Zurich — Engineering Consultancy

Forward
Deployment.

We bring precision engineering to your production systems. Modern tooling, rigorous practices, and hands-on deployment expertise — rooted in Zurich.

Our Services ↓ Get in Touch →

Who We Are

Engineering quality,
delivered with precision.

Forward Deployment Zurich is a boutique consultancy built on two convictions: that software quality is non-negotiable, and that modern tooling should be in every engineer's hands. We embed directly with your team to deploy, iterate, and ship.

01 — Principle

Quality is the work itself

Not a phase, not a gate at the end. We write production code with the same care we'd give our own infrastructure. Every deployment pipeline, every architectural call — scrutinised, tested, owned.

02 — Principle

Modern tools, practical choices

We don't chase hype. We evaluate, integrate, and ship with the tooling that actually moves the needle — from model-driven workflows to infrastructure-as-code. If it's battle-tested, it's in our stack.

What We Do

Hands-on engineering,
not slide decks.

Transform

Digitalization

Moving organisations off legacy processes and onto modern digital workflows — system integration, process automation, and custom tooling that actually fits how your team works.

Provision

Infrastructure Design & Deployment

Terraform, IaC, cloud architecture — we design and deploy production-grade infrastructure that scales without turning into a maintenance burden.

Orchestrate

Multi-Agent & Multi-Modal Systems

Architecting multi-agent, multi-modal AI workflows — coordinating LLMs, vision models, and toolchains into production pipelines that do more than demo well.

Analyse

Big Data

Data pipelines, distributed processing, storage architecture — handling data at scale without the usual mess of brittle jobs and mystery failures.

Model

Data Science

Statistical modelling, ML pipelines, experiment tracking — turning data into decisions that hold up under scrutiny, not just in a notebook.

Accelerate

High-Performance Infrastructure

GPU clusters, low-latency networking, compute-optimised environments — infrastructure tuned for workloads that can't afford to wait.

Our Approach

Methodology that respects
your reality.

01

Understand

Deep-dive into your systems, team structure, and constraints. We map the landscape before proposing a path.

02

Embed

Our engineers join your team on the ground. Same tools, same standups, same codebase. Full context, zero friction.

03

Deliver

Iterative deployment with measurable outcomes. We ship working software and leave behind practices that stick.

04

Evolve

Knowledge transfer, documentation, and ongoing support. Your team inherits everything we build, and we stay available.

Selected Work

Two engagements.
Measurable outcomes.

From 340,000 documents to 12-second answers.

Client context

A leading Swiss pharmaceutical company (client name protected under NDA) managing 14 active clinical trials across oncology and rare disease. Their regulatory affairs team was spending 60% of analyst time manually locating and cross-referencing documents across 6 disconnected systems.

Challenge

  • ▪ 11-week regulatory submission cycle
  • ▪ 340,000+ unstructured documents, many scanned PDFs
  • ▪ Swissmedic audit flagged data lineage gaps

Solution

Multi-agent document intelligence platform with four specialised AI agents — OCR, text extraction, classification (~95% accuracy on held-out samples), and entity extraction — orchestrated via LangChain routing with confidence-based human-in-the-loop escalation. Knowledge graph (Neo4j) + vector search (pgvector) for natural-language regulatory queries. Full infrastructure on Azure Switzerland North via Terraform.

18 weeks — 2 engineers full-time, 1 part-time

See full story
342K documents processed
~95% held-out classification accuracy
<12s time-to-find (was ~45 min)
~6–7 wk submission cycle (was 11)

Stack

PythonLangChainTerraformAKSNeo4jpgvectorFastAPIRedis

Service areas

Multi-Agent AIInfrastructure / IaCBig Data

Exhibit 1

Regulatory teams recovered cycle time without weakening review controls.

Measured movement from the legacy search workflow to the governed production assistant. Values are rounded to avoid implying precision beyond the NDA-safe sample.

Preparation cycle
11 wk → ~6–7 wk
Two filing windows after rollout
Median retrieval
~45 min → <12 sec
Cross-system regulatory queries

Filing package preparation

about 40% shorter

Normalized index

Legacy process

11 weeks

100

Production workflow

~6–7 weeks

60

Cross-system document retrieval

same-session answerability

Normalized index

Manual search

~45 minutes

100

Audited query path

<12 seconds

8

Source Engagement run logs and two filing-window retrospectives; anonymised and normalised for publication.

Control Low-confidence classifications remained in the human review queue; the exhibit excludes exploratory prompt tests.

Legacy risk platform to real-time Azure analytics.

Client context

A major Swiss re-insurance group (client name protected under NDA) running legacy on-prem risk analytics that couldn’t scale with growing regulatory demands. Batch jobs ran 14+ hours, meaning analysts waited until the next business day for updated risk positions. FINMA was pressuring the group to improve reporting granularity across its reinsurance book.

Challenge

  • ▪ Batch processing exceeding 14 hours on the legacy platform
  • ▪ Analysts blocked until next day for updated risk positions
  • ▪ FINMA pressure to improve reporting granularity and transparency

Solution

Migrated the analytics stack to Azure (fully Terraform-provisioned infrastructure). Re-engineered the heaviest data pipelines in Python with Polars and pandas, replacing stored procedures that had accumulated over a decade. Built real-time KPI dashboards in Grafana, replacing Excel-based reporting that had been the de-facto standard for underwriting and exposure teams.

22 weeks — 1 senior engineer embedded full-time

See full story
14h → 38min batch processing time
2.4B rows processed daily
~3× dashboard adoption vs. legacy
~CHF 180K annual infra savings

Stack

PythonPolarsAzure Data FactoryTerraformPostgreSQLGrafanaCI/CDDocker

Service areas

Infrastructure / IaCBig DataDigitalization

Exhibit 2

Risk analytics moved from overnight batch dependency to intraday availability.

Primary operating measures from the migration cutover. Adoption is shown as an index because the absolute user count is client-confidential.

Primary batch window
14h → 38min
Equivalent risk aggregation load
Run-rate reduction
~CHF 180K / yr
Rightsizing and reserved capacity

Nightly risk aggregation

intraday reruns feasible

Normalized index

On-prem batch

14 hours

100

Polars pipeline

38 minutes

5

Analyst dashboard usage

tracked as weekly active use

Normalized index

Excel baseline

1.0×

33

Eight weeks post-launch

~3.0×

100

Source Pipeline telemetry, Grafana usage logs, and finance run-rate model; NDA-safe aggregate.

Control FINMA-facing views carried freshness timestamps and pipeline version identifiers after cutover.

Get Started

Ready to deploy forward?

Whether you need embedded engineers, help with model tooling, or want to raise the bar on your engineering practices — we'd like to hear from you.

hello@fdz.ch →

Based in Zurich, working globally.