Service
Pain
Most production workloads do not need the largest public model - they need a smaller one that fits latency and cost targets, respects your data rules, and behaves predictably on your tasks.
Delivery
We scope datasets and evaluation gates with you, then train or adapt models and compress them through distillation, quantization, pruning, and PEFT/LoRA where appropriate - so what ships matches what you agreed to measure.
Differentiator
Engagements are built around acceptance criteria and artifacts your ML and platform teams can operate - not a one-off notebook export or a generic checkpoint rename.
Repetitive internal workloads that hammer public APIs become expensive and hard to budget. A compact model on hardware you control turns marginal cost into capacity planning.
When responses must be fast or data must stay in a specific region or VPC, a right-sized SLM is often the straightest path - once evaluation and safety gates are explicit.
Domain terms, formatting rules, and refusal boundaries rarely match off-the-shelf weights. Custom data curation and targeted adaptation close that gap without pretending every task needs a 70B teacher.
The same four stages we use on the homepage - here in context for custom model work. Select a step for detail and where to read more on this site.
Custom SLM
Select a pipeline stage to read a short summary. Four steps from data through deployment.
Three pillars that usually appear in sequence or in parallel, depending on your baseline model and infrastructure.
We help you define what “good” looks like for your use case: source systems, labeling or weak-supervision strategies, PII and retention policies, train/validation splits, and leakage checks. Deliverables typically include dataset specs, quality reports, and reproducible extraction pipelines aligned with your governance workflow - not a one-time CSV dump.
Distillation transfers behavior from a larger teacher (often run privately in your boundary) into a student that matches latency targets. Quantization and pruning reduce memory and compute where metrics allow. We document which techniques were applied, what accuracy moved, and how to re-run compression when baselines change.
When full fine-tunes are unnecessary, parameter-efficient methods adapt a base checkpoint with smaller update sets - useful for fast iterations and controlled promotion paths. We specify adapter formats, merge strategies, and how your serving stack should load them.
Exact artifacts are named in the statement of work; this list is a typical superset for planning conversations.
Ready to scope a pilot or PoC?
We align metrics, data access, and infra before proposing a concrete plan.
Practical answers for technical buyers; validate resale, SLA, and capacity wording with legal and sales before public launch and paid campaigns.
Discuss data boundaries, evaluation, and rollout with our team
Start with a discovery call or a scoped PoC - see the contact page for both options.