Fine-tune open models on your data. In your environment.
Backboard Post-Training turns open-weight models into specialists that know your domain, your workflows, and your language. Without ever sending your data outside your walls.
The problem
General-purpose models are impressive, but they don't know your business. They don't know your product catalog, your legal precedents, your underwriting rules, or how your best engineers actually write code.
You have two options today, and both are broken:
∞
Prompt-engineer forever.
Stuff more context into every call, pay for it every time, and still get generic answers.
↗
Send your data to a hyperscaler for fine-tuning.
Lose control, lose sovereignty, and hope their terms of service hold up.
Neither works for teams that need real domain expertise and real data control.
What Post-Training does
Backboard Post-Training fine-tunes open-weight models on your proprietary data, in an environment you control, and delivers a model that's genuinely yours.
In practice, Post-Training lets you:
✦
Turn a general model into a domain expert for insurance, legal, engineering, healthcare, government, or any specialized field
⌂
Keep your data in-house the entire time, from training to deployment
◈
Own the resulting weights so you're never locked into a vendor
≫
Ship smaller, faster models that outperform larger general-purpose ones on your specific tasks
Why it matters
◎
For AI leaders
A fine-tuned open model on your infrastructure is often cheaper, faster, and better than a frontier API call for your specific workload. Post-Training is how you get there without a research team.
⚖
For regulated industries
Your data never leaves your environment. Not to us, not to a hyperscaler, not to a model provider. That's the only posture that works for finance, healthcare, government, and any team under a data residency mandate.
✦
For product teams
A model that actually understands your domain gives you accuracy your users can feel. Fewer hallucinations, fewer guardrails, more useful answers.
How it works
Post-Training runs as a managed pipeline inside your environment or ours, depending on your data policy.
01
Discovery
We work with your team to identify the task, the data, and the evaluation criteria that define success.
02
Data preparation
Your data is cleaned, structured, and formatted for training, with your team in the loop the whole way.
03
Training
We fine-tune the open-weight model of your choice using the techniques best suited to your task, from full fine-tuning to LoRA and beyond.
04
Evaluation
We benchmark the resulting model against your criteria and against the general-purpose baseline, so you see the lift.
05
Deployment
The model ships to your Backboard environment, ready to serve through the Unified API and, if you want, compressed with BBQ.
Most projects go from kickoff to deployed model in four to eight weeks.
Proven results
Post-Training is already live with customers in insurance, real estate, and infrastructure. Typical outcomes include:
✓
Smaller models beating larger ones on domain-specific accuracy
↓
50 to 90 percent inference cost reduction compared to frontier API calls for the same task
⌂
Full data sovereignty with training and inference inside customer environments
"
Post-Training cut our inference costs by 70 percent while accuracy on underwriting tasks actually improved. We trained and deployed entirely inside our own VPC — nothing ever left our environment.
AV
Alessandra Voss
VP of AI Platform, Solraven Assurance
Post-Training and the Backboard platform
Post-Training is one of three ways Backboard helps you keep AI in-house:
⇄
Unified API to access every model through a single endpoint
⚙
BBQ Quantization to compress those models onto your hardware
✦
Post-Training to shape them around your data
The pipeline compounds. Fine-tune with Post-Training, compress with BBQ, serve through the Unified API. One platform, one contract, one team supporting you end to end.
Your data, your model, your weights
We say this loudly because the industry doesn't.
We do not train on your data or codebases. Ever.
Not for our models, not for anyone else's.
You own the resulting weights.
They're yours to keep, deploy, and even leave with.
Training can run in your environment.
Cloud, private cloud, on-prem, or air-gapped.
Sovereign by default.
Data in transit and at rest stays where you need it.
Get started
For enterprises and governments
Book a scoping call. We'll help you identify the task with the highest ROI and outline a 30, 60, or 90 day plan.
For partners and systems integrators
Post-Training is available through our partner program with joint delivery models.
For developers and researchers
Read about our approach, evaluation methodology, and open-weight model support.
FAQ
Which models can you fine-tune?
Open-weight models across the Qwen, Llama, DeepSeek, Mistral, and Gemma families. If there's a model you want to work with, ask us.
Where does training happen?
In the environment that fits your data policy. We support cloud, private cloud, on-prem, and air-gapped deployments.
Do you train on our data?
No. Never. Your data is used only to train your model, and only for you.
Who owns the resulting model?
You do. The weights are yours to keep, deploy, and take with you.
How long does a Post-Training project take?
Most projects run four to eight weeks from kickoff to deployed model, depending on data readiness and task complexity.
Can I combine Post-Training with BBQ?
Yes, and most customers do. Fine-tune first, quantize second, then serve through the Unified API on your hardware.
What if we don't have clean training data?
We help with that. Data preparation is part of every project, and we've built tooling for the messy real-world cases.
Build with AI. Keep it in-house.
Post-Training is how the best teams turn open models into their own competitive advantage.