Small · Instant · Invisible
Trusted by
TenderSeal
VerseOdin
We help replace oversized general-purpose models with smaller, specialized systems engineered around your data, workflows, and infrastructure.
As AI moves from demos into core products and workflows, the requirements change. Enterprises need models that are economical at scale, predictable in production, deployable inside their infrastructure, and optimized for the work they actually perform.
That is the layer we build.
Purpose-built capabilities that reduce cost, improve performance, cut latency, and give enterprises greater control over how and where their AI runs.
Specialized models dramatically reduce the infrastructure required for production AI.
Specialized systems shaped around the constraints, data, and operating environments of each industry.
Reliable, private intelligence that supports care teams without adding friction to the work already happening.
Explore industry
Practical intelligence that runs close to the process and performs predictably under real operating constraints.
Explore industry
Systems designed around accountability, data boundaries, deployment requirements, and long-term operational ownership.
Explore industry
Systems designed to operate within strict reliability, deployment, and data-access boundaries.
Explore industryOur research team works across model architecture, training, post-training, evaluation, and inference to build foundation models and production systems that go beyond off-the-shelf capabilities.
Foundation model research
We design datasets, tokenization strategies, training objectives and pre-training runs to understand where focused models can match or outperform much larger systems.
Dataset design · Tokenization · Pre-training · Scaling
Model architecture
We test model structures, routing strategies, objective functions and parameter allocation through controlled experiments and ablations.
Architecture · Ablations · Routing · Multimodality
Post-training & evaluation
We build task-specific evaluation suites, run comparative benchmarks and develop post-training methods around the capabilities and failure modes that matter in practice.
Fine-tuning · Preference optimization · Evals · Reliability
Inference & systems
We study quantization, decoding, batching, caching and serving architectures to improve latency, throughput, memory use and inference cost.
Quantization · Decoding · Serving · Edge
We build and train models instead of limiting our work to third-party API orchestration.
Model decisions are supported by comparative runs, ablations and measured results.
Specialist systems are evaluated against credible frontier and open-model baselines.
Deployment behavior continuously informs the next research cycle.
Start with the AI problem in front of you.
Cost per outcome
Benchmark smaller, specialized models against your real workload. Preserve the quality you need while reducing cost, latency and vendor dependence.
What you get
Benchmark results, a validated model recommendation and a practical migration path.
Task success rate
Find where quality, latency, reliability and spend break down across models, retrieval, routing and serving infrastructure.
What you get
A measured optimization roadmap ranked by impact, effort and expected payback.
Time to production
Turn a high-value enterprise workflow into a deployable AI system with the right model, evaluation suite and infrastructure.
What you get
A production-ready system built and evaluated around your workflow.
