Farloop

The problem

General-purpose models don't know your business. Fine-tuned ones go stale.

Cost

Frontier APIs get expensive at production volume.

Per-token pricing scales with every task you automate. At production volume, the bill starts deciding which workflows are worth running at all.

Quality

Prompts and retrieval hit a ceiling.

You can describe your business in a prompt. You can't fit years of expert judgment into one. Judgment-heavy work needs a model that has learned how your people decide.

Drift

A model trained once is already out of date.

Products, policies, and edge cases change. Without a loop that feeds production back into training, a fine-tuned model drifts, and nobody owns retraining it.

How it works

Built from what your experts know. Improved by what production teaches.

Build a specialized model, put it to work in your environment, and keep it learning from the cases it gets wrong.

Build

A model trained on how your business decides

Your documentation, decisions, and corrections become the training set. The result is a model that outperforms general-purpose APIs on your tasks.

Start from what your experts knowDocumentation, past decisions, and expert corrections are turned into verified training data. Where real examples are thin, synthetic ones are generated and checked against your rules.
Post-train the right open modelQwen, Llama, Gemma, and other open-weight models, sized to the task. When a stronger open model ships, your data, evals, and deployment carry over to it.
Measured against your definition of correctTraining is scored on evals written for your workflow, so progress means progress on your tasks, not on a public benchmark.

farloop · training runillustrative

$ farloop train document-reviewer

base model          qwen3-8b
sources             docs          412 pages
                    decisions     3,180 records
                    corrections   940 items
synthetic           +6,200 generated
                    6,047 passed checks
target evals        1,152 cases

✔ trained document-reviewer v1.0
held-out eval       93.4%  (general-purpose API: 89.1%)

Run

In production, inside your environment

An API endpoint your engineers call like any other provider, served from infrastructure you control.

Runs inside your networkDeploy in your VPC on AWS, GCP, or Azure, or on-premise. Managed hosting is available if you would rather not run GPUs yourself.
A drop-in endpointAn OpenAI-compatible API, so switching a workflow to your own model is a configuration change, not a rewrite.
Ship only what passesA new version replaces the one in production only after it clears your held-out evals. Rolling back is one step.

farloop · deploymentsillustrative

$ farloop deployments document-reviewer

environment         your VPC
endpoint            POST /v1/chat/completions

versions
  v1.14  production   held-out 93.4%   serving 100%
  v1.15  candidate    held-out 94.1%   regressions 0
  v1.13  retired

latency p50         210 ms
cost / 1k tasks     $0.71   (frontier API: $11.20)

Learn

A model that keeps up with your business

The cases your model gets wrong in production are the most valuable training data you have. Farloop collects them, learns from them, and ships the result on a cadence you set.

Find what went wrongAn agent harness reviews production traces, flags the cases where the model went wrong, and collects feedback from the experts who caught them.
Failures become evalsEach failure becomes an eval that defines the expected result. Over time, your eval set becomes the definition of correct for your business.
Retrain, verify, ship, repeatTargeted training data addresses the failures. An update ships only if it fixes them without breaking what already worked.

farloop · cycle summaryillustrative

$ farloop cycle document-reviewer --cycle 15

traces reviewed     41,208
failures flagged    163   (0.40%)
  ├ wrong field mapping      71
  ├ missed exception         52
  └ outdated rule (v3 → v4)  40
expert feedback     38 corrections

new evals           +163   total 2,417
training examples   +1,940 generated
                    1,902 passed checks

post-train          v1.14 → v1.15
held-out eval       94.1%  (v1.14: 93.4%)
regressions         0

✔ ready to promote document-reviewer v1.15

Use cases

Built for work that needs judgment, at volume.

Anywhere a general model gets close but your experts still have to correct it.

Document extraction and review

Read invoices, contracts, forms, and claims the way your reviewers do, and apply the same rules they apply.

Support and ticket resolution

Resolve refunds, escalations, and exceptions according to your policies, not a generic playbook.

Classification and moderation

Categorize, tag, and moderate content at scale with the precision of your best reviewer.

Code and integration generation

Generate connectors, transformations, and configuration that match how your platform actually works.

Agent tool use

Train agents on your internal systems and APIs, so long multi-step tasks hold up in production instead of only in demos.

Operational decisions

Routing, prioritization, and approvals where a specialist currently makes the call by hand.

Compared

Three ways to run AI on your business.

	Frontier API + prompts	One-off fine-tune	Farloop
Knows your business	Only what fits in the prompt	Yes, as of training day	Yes, and keeps learning
Cost per task	High, grows with volume	Low	Low, about a tenth of frontier
Improves from production	No	Only if someone retrains it	Continuously, from real failures
Regression testing before updates	Not in your control	Manual, if at all	Built in, on held-out examples
You own the model	No	Yes	Yes, weights, data and evals
Who maintains it	Your team, via prompts	Your ML team, if you have one	Farloop, alongside your team

Swipe sideways to compare →

Working with us

Every engagement starts with the workflow, not the model.

A specialized model is only as good as the understanding behind it. Pick the mode that fits your team.

With your team

We build it with you

Farloop engineers join your team for the engagement: mapping the workflow, writing the first evals with your experts, training the model, and running the improvement loop until it holds up in production.

For your team

We run it for you

Your team keeps the workflow and reviews what the model gets wrong. Farloop handles data generation, training, evaluation, and infrastructure, and reports on every update before it ships.

Ownership and security

Your data stays yours. So does the model.

Nothing leaves your network

Train and serve inside your VPC or on-premise. Managed hosting is optional, never required.

The weights are yours

Weights, training data, and evals belong to you and can be exported at any time. Nothing you share is used to train models for anyone else.

Every update is yours to approve

Each new version arrives with its eval results and a one-step rollback. It goes live when you say so.

Questions

What people ask us first.

What does early access include?

The Farloop team working directly on one of your workflows: training a model on your expertise, running it in your environment, and operating the improvement loop. Early access partners shape what we build and get pricing that reflects it.

Which models do you train?

Open-weight models such as Qwen, Llama, Gemma, Mistral, and DeepSeek, sized to the task. Most workflows run well on models between 4B and 32B parameters, which is where the cost advantage over frontier APIs is largest. The base model is chosen against your evals, and when a better open model comes out, your data and evals carry over to it.

Where does the model run?

In your cloud (AWS, GCP, Azure) or on-premise, so your data never leaves your environment. We can also host it on managed infrastructure if you'd rather not run GPUs. Training can happen in either place.

What do you need from us to start?

A workflow to focus on, access to the documentation and past decisions around it, and some time from the people who know it best. If you already have an AI system in production, its traces and the corrections your team makes are the most valuable input.

Do we need our own ML engineers?

No. Your engineers integrate the model the same way they would any API, and your domain experts review failures. Farloop handles data generation, training, evaluation, and deployment.

How is this different from fine-tuning once?

A single fine-tune captures your business on one day. Farloop keeps capturing it. Production failures become evals and training data, the model is retrained, and each update is checked for regressions on held-out examples before it ships. The maintenance is the product.

How often does the model update?

On a cadence you set. Some teams promote a new version as soon as it passes regression checks; others batch updates behind a review step. Either way, nothing reaches production without clearing the held-out evals.

How is it priced?

An annual subscription per workflow, scoped once we've measured your baseline. At production volume the savings against frontier API bills typically cover it several times over.

Early access

Build a model that knows your business.

We're opening Farloop to a small group of early access partners. Tell us about one workflow you'd like to run on a model you own, and we'll come back with whether it's a fit.

Request early access

A short form: who you are, the workflow, and where it runs today. We reply within a few days.

Get early access

Or email us

hello@farloop.ai

Models that learn your business. And keep learning.