AI Development
Production AI systems.
Built to run, not to demo.
We design, build, and operate AI systems that work under real-world conditions — unpredictable data, high stakes, zero tolerance for silent failures.
No pitch deck. No obligation. We'll tell you honestly if we're a fit.
The real problem
Most AI projects fail after the demo.
A model that scores 94% in a notebook is not a product. Production is a different environment with different failure modes — and most teams only discover this after budget is spent.
Data changes, model doesn't know
Production data drifts. Seasonality shifts. Upstream schemas break. A model trained last quarter silently degrades — and no alert fires.
Offline metrics don't reflect reality
An F1 score of 0.91 on a held-out test set means nothing if the test set doesn't match the distribution your model will face on Tuesday morning.
No one owns the model in production
Data science ships the model, DevOps runs the container, product owns the KPIs. Nobody has a runbook for when the model starts hallucinating at 2am.
Inference costs scale faster than value
You build a RAG pipeline, it works in staging, then usage grows and your LLM bill triples. Nobody designed the cost model from the start.
The "pilot" is permanent
Proof-of-concepts become the production system by accident. Hardcoded paths, no rollback, no monitoring. Six months later, nobody dares touch it.
Integration is underestimated
Connecting an AI model to existing systems — ERP, databases, legacy APIs — takes longer than building the model. Most teams only realize this mid-project.
What we build
Concrete capabilities, not abstract descriptions.
We work in four areas. If your problem doesn't fit, we'll tell you directly rather than overpromise.
Computer Vision
Object detection, segmentation, and classification in production environments with edge deployment, low-latency inference, and real-world data variance.
- → Detection and segmentation pipelines
- → Edge and on-device inference
- → Data annotation pipelines and tooling
- → Domain-specific training and fine-tuning
LLM / RAG Systems
Retrieval-augmented generation and LLM pipelines grounded in your data — with evaluation harnesses, cost controls, and access management built in.
- → RAG architecture and chunking strategy
- → Hybrid search and re-ranking pipelines
- → RBAC and multi-tenant retrieval
- → Evaluation, hallucination detection, guardrails
ML for Business
Practical guide for business and product teams: when ML is worth it, where projects fail, and how to evaluate readiness before investing.
- → ML vs rule-based systems
- → Probability, thresholds, and business risk
- → Typical failure modes before production
- → Readiness checklist for teams and data
AI Consulting
Independent technical assessment of your AI strategy, architecture, or existing systems. We tell you what will and won't work — before you spend on it.
- → AI feasibility and risk assessment
- → Architecture review and redesign
- → Team upskilling and technical leadership
- → Vendor and tooling evaluation
Computer Vision
Vision systems that hold up in the field, not just the lab.
Real computer vision projects fail for predictable reasons: training data that doesn't match field conditions, models that can't handle edge cases, inference pipelines that break under load. We solve these before deployment, not after.
Crop disease detection at scale
We built the AgrigateVision system: drone-captured field imagery processed in real time, 40K+ images, multi-class detection with IoU-optimized training and on-device inference. See the case study →
AR interior fitting room
RoomIQ: real-time object placement using camera-based room estimation, hybrid classical + ML geometry engine, sub-100ms rendering on mobile. See the case study →
What the engagement covers
Common failure modes we prevent
LLM / RAG Systems
Enterprise search and automation on your data — without hallucinations and runaway costs.
Most RAG systems work fine in demos and break within weeks in production. The reasons are always the same: no evaluation harness, no cost model, no access controls. We build the boring infrastructure that makes LLMs reliable.
AI Infrastructure / MLOps
The infrastructure that keeps AI systems running after launch.
Shipping a model is not the end. Most production incidents happen in infrastructure: failed feature stores, broken training pipelines, alert fatigue, no rollback path. We build and operate the MLOps layer so your team can focus on the product.
Stack we work with
Stack is chosen to fit your constraints — not to match a default template.
What we've shipped
Real systems. Real constraints.
Every project below went to production. None started as a clean-slate greenfield — all had data issues, integration complexity, and hard requirements.
AgrigateVision
Drone-based crop disease detection. 40K+ training images, multi-class segmentation, on-device inference in field conditions.
View case study →
Steve — Trading Bot
Live algo trading with statistical validation, multi-layer risk controls, and MT5 integration. Running in production on real capital.
View case study →
AxisCorePay
Payment infrastructure with Shamir secret sharing, MPC threshold signatures, and dual-token settlement engine.
View case study →
RoomIQ
AR interior fitting room. Real-time object placement, hybrid geometry engine, sub-100ms on mobile. 95%+ user placement accuracy.
View case study →
MTRobot
Institutional algo trading platform. Isolated execution environments, event-driven architecture, 3-click deployment for non-technical traders.
View case study →
Under NDA
Several engagements are not publicly disclosed. References available on request under NDA.
Who we work with
When companies come to us.
We work best with teams that have tried something and hit a wall — not teams looking for a vendor who'll agree with everything. These are the situations where we add the most value.
Our model works in staging but degrades after two weeks in production. We don't know why.
→ Data drift, training-serving skew, or missing monitoring. We can diagnose and fix within discovery.
We built a RAG prototype that demos well, but the answers aren't reliable enough to ship to customers.
→ Chunking, retrieval quality, and evaluation harness are the usual culprits. Fixable without starting over.
We want to add computer vision to our process but have no idea if our data is good enough to start.
→ Data audit in 2–3 weeks. We'll tell you exactly what you have and what it's realistically worth.
Our data science team builds models, but they keep getting stuck at integration. We've had three failed handoffs to engineering.
→ We bridge data science and production engineering. This is a structural problem, not a skill gap.
We're spending $40K/month on LLM API calls. We need to cut costs without breaking the product.
→ Semantic caching, query routing, and tiered inference can typically cut costs 40–70% without quality loss.
We need an outside technical opinion. Our team is too close to the problem to see what's wrong.
→ Architecture review with a written report. Clear findings, no upsell pressure.
Frequently asked
How long does it take to ship a production AI system?
Discovery and architecture take 2–3 weeks. A production pilot runs 2–8 weeks depending on data readiness and integration complexity. Full rollout adds another 2–16 weeks. The biggest variable is data quality, not model selection.
We already have a model. Can you help productionize it?
Yes. Most of our engagements start with a model that 'works in notebooks' but isn't production-ready. We audit the pipeline, add monitoring, harden the serving layer, and set up rollback and incident response.
Do you work with internal teams or replace them?
Either. We can embed as a technical lead within your team, take full ownership end-to-end, or work as a bridge between data science and engineering. We adapt to your structure.
What domains do you work in?
Computer vision in agriculture and industrial settings, LLM/RAG pipelines for enterprise search and automation, algorithmic trading systems, and custom MLOps infrastructure. We do not take on projects outside our expertise.
Ready to talk?
Tell us your constraints.
We'll scope a delivery plan.
30-minute call. No pitch deck. We'll ask about your data, constraints, and timeline — and tell you honestly whether the problem is solvable and how.
EU-based team · 24h response · NDA available from day one