Build systems
We think beyond isolated features. We build systems that scale, with boundaries that hold when the requirements change.
AI / Data / Systems
IntelligenceEngineered.
We build intelligent software, data infrastructure and AI systems for the next generation of businesses.
System onlineSystem 001 — Intelligence infrastructure
Scroll01Position
Complexity
is a systems
problem.
We design the infrastructure that turns complex data into intelligent action.
Most failures we are asked to fix are not model failures. They are boundary failures: unclear ownership, unversioned data and systems nobody can observe.
02Capabilities
The Stack
Five disciplines, one delivery model. We work across the whole path from raw event to production decision.
Operating surface
01
AI
Models applied to problems with a measurable definition of done.
02
Data
Pipelines with contracts, lineage and published grain.
03
ML
Training, evaluation and promotion treated as one workflow.
04
Cloud
Infrastructure defined as code, reproducible per environment.
05
RAG
Hybrid retrieval, re-ranking and grounded generation.
06
MLOps
Versioned models, shadow deploys and rollback by default.
07
Agents
Planners with tools, budgets and observable execution traces.
08
Automation
Deterministic workflows where randomness would be a liability.
03Pipeline
From data
to intelligence
Six stages, each with its own contract. Nothing moves forward until the previous stage is observable.
- 01
Data
Sources, contracts, events
- 02
Ingest
Stream and batch capture
- 03
Process
Validate, conform, enrich
- 04
Model
Train, evaluate, version
- 05
Intelligence
Score, retrieve, reason
- 06
Action
Decisions in production systems
04Case studies
Selected work
Engineering-driven projects — from internal R&D to full-scale client deployments. Each one is marked with its real status.
05Testimonials
What clients
say afterwards.
Quotes from engineering and operations leaders. Client identities are anonymised where required by NDA.
They rebuilt our planning cycle around live data instead of last month's exports. For the first time our operations team and our planners are working from the same numbers, at the same time.
Marta Reinholt
VP Supply Chain Operations · Global retail enterprise
The inspection system matched line speed on day one and never needed a shutdown to install. Our first audit after go-live found nothing — which had never happened before.
Daniel Okafor
Director of Manufacturing Engineering · Electronics manufacturer
The audit trail is the part that convinced our risk team. Every automated decision is explainable, reproducible and reviewable — that is rarer than it should be in this category.
Ingrid Halvorsen
Head of Digital Channels · Top-20 global bank
Patients stopped calling the clinic about results the same week we shipped. The offline behaviour in rural areas was not a nice-to-have for us; it was the whole requirement.
Priya Raghavan
Chief Digital Officer · Regional healthcare network
They treated rendering precision and multiplayer sync as one problem instead of two. We went from a blank repository to a paying product without ever shipping a broken drawing.
Tomas Lindberg
Co-founder & CTO · AEC software startup
06Research
Exploring
what's next.
Open questions we keep returning to. Published work is added here when it is ready to be read, not before.
- 01Generative AI
- 02Agentic Systems
- 03Machine Learning
- 04Data Infrastructure
- 05Real-Time Intelligence
07Philosophy
Engineer for reality
Models are only useful when they work in production. Latency, drift, incomplete data and rollback are design inputs, not afterthoughts.
Simplify complexity
The best technology makes complex problems easier to understand. If a system cannot be explained, it cannot be operated.
Next
Build
what comes
next.
Have a difficult technical problem? Let's build the system behind it.