What you get in end-to-end AI delivery
We build custom AI models from scratch for teams that need measurable outcomes, not demos. Whether it is an LLM for document workflows, a vision model for quality control or time-series forecasting for risk, we design the data, run the training, prove it with evaluation and ship production-ready serving. Most projects start with a four-week PoC and move to production in six to twelve weeks.
Custom LLM, vision or time-series models with clear metrics, safety controls and production-ready serving.
Use-case and metric design
+We design use-cases and metrics strictly aligned to your business goals to ensure measurable success.
Data pipelines
+Comprehensive pipelines for data collection, cleaning, labeling, and governance to ensure high-quality input.
Training and fine-tuning
+Expert training and fine-tuning for specific model types including LLMs, computer vision, or time-series analysis.
Evaluation and safety
+Rigorous testing with red-team scenarios and guardrails to ensure model safety and reliability.
Low-latency serving
+Production-ready serving infrastructure with autoscaling and cost control mechanisms.
MLOps
+Full MLOps implementation for continuous monitoring, drift detection, and rollback capabilities.
Our Case Studies
Architecture Reference
Data
+Pipelines with lineage, versioned datasets, and clear labeling guidelines to maintain data integrity.
Model
+Base choice selection, implementation of adapters or fine-tuning, and quantization where feasible for performance.
Safety
+Implementation of content filters, jailbreak resistance, PII scrubbing, and rate limiting policies.
Serving
+Flexible inference options on GPU or CPU, including batching, caching strategies, and canary releases.
MLOps
+Comprehensive experiment tracking, model registry management, and continuous integration/delivery for models.
Who we help
We deliver domain-specific models for various high-requirement sectors.
Fintech and Web3
+Teams needing domain-specific models for risk assessment, fraud detection, or regulatory compliance.
Product Companies
+Companies building AI-native features that must run reliably and efficiently at scale.
Enterprises
+Organizations requiring on-premises or private cloud deployments with strict governance and security.
