> This is the markdown version of https://www.maniac.ai/agents. Visit the full page for interactive content.


\[ Enterprise agents \]

# The only way AI works  
is if you own the models.

Renting intelligence from a foundation model provider is fine for prototypes. Owning intelligence is how you ship AI that actually matters to your business.

[Book a Demo](/book-demo)[Learn More](https://docs.maniac.ai/agent-setup/agent-setup)

\[ Why own your models \]

## Intelligence that  
compounds.

### No vendor lock-in

If a provider changes pricing, deprecates a model, or silently degrades quality, you're exposed. Your own model is yours.

### Determinism

Foundation model providers silently update weights. Your outputs can change overnight. An owned model only changes when you promote a new version.

### Compounding advantage

A competitor using GPT-5 gets the same model as everyone else. Your model, trained on your data and edge cases, is a moat that deepens every month.

### Compliance & privacy

Regulated industries often can't send data to third-party APIs. Owning the model means the data never leaves your environment.

### Cost at scale is predictable

A fine-tuned model running on your infrastructure has a cost ceiling. Foundation model pricing is whatever the provider decides next quarter.

### Task-specific performance

A 14B model fine-tuned on your exact task will outperform a 400B generalist on that task. Smaller, faster, cheaper, and more accurate.

\[ How it works \]

## From your systems to  
intelligent workflows.

We leverage our continuous learning pipeline and context layer to integrate with your existing software, deploy agents that make work easier, and create specialized agents where the task demands it.

01

### Integrate with your systems

Maniac's context layer connects to your existing software products, CRMs, databases, internal tools, document stores. We map the data your AI needs to do real work.

02

### Deploy a centralized agent

A generalist agent that understands your business context, makes work easier for your team, and handles the tasks that need broad reasoning across systems.

03

### Create specialized agents

Some workflows are too complex or nuanced for a generalist. We create purpose-built agents for specific tasks, each with their own continuously improving model.

04

### Continuous learning

Every agent runs on our model engine. As your customers change, your data changes, and your workflows evolve, the models adapt automatically. No manual retraining.

## Engineering Blog

Deep dives on model optimization, agent throughput, and the economics of running intelligence at scale, plus updates from the Maniac team.

[View the blog](/blog)

[

Model LandscapeApr 16, 2026

### Claude Opus 4.7 vs Claude Mythos Preview: key differences, benchmarks, and availability

Claude Opus 4.7 vs Claude Mythos Preview: a practical Anthropic model comparison covering benchmarks, availability, pricing, safety posture, and use cases.

](/blog/claude-opus-4-7-vs-claude-mythos-preview)[

Model LandscapeApr 13, 2026

### Interactive open vs closed frontier across benchmarks

Step through how the best open-weight and closed-weight models improved over the last year on SWE-bench, AIME, GPQA, and more—using the same Vals AI sourced rows.

](/blog/interactive-open-vs-closed-benchmark-frontier)

## AI that works because  
you own it.

Stop renting models. Start owning intelligence that compounds for your business.

[Book a Demo](/book-demo)[Explore the API](/adaptive-inference)

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*Maniac, High throughput background agents. Opus-quality outputs at 1/50 of the cost. Learn more at [maniac.ai](https://www.maniac.ai).*