Your model, chosen by evidence. Tuned to fit.
We do not guess which model fits you — we measure it. Candidate open-weight models are simulated side by side in isolated VM environments on your actual workload, benchmarked, and only then selected, tuned, and deployed.
jh@binah.team — we respond within one business day.
How we choose your model
Model selection is an engineering decision, not a preference. Every engagement starts with evidence.
Define requirements
Workload, quality bar, latency and cost targets, data-residency constraints — agreed and written down before anything runs.
Simulate in VMs
Candidate models — Gemma, gpt-oss, Llama, and more — run side by side in isolated VM environments on your real tasks and data.
Benchmark report
You receive a per-model report: task quality, latency, cost per thousand requests, and GPU footprint — evidence you keep, whatever you decide.
Select & architect
Based on the numbers we recommend one model, a multi-model mix, or a hybrid with Claude — and design the serving architecture around it.
The tuning work
Once the model is chosen, we adapt it to your domain and deliver it where your data lives.
Model fine-tuning
- Supervised fine-tuning (SFT)
- LoRA / QLoRA adaptation
- Preference tuning (DPO)
- Domain and task specialization
Data & evaluation
- Training dataset curation
- Synthetic data generation
- Benchmark design and reporting
- Before/after quality measurement
Private serving
- Quantization (GGUF, AWQ)
- vLLM and Ollama serving stacks
- On-premises and VPC deployment
- GPU sizing and cost planning
AI infrastructure consulting
Beyond a single model: we plan the AI infrastructure your roadmap needs.
Model portfolio design
Single model or several: a small tuned model for high-volume tasks, a frontier model for hard cases — with routing rules between them.
Capacity & cost planning
GPU sizing, quantization strategy, on-premises vs VPC vs hybrid — with a cost model you can put in front of your CFO.
Operations & governance
Monitoring, evaluation gates for model updates, access control, and audit trails — designed in from the start.
What you receive
Every tuning engagement ends with assets you own — no lock-in.
- Benchmark report across candidate models
- Tuned model weights — yours outright
- Reproducible training pipeline
- Evaluation suite and quality baselines
- Serving stack (vLLM / Ollama) configured
- Operations runbook and handover