The infrastructure under your AI features — built for production, not demos.
We design and ship LLM applications, RAG pipelines, ML platforms, and predictive systems — with the engineering rigor your business actually needs.
What we build
Four practice areas
Specialists in each layer of the AI stack — from raw data to deployed model to business outcome.
Generative AI & LLM engineering
We design and deploy LLM-powered applications — RAG pipelines, AI agents, function-calling workflows, and multi-modal systems — and handle prompt engineering, context management, cost optimization, and production hardening.
RAG & knowledge systems
We build retrieval-augmented generation systems that make your private data queryable. Vector search, hybrid retrieval, chunking strategies, and re-ranking — tuned for accuracy and latency in production.
ML platform & AI governance
We stand up the infrastructure teams need to move fast without breaking models in production. Feature stores, experiment tracking, model registries, CI/CD for ML, drift monitoring, and bias/compliance guardrails.
Predictive ML & recommendations
We build structured ML pipelines for forecasting, anomaly detection, churn prediction, and recommendation engines, end to end: data prep, training, evaluation, deployment, and scheduled retraining.
How it fits together
From where most teams start to what we build
Typical starting point
Model
Works in a notebook
not yet in production
Data
Manual exports
trained on a one-off snapshot
Production
No evaluation
nobody sees it drift
What we build
Data
Feature pipelines
scheduled · versioned
Training
Automated retraining
MLflow · Kubeflow
Serving
Model serving
Kubernetes · GPU
Evaluation
Evals and guardrails
Drift and cost monitoring
From a model that works on a laptop to one that runs in production: fed by pipelines, retrained on a schedule, served on Kubernetes, and watched for drift and cost. The Volvo case shows the pipelines, the retraining, and the Kubernetes deployment in practice.
Case studies
Work that shipped
Selected projects from our AI practice — real results, anonymized clients.
Client
Food delivery platform
Scope
Production ML: forecasting, recommendations, classification
Took forecasting, recommendation, and product-classification models from prototype to production across 20+ city markets: preparation-time and order-volume forecasting, NLP classification of 200k+ products that ended manual taxonomy upkeep, and collaborative-filtering recommendations — with automated retraining and rollback.
Client
Global media & entertainment company
Scope
AI-assisted metadata platform, 1B+ records
Unified Apple, Spotify, YouTube, and TikTok metadata into one Databricks lakehouse of 1B+ records, with automated data-quality checks and a natural-language query layer, so marketing, rights, and analytics teams answer their own questions.
Client
Enterprise IT client
Scope
ML incident triage in its ITSM workflow
Built a deep-learning classifier (transfer learning) that categorizes incoming incidents and returns the category to the existing ITSM workflow over a REST API, with no change to the platform's UI. Accuracy cleared the client's UAT threshold, and a retraining pipeline keeps it there as ticket patterns drift.
Two minutes, no email required
How production-ready is your AI platform?
Six questions; “Not sure” is a fine answer. You get the score and the fixes straight away. We keep your answers without your name or email, to improve the check, and if you then contact us from this tab, we attach them so the engineer can prepare.
Production
Question 1 of 6
Where do your models or AI features run today?
Filling in as you answer
Production
—
Training data
—
Evaluation
—
Monitoring
—
Retraining
—
Inference cost
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How we engage
Four ways to work with us
Start with a fixed-scope audit, scale into a project, keep us on to run it. Engineers on every call — there is no sales team.
2 weeks · fixed scope
AI Readiness Assessment
A structured audit of your data, infrastructure, and use-case pipeline. Delivered as a prioritized roadmap with cost and effort estimates for your top 3 AI initiatives.
6–16 weeks
Project Delivery
Fixed-scope delivery of a defined AI system — RAG pipeline, ML model, LLM integration, or MLOps platform. Clear milestones, weekly check-ins, handover with documentation.
Monthly · inside your team
Embedded Platform Engineers
One engineer or a full squad — sized to the roadmap, month by month. Senior engineers join your team, your tools, and your rituals, led by the same architect from day one.
Monthly · we run it
Managed Platform Operations
Senior engineers own your platform in production: on-call for incidents, with response times written into the contract (24/7 if you need it), upgrades, security patching, capacity and cost reviews, and a monthly written report. Your team keeps shipping features.
When the platform is the constraint
We also build the product on top.
APIs, services, and internal tools built by the same senior engineers who run your platform.
Delivered inside an existing engagement — never sold as a standalone body shop.
Production-grade by default: CI/CD, observability, and cost visibility from the first commit.
Our standards
What we don't do
We work with companies that are serious about shipping AI. That means being direct about the patterns we avoid.
Off-the-shelf AI wrappers dressed up as strategy
Proofs of concept with no path to production
Vendor lock-in hidden behind abstraction layers
AI for its own sake — every system we build has a measurable business outcome
Ready to talk about your AI initiative?
A direct conversation about your use case and whether we're the right fit.
Book a free 45-min architecture review45 minutes with a senior engineer: your setup, the risks, what to fix first. No sales team.