Model engineering · AI products

Make AI fit the work.

From industry model training and post-training to optimized inference, private deployment and AI products, NeuralStream makes AI fit real-world workloads.

Explore what we build ↓Discuss your workload ↗

Engineered around the workload. Evaluated in the real world.

01 Adapt02 Optimize03 Deploy04 Improve

NeuralStreamModel
intelligenceFrom weights to workflows

01 / ShapeTrain & adapt

02 / OptimizeQuantize & accelerate

03 / ServeDeploy & integrate

04 / ImproveEvaluate in use

The model lifecycle / concept

Designed for production realities

01 Fit the task

02 Fit the environment

03 Improve the whole system

What we build

From model capability
to operational value.

Three connected solution families. Each begins with a real workload and is designed around the quality, cost, data and operational constraints that matter.

01 / Model engineering

Make the model right for the job.

Develop and adapt industry and enterprise models for defined tasks. Work can span data strategy, supervised fine-tuning, post-training, distillation and task-specific evaluation — with training from scratch considered only where data, compute and business requirements justify it.

Industry model developmentPost-trainingDistillationQuantization

Typical outputs Evaluation baseline · adapted model artifact · optimization recommendations · deployment plan.

Discuss a model program ↗

Foundation modelStarting point

Domain adaptationFit to the task

Optimized modelEvaluate & serve

02 / Inference & private AI

Make deployment work in your world.

Deploy models into cloud, dedicated or on-premise environments. Tune serving and runtime behavior against the actual workload — balancing quality, latency, throughput, infrastructure cost and data boundaries.

Inference optimizationQuantized servingPrivate deploymentEvaluation

Typical outputs Serving architecture · workload benchmark · deployment package · operations handover.

Plan a deployment ↗

ModelQuality & task fit

→

RuntimeLatency & throughput

→

EnvironmentCloud, dedicated, on-prem

03 / Agent products

Move from answers to finished work.

Build AI experiences that use tools, maintain task context, respect workflow boundaries and return work that people can review. We bring product thinking and measurable task evaluation to the experience as well as the model beneath it.

Task completionTool useWorkflow controlHuman review

What matters Clear task scope · reviewable output · controlled actions · measurable quality.

Explore StreamCode ↘

Agent
execution

A lifecycle, not a one-off model

Built to improve
after deployment.

A model is only one part of a successful AI system. We connect adaptation, optimization, deployment and evaluation so each decision is grounded in how the system will actually be used.

01 / DEFINE

Start with the workload.

Set task-level success criteria, data boundaries and a baseline before choosing how much to train or tune.

02 / ADAPT

Shape model behavior.

Apply data curation, fine-tuning, post-training or distillation when evidence shows it is the right route.

03 / OPTIMIZE

Balance the trade-offs.

Evaluate quality, speed, throughput and cost together rather than optimizing one metric in isolation.

04 / OPERATE

Deploy with control.

Integrate into the target environment, observe performance and improve against real usage.

Important by design: not every workload needs fine-tuning or training from scratch.Choose the lightest approach that meets the requirement.

A product from NeuralStream

Meet StreamCode.

An AI coding agent experience for turning software tasks into structured work. StreamCode is part of our broader direction: building AI products that do more than generate text.

Explore StreamCode ↗

Opens the separate StreamCode product website.

StreamCode

AI coding workflow

01Understand the task
02Inspect context and constraints
03Plan the change
04Implement and verify
05Return reviewable work

Our engineering standard

Built around evidence.
Designed for reality.

We avoid promises that only work on a slide. The right solution is the one that performs against your task, your data and your deployment constraints.

Evaluate before optimizing.

Establish a task-level baseline, then measure whether training, quantization or runtime changes improve the result that matters.

Choose the right level of intervention.

Use prompting, retrieval, adaptation or deeper training according to evidence — not because the most complex option sounds impressive.

Design for the target environment.

Data boundaries, hardware, latency, operating model and governance are part of the product design from the beginning.

Improve the whole system.

Model quality matters. So do orchestration, tools, reliability, observability and the human decisions around the system.

Start with the workload

What should your AI be able to do?

Tell us about the model, the task and the environment. We’ll start with the requirement, then work toward the right engineering path.

Discuss an AI program ↗