Most AI projects fail at the integration, not the model.
We put language models, document pipelines and prediction into systems you already run, wired to your data with the guardrails and fallbacks that production needs.
What we build
- 01LLM Fine-Tuning
- Tune open-source models like Llama-3 or Mistral on proprietary organizational data, deploying inside private hosting nodes.
- 02Predictive Forecasting
- Train statistical regressors and neural networks to project monthly sales demand, warehouse inventory, and customer churn.
- 03AI Safety & Compliance
- Set up content moderation layers, guardrails, and compliance screening to ensure models conform to strict data-protection laws.
What usually goes wrong
On the left is what teams tell us when they get in touch. On the right is how we deal with it.
Extremely high training and GPU computing costs.
We optimize training runs, compile models using TensorRT, and fine-tune lightweight open-source models.
Hallucinations and unpredictable outputs breaking data rules.
We wrap models in strict validation schemas, guardrail checking layers, and structured JSON formats.
Data privacy concerns exposing proprietary company records.
We deploy open-source models (Llama/Mistral) directly within your private cloud nodes so data never leaves your environment.
How the work runs
- 01
Data Preparation
Cleaning, structuring, and labeling your proprietary data for model training.
- 02
Model Training & Tuning
Selecting and fine-tuning the optimal algorithms for your specific use case.
- 03
Production Integration
Deploying the models via scalable APIs directly into your software ecosystem.
What we work with
We pick from this set rather than starting every project with a new stack.
Python
TensorFlow
PyTorch
- OpenAI API
Hugging Face
- AWS SageMaker
How we engage
- Dedicated team
- Engineers working full time on your product, in your tracker and your standups. Best when the work runs for months and the context is worth keeping.
- Fixed scope
- An agreed deliverable, a milestone schedule and a price. Best when you know what you need built and want the risk on our side.
- Extending your team
- One or two senior engineers alongside people you already employ. Best when you have the direction and need the throughput.
Questions we get asked
Something not covered here? Ask us directly and an engineer will answer, not a sales form.
Ask a questionWe set up completely private hosting environments inside AWS/GCP VPC container nodes. Your corporate records are never processed by external public APIs and are never used to train foreign models.
Tell us what you are trying to build.
We will tell you how we would approach it, what it would take, and whether we are the right team for it.
