Purpose-built performance
A model trained for your task, instead of one general model stretched across every task.
The specialization layer for AI
Intelligence, made specific
Fine-tune foundation models on what makes your organization different. Run them serverless. Build on top.
Status
General model
Specialization
000%
Target
Fraud intelligence
Scroll
001
The problem
Foundation models know the world.
They don’t know yours.
General models are trained on everything, so they’re specialized in nothing. The next leap in AI won’t come from a bigger model. It will come from models shaped around one domain, one dataset, one job.
- 01Your data
- 02Your domain
- 03Your workflows
- 04Your problems
002
The shift
You don’t need the frontier.
You need the right fit.
Most enterprise use cases don’t call for the largest model in the world. An open-source model, fine-tuned on your data for your task, delivers the performance that matters, at a fraction of the cost, with your data under your control.
-
01
General frontier model
Trained on everything, specialized in nothing. Vast, costly, and run on someone else’s terms.
-
02
Fine-tuned on your data
An open-source model learns your domain, your documents, and your task.
-
03
Specialized open model
Compact, precise, and yours, running at a fraction of the cost.
- Model size
- Running cost
- Fit to your task
A fraction of the cost
Smaller, specialized models are cheaper to run at scale. Pay for the capability you use, not the capability you don’t.
Your data stays yours
Your data trains your model, not someone else’s. Sensitive information stays under your control.
General frontier model
Specialized open model · Marven
Built for
Every task
Your task
Running cost
Premium, per-token pricing
A fraction of the cost
Your data
Sent to a third-party provider
Stays under your control
Ownership
Rented access
A model you own
003
Platform
Fine-tune.
Infer. Build.
One platform that takes you from a general-purpose model to a specialized one running in production, and to the products built on top of it.
Stage 01 — Fine-tuning
Make it yours
Start with a foundation model. Adapt it to a domain, dataset, task, or behavior. The result performs differently because it understands something specific.
- Base
- Leading open foundation models
- Input
- Your own data, for your own task
- Evaluation
- Compared against the base model
- Output
- A specialized model you own
from marven import Marven
mv = Marven()
job = mv.fine_tune(
base="open-foundation-8b",
data="s3://acme/claims-2025.jsonl",
objective="flag-anomalous-claims",
)
model = job.wait() # → marven/acme-claims-v1Stage 02 — Inference
Run it. No infrastructure.
Models are only useful when they run. Deploy specialized models to serverless inference without managing the compute underneath.
- Compute
- Serverless, nothing to provision
- Scaling
- Scales with demand
- Access
- One endpoint for every model
- Operations
- No GPUs or clusters to manage
endpoint = model.deploy(serverless=True)
endpoint.run(
"Claim #48213: water damage, filed two"
" days after the policy started."
)
# {
# "risk": "high",
# "signals": ["policy_age", "claim_type"]
# }Stage 03 — Intelligence
Build on top
The model is the primitive. The application is the product. Build systems that reason over your data, detect patterns, automate decisions, and work inside real workflows.
- Reasoning
- Over documents, data, and knowledge
- Automation
- Detect patterns, automate decisions
- Integration
- The workflows you already run
- Result
- Products built on your own model
claims = mv.intelligence(
model=model,
sources=["sharepoint://claims",
"postgres://policies"],
)
claims.watch("new_claims").on_flag(
route_to="investigations",
)
004
Domains
One platform.
Every domain.
A general-purpose model is the starting point. Specialization is what makes it useful.
Organizational
Intelligence
An intelligence layer that understands your organization’s knowledge, data, documents, and processes.
Fraud
Intelligence
Models that find patterns across transactions, behavior, entities, and events, and surface what doesn’t fit.
Financial
Intelligence
Turn financial information into systems that analyze, understand, and reason at scale.
Document
Intelligence
Go beyond extracting text. Understand structure, relationships, context, and meaning across complex documents.
Customer
Intelligence
Models built around conversations, behavior, intent, and the full context of each customer.
Operational
Intelligence
Connect intelligence to the processes that run your organization, so insight turns into action.
005
Not another chatbot. Not another wrapper.
Build your own intelligence
AI that’s built for a purpose, on a platform made for building it.
- T−04Start with a model
- T−03Make it yours
- T−02Run it
- T−01Build on it
- T−00Scale it