Stop renting intelligence.
Own a model that beats GPT-4 on your task.
We take your data and ship a production-ready, fine-tuned open model — dataset, training, evaluation and deployment, all handled by us. It beats a generic frontier API on your specific task, at 10–50× lower inference cost. You own the weights.
Eval-first · open weights · on-prem option · no vendor lock
Why now
You're bleeding money — and data — into per-token APIs
For narrow, high-volume tasks — classification, extraction, routing, support replies, structured generation — a small fine-tuned open model wins. The blocker isn't desire; it's that fine-tuning, eval and deploy are hard and most teams have no ML engineer. We are that team, as a service.
Cost
10–50× cheaper inference once you own a 3B–14B model tuned for the job.
Privacy & compliance
Data never leaves your perimeter — on-prem or in your VPC. It never trains anyone else's model.
Quality on the long tail
Generic models are mediocre on niche domains. A tuned model is specialized for yours.
Latency & control
No rate limits, no surprise deprecations breaking prod overnight. You own the stack.
How it works
From your data to a production model
Free model audit
A 30-minute call. We tell you — honestly — whether fine-tuning is worth it for your task, and roughly what it would cost. No pitch if the answer is no.
Data engineering
We curate and augment a training set from your examples (most pilots start with <1k), engineer synthetic data, dedup, and build clean eval splits.
Train & evaluate
QLoRA fine-tuning on an open model, run with our 2-stage protocol (smoke test → full run). We benchmark against your current GPT-4/Claude baseline and report the numbers.
Deploy & hand off
A hosted endpoint, or Docker/vLLM on-prem in your cloud. You receive the full repo, weights, runbook and monitoring. It's yours.
Pricing
Two ways to start
Anchor on Production. The Pilot is the de-risking entry point. You pay inference at cost — no per-token markup. Custom enterprise scope quoted separately.
Pilot
The de-risking entry point.
- ✓1 task, 1 model
- ✓Curate up to ~5k examples (yours + synthetic)
- ✓QLoRA/LoRA on Llama / Qwen / Mistral / Gemma
- ✓Eval harness vs. your current GPT-4/Claude baseline
- ✓Hosted inference endpoint (API)
- ✓Model + eval report + endpoint handoff
- ✓Inference billed at cost — no per-token markup
Production
Hardened for production. Anchor here.
- ✓1 task, hardened for prod
- ✓Synthetic data engine, dedup, eval splits
- ✓Hyperparameter sweep + 2-stage protocol
- ✓Custom eval suite + red-team + regression set
- ✓On-prem or your cloud (Docker / vLLM) + monitoring
- ✓1 improvement cycle after live feedback
- ✓Full repo, weights, docs, runbook
- ✓Inference billed at cost — no per-token markup
Reference models
Small models that beat frontier APIs on one task
Open, reproducible demos built on the exact pipeline we run for clients. Benchmark numbers are illustrative reference results from our standard eval harness.
Atlas
GitHub ↗Knowledge-grounded QA
Prometheus
GitHub ↗Support reply generation
Artemis
GitHub ↗Sales / lead qualification
Hermes
GitHub ↗Code assistant
Zeus
GitHub ↗Intent routing
Arquimedes
GitHub ↗Structured extraction
Why MSC Labs
Evidence, not promises
Eval-first
We benchmark against your real GPT-4/Claude baseline and report numbers before/after. No vibes.
Open weights, you own them
Llama, Qwen, Mistral, Gemma. No vendor lock. Full repo and runbook handed to you.
2-stage training protocol
Smoke test → full run. We never burn paid GPU debugging blind. Predictable cost and timeline.
Privacy by design
On-prem / in-VPC option. Your data never leaves your perimeter, never trains anyone else's model.
FAQ
The honest answers
Prompting is rented intelligence at $/token. For a fixed, high-volume task a tuned small model is cheaper, faster, private and often more accurate. We prove it with an eval against your current baseline before you commit further.
Is fine-tuning worth it for your task?
Book a free 30-minute model audit. We'll tell you if a fine-tuned model beats your current API for your task — and roughly what it would cost. No obligation.