest Insights
Field Notes
The quiet economics of reproducible pipelines
Why teams that invest in deterministic compute stop firefighting and start compounding.
Architecture
Event sourcing for regulated systems
A pragmatic pattern for products that must remember what happened, not just what is.
AI in Practice
RAG is not a product — it is plumbing
Where retrieval architectures succeed, and where they quietly collapse under real load.
14Partners & Certifications
AWS Advanced
GCP Partner
ISO 27001
GDPR Aligned
HL7 FHIR
SOC 2 Type II
15Contact Information
Start a
conversation.
- Company
- Life & Brain GmbH
- Domain
- lifebrainlab.com
- fischermarie909@gmail.com
- Language
- English