01
Custom Pretraining
Build domain-specific foundation models from your proprietary data — owned by you, trained where your data lives.
- Continued pretraining or from scratch
- Distributed training at scale
- Evaluation built from your workflows
Enterprise foundation model pretraining
Enterprise Pretraining.
Done Right.
We help the world's most ambitious companies build, optimize, and continuously improve proprietary foundation models — with proven best practices that compound efficiency over time.
Built for enterprises that lead in Financial Services · Healthcare · Manufacturing · Legal · Technology
What we do
The pretraining partner for enterprises that intend to lead.
Four disciplines, one outcome: models that are yours, measurably better, and cheaper to train and run with every cycle.
02
Best-Practice Consulting
Adopt the highest-leverage training, data, and evaluation practices used by frontier labs — without years of trial and error.
- AI maturity & data readiness audits
- Playbooks and team enablement
- Board-ready roadmaps
03
Ongoing Optimization
Continuous improvement loops that lower cost and raise quality with every training cycle.
- Cost and goodput optimization
- Iterative retraining with regression gates
- Efficiency that compounds year over year
04
AI Cost Optimization
Small and local models handle routine work; frontier models are reserved for deep reasoning. Up to 80% lower AI spend.
- Workload audit and savings map
- Evaluated task-to-model routing
- Local models for high-volume tasks
AI cost optimization
Stop paying frontier prices for routine work.
Most enterprise AI traffic doesn't need a frontier model. We route classification, extraction, and summarization to fast, inexpensive models — many running on your own hardware — and reserve frontier models for the problems that demand deep reasoning.
Up to
80%lower AI spend1 — with quality held to thresholds you set.
- ≈ free
Cache & rules
Repeated queries, deterministic lookups
- Lowest
Small & local models
Classification, extraction, reformatting, short summaries
- Moderate
Mid-size models
Grounded Q&A, drafting from templates
- Highest
Frontier reasoning
Multi-step analysis, complex judgment, novel problems
Illustrative. Line weight shows relative request volume; low-confidence answers escalate automatically to a stronger model.
1 Savings depend on task mix, quality thresholds, and model pricing. Published research on LLM cascades has reported cost reductions of up to 98% on benchmark tasks (Chen, Zaharia & Zou, 2023). We baseline your own traffic before committing to a target.
How we work
A delivery system engineered to remove guesswork.
Every stage produces an artifact you keep, and every scale-up decision is backed by evidence gathered at a fraction of the cost.
Explore the methodStep 01
Discover
Map use cases, data estate, infrastructure, and constraints — and establish candidly whether pretraining is the right lever.
Learn moreOutput
Readiness assessment
Step 02
Data Strategy & Curation
Inventory, redact, deduplicate, quality-score, and mix proprietary corpora into training-grade datasets with full lineage.
Learn moreOutput
Versioned training corpus
Step 03
Pretraining Architecture
Choose base model, size, tokenizer, and parallelism from scaling-law evidence gathered at small scale.
Learn moreOutput
Scaling plan & cost envelope
Step 04
Training & Evaluation
Fault-tolerant distributed training with continuous evaluation against harnesses built from real workflows.
Learn moreOutput
Evaluated base model
Step 05
Deployment & Continuous Improvement
Ship to production, then compound: each cycle reuses what the last one learned to cut cost and raise quality.
Learn moreOutput
Compounding roadmap
Founding partner program
Founding partner program
Shape the practice with us.
We are partnering with a select group of enterprises for our first engagements. Founding partners work directly with our principals, influence what we build next, and receive founding-partner terms.
- Principal-led engagement from day one
- Early access to our evaluation and routing tooling
- A published case study only if and when you choose
Security & trust
Built for the highest-stakes environments.
Your data never trains our models. Full isolation, audit logs, and private cloud, on-premises, and air-gapped deployment options.
Visit the Trust CenterYour data never trains our models
No client data or derivative is ever used for any other client, product, or model.
Full isolation
Dedicated environments per engagement. No shared clusters, no commingled storage.
Audit-ready by default
Every data access, training job, and artifact transfer is logged and exportable to your SIEM.
Deploy anywhere
Your VPC, dedicated private cloud, on-premises, or fully air-gapped.
Insights
Field notes on enterprise pretraining.
Start the conversation
Build the model only your data can build.
Tell us where you are — exploring, piloting, or already training — and we'll show you the fastest credible path to a model you own and an AI bill that shrinks.
We respond within 1 business day. Mutual NDA available before any data discussion.