Model toMarket
Custom-built machine learning models trained on your data, tuned on dedicated compute, and deployed into your infrastructure. Every model is purpose-built for your problem.
Your Data, Your Model
We design, train, and deploy machine learning models built entirely around your problem. PyTorch stack, tuned on dedicated compute, exported to ONNX for production.
PyTorch 
Keras 
scikit-learn 
ONNX 
Optuna 
Python 
RabbitMQ 
AWS PyTorch Keras scikit-learn ONNX Optuna Python RabbitMQ AWS Custom Architecture Design
Every model is designed from the ground up for your dataset. We converge on an agreeable loss function, design the architecture, and train until your acceptance criteria are met.
Parallel Hyperparameter Tuning
Optuna-based HPT with custom search space division across GPU nodes. We run parallel trials on dedicated infrastructure to find optimal configurations faster than sequential cloud runs.
Dedicated Compute
Training runs on dedicated GPU infrastructure — private datacenter or cloud, matched to your project's requirements. Custom FSDP and data parallelism across nodes when model size demands it.
MLOps Pipeline
AWS Step Functions orchestrate training jobs, cross-validation, and deployment. Lambda functions handle triggers and monitoring. Models go from notebook to production with a clear, repeatable pipeline.
Data Drift Monitoring
Production models degrade. We build retraining pipelines that detect distribution shift and trigger automated re-tuning. Ongoing maintenance agreements keep your models accurate.
Feature Engineering & EDA
Exploratory data analysis, feature extraction, and dataset scaling pipelines. We clean, transform, and structure your raw data into training-ready datasets with reproducible processing steps.
We Build Models Like This
Deep Volatility is our own multi-variate time-series transformer — proof that we practice what we sell.
Deep Volatility
LNM — Large Numbers Model
A multi-variate time-series transformer trained on financial data across S&P 100, BIST 100, TEFAS funds, and 100+ cryptocurrency pairs. Ingests multi-source inputs — price, volume, order flow, macro indicators — and outputs multi-frame volatility and directional forecasts. Event-aware architecture handles dividend dates, FED announcements, and regime changes without manual feature flags.
Client instances are deployed via token-based access with an auto-fine-tune CLI that adapts the base model to private datasets. Automatic hyperparameter tuning runs on dedicated compute so clients get production-ready models without provisioning anything.
Multi-Source Ingestion
Price, volume, order flow, macro calendar, and alternative data feeds combined into a single tensor representation per time step.
Multi-Frame Forecasting
Simultaneous predictions across multiple time horizons in a single forward pass. Short-term for execution timing, long-term for directional conviction.
Continuous Retraining
Automated drift detection triggers retraining pipelines. Models stay current with shifting market regimes without manual intervention.
Agents That Act
Custom reinforcement learning agents trained via StableBaselines on domain-specific environments. From backtesting to autonomous real-time execution.
Custom RL Environments
StableBaselines-powered backtesting on custom Gymnasium wrappers. We model your domain as a Markov decision process, define reward shaping, and train agents with PPO or SAC until behaviour meets acceptance criteria.
Risk-Constrained Training
Hard safety constraints embedded into reward functions and observation spaces. Agents learn optimal behaviour without ever exploring forbidden state-action regions during live execution.
Simulation-First Validation
Agents are validated on historical replay and synthetic data before any live deployment. Walk-forward validation ensures models generalise to unseen market conditions.
Legion
Autonomous RL Ensemble
A custom A3C (Asynchronous Advantage Actor-Critic) implementation designed for non-episodic, continuous environments. Legion deploys a distributed ensemble of RL agents that independently track targets, manage multi-wallet balances, and execute low-latency position entry ahead of market movements — currently monitoring 1,500+ targets simultaneously.
Each agent maintains its own risk budget, position tracking, and exit strategy. The ensemble coordinates through shared state without a central controller, enabling horizontal scaling as new targets are added.
Automated, End to End
From raw data to production model — every step is orchestrated, versioned, and repeatable. No notebooks left running, no manual hand-offs.
Data Collection & Scraping
Automated scrapers, API ingestion, and sensor data aggregation pipelines. Raw data flows into versioned datasets with full lineage tracking — no manual exports.
Feature Engineering
Automated and manual feature extraction, scaling, and transformation pipelines. Reproducible processing steps turn raw signals into training-ready feature stores.
Automated Training
Training jobs run on AWS Deep Learning Containers with pinned framework versions. No SageMaker vendor lock — just Docker images, step functions, and your own orchestration logic.
Hyperparameter Tuning
Optuna-driven search across GPU nodes with custom search space division. Parallel trials converge on optimal configurations faster than sequential cloud runs.
Cross Validation & Registry
K-fold and walk-forward validation baked into every training run. Passing models are tagged, versioned, and pushed to a custom model registry with full metadata and reproducibility artifacts.
Continuous Retraining
Drift detection triggers automated retraining cycles. New data flows through the same pipeline — feature extraction, training, validation, registry — without human intervention.
No Vendor Lock-In
Training runs on AWS Deep Learning Containers — pre-built Docker images with pinned PyTorch and CUDA versions. We orchestrate with Step Functions and Lambda, not SageMaker. You own every artifact and can move the entire pipeline to any cloud or on-prem infrastructure without rewriting a single job.
From Training to Production
ONNX export, containerised microservices, and message-driven architecture. Models ship to production with the same rigour as any backend service.
ONNX Export Pipeline
Models trained in PyTorch are exported to ONNX for runtime-agnostic deployment. Smaller binaries, faster cold starts, and cross-platform compatibility without rewriting inference code.
Containerised Inference
Each model runs in its own container with pinned dependencies and health checks. Horizontal scaling behind a load balancer — spin up more instances when throughput demand spikes.
RabbitMQ Message Bus
Inference requests and results flow through RabbitMQ task queues. Decoupled producers and consumers mean upstream services never block on model latency.
Monitoring & Alerting
Inference latency, throughput, error rates, and model output distributions are tracked in real time. Alerts fire before degraded predictions reach downstream consumers.
Let's Build Something Together
Location
Istanbul, Türkiye
Have a project in mind or want to explore how we can help? Drop us a line or head to our contact page.
Let's Talk