What we provide
With our service, training costs only 10% of a typical approach.
Map your workflows, expert practice, and historical cases. Pin down the task goal, correct behavior, and common errors.
Rebuild your enterprise task environment under control, and set scoring logic and edge cases.
Compare models, harnesses, and agents across versions. Review successes and failures, and set the launch bar.
From real work
Around concrete industry tasks, we run hard, scene-specific training.
Train the model around clinical and research questions: retrieve papers, judge evidence grade, and extract population, intervention, comparison, and outcome. Tie conclusions back to evidence and build a traceable evidence chain so researchers can compare applicability and disagreements across studies.
For drug R&D, build tasks around targets, mechanisms, clinical studies, and competitor progress. The model learns the team's screening bar and evidence judgment, and assembles support or rebuttal for a hypothesis so research decisions can be checked.
Around ICD coding, chart-content QC, and medical data standards, train the model to catch coding drift, missing records, and logical conflicts in care information.
Shipped work covers 27 QC indicators and more than 8,000 physician-experience traces, turning expert judgment into a skill the model can learn.
Why post-train
Keep intelligence sovereignty in your own hands.
| Your model — intelligence that is yours | |
|---|---|
| Quality | Dedicated benchmark and specialist data |
| Latency | Less wasted reasoning and retries |
| Task cost | Model and inference tuned to the task |
| Iteration | Business feedback drives the next train |
| Ownership | Your data, weights, and capability accrue |
| General model — not optimized for you | |
|---|---|
| Quality | General skill, no specialist training |
| Latency | A general inference path |
| Task cost | A general calling setup |
| Iteration | Moves with the general release |
| Ownership | A shared general model |
Available models
We support frontier bases. Training stays smooth and stable.
Training and delivery
Advantage 1
Frontier models such as Kimi K3 and DeepSeek V4.1 Flash, or a light model aimed at one task: we post-train both. From your goal, quality bar, and budget we pick the base, then train and deploy.
Advantage 2
We post-train with LoRA, shared bases, and distributed optimization so fewer parameters update and less compute is spent. On shipped projects, GPU cost dropped by more than 90%.
Advantage 3
From dedicated eval and data through post-training, inference, and iteration, we take the full loop. You work with one Mind Lab team. We own handoff between steps and keep the model moving through automation, so you drop the coordination cost.
Advantage 4
We turned large-model training infrastructure into a service you can use, and dropped the first-training bar to $1,000. With light adapters and on-demand scheduling, one real task is enough to start a model that is yours.
Ways of working
For product and business teams. You set the goal and review the plan and cost; we run benchmark, data, training, and deploy end to end.
For teams that already have models and data. You choose the model, method, and deploy constraints; our platform runs the engineering and version delivery.
For algorithm and research teams. You write the loss, trainer, and RL loop; our platform supplies training and inference infrastructure.
Contact
Tell us the industry, the target task, and the data you already have. Together we pin down the benchmark, method, and deploy plan, starting from one real task.
