Workload-to-model for production AI

Turn one repeated AI workload into a specialized model you own.

Nablo takes one production workload from evaluation and approved training data through post-training, proof, and deployment. Your existing product stays in place. You keep the customer-specific model or adapter, subject to base-model and third-party licenses.

Check if your workload fits See the technical proof
Where Nablo sits

Training platforms provide tools. Nablo delivers the model.

Nablo uses existing training and serving infrastructure. We own the workload definition, evaluation, lawful data path, method choice, experiments, proof, and deployment handoff.

01 Existing production agent
02 One repeated workload
NABLO Workload-to-model system Evaluate, train, prove, deploy
04 Training infrastructure
05 Evaluated model or adapter

The model returns to the same product and workflow. Nablo is not another agent builder, foundation model, or low-level training API.

When to specialize

Start with a frontier model. Specialize when the workload repeats.

Frontier APIs are usually the fastest way to prove a product. A bounded production workload becomes a specialization candidate when one of these constraints starts to matter.

01 / ECONOMICS

Usage changes the cost curve

At enough repeated volume, a dedicated compact model can cost less per completed task than recurring frontier API calls. The comparison has to include real serving utilization.

02 / CONTROL

Latency and reliability become product constraints

A model deployed in your cloud or on dedicated infrastructure gives you more control over response time, capacity, versioning, and availability.

03 / QUALITY

The job rewards specific behaviour

A general model can be capable yet inconsistent on one narrow workflow. Task-specific data and evaluation can improve the behaviour that matters without rebuilding the application.

How Nablo works

One workload. One frozen bar. One deployable result.

Training is not automatic and reinforcement learning is not mandatory. The method follows the workload, the evidence, and the evaluation.

1

Define and measure the workload

We isolate one repeated model-backed task, inspect its tools and failure modes, and freeze the current quality, cost, and latency before changing it.

2

Build the lawful data path

We use the evidence the workload supports: approved traces, tests, tool results, human corrections, replay, or business outcomes. We fix the prompt or harness first when that is enough.

3

Train, prove, and deploy

We choose the appropriate post-training method, compare the result against the current system on the frozen evaluation, and deploy only if the result clears the agreed bar.

Why Nablo

Your product stays. Nablo improves the model underneath it.

KEEP THE SYSTEM

Your existing architecture stays

Nablo specializes one model-backed component inside the product already running. We do not replace your application, orchestration, tools, or customer experience.

FOLLOW THE EVIDENCE

The method is not predetermined

Supervised fine-tuning may be enough. Distillation, preference optimization, or RL are used only when the data and reward support them.

KEEP THE RESULT

The proof is portable

You keep the customer-specific model or adapter and its evaluation. Deploy it in your cloud, or use dedicated Nablo hosting with an agreed export path.

Who it is for

Built for production AI teams, not prototypes.

The strongest fit is an AI-enabled B2B software company with a repeated LLM workflow already running in a paid product or contracted service, a technical owner, and a measurable quality, cost, latency, or control problem.

Not a fit: early prototypes, general employee copilots, low-volume workflows, or teams looking for someone to build the entire application.

One bounded model-backed workflow repeats in production.
The team can define what a good result means.
A lawful source of training and evaluation evidence exists.
There is a real reason to improve quality, economics, latency, or control.
Technical evidence

We publish the work, including what failed.

These are technical experiments, not customer results. They show the evaluation and post-training discipline behind the product.

Technical case study

A 9B SQL agent, from 78 to 115

Qwen3.5-9B matched GPT-5.5 medium's aggregate score on one frozen 220-task benchmark. The post covers the training path, a failed GRPO run, regressions, cost, and limits.

78 → 115 / 220 Frozen SQL agent benchmark
Read the case study
Five-part research series

How a 0.8B agent improved step by step

Five experiments across supervised training, soft labels, student-state correction, on-policy probability distillation, and self-distillation. Each post shows the method, result, and failure.

5 experiments One shared SQL agent benchmark
Browse the research series
Design partnerships

Have a repeated production AI workload that may be ready for a specialized model?

Nablo works directly with production AI teams to establish feasibility, compare a compact model against the system they use today, and deploy only when the result clears the agreed performance bar.

Book a 20-minute fit call Email Isaac

If the workload is not suitable for specialization, we say so early.

Founder

Built by a technical founder with deep AI and product experience.

Research depth supports the product. The job is to deliver a result that works inside a real system.

Isaac Kargar

Isaac Kargar

Founder and principal engineer

Isaac has a PhD in multi-agent reinforcement learning, more than ten years of enterprise ML experience, and previously co-founded Resoniks, raising €2.65 million.

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