What we do

Design the system before a line is written.

AI Solutions ArchitectEnd-to-end ownership of the AI system
AI Infrastructure ArchitectThe system that makes the system work at scale
AI Security & Governance ArchitectCompliance and auditability baked into the design

Build it so it actually works — in production, under load, with real users.

AI Product EngineerWhen AI is the product, not a feature within one
Applied AI EngineerFrom research to production — reliably, at scale
AI Integration EngineerMaking AI outputs actionable inside existing workflows

The substrate everything sits on. AI-ready data, by design.

AI Data EngineerThe pipeline that makes models work in production
ML Data EngineerThe bridge between raw data and model-ready inputs
Data Governance & Quality LeadMaking data trustworthy enough to act on

The engineering of change — ensuring AI gets used, trusted, and embedded.

AI Strategy PartnerWhere engineering meets the executive table
AI Transformation LeadThe person who makes sure AI actually gets used
AI Enablement EngineerMaking AI accessible inside the organization

AI-native platforms built by Datawise — designed to orchestrate intelligence and unlock institutional knowledge at scale.

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← Case StudiesLLM Reinforcement Learning training

Teach your agents how to use tools

Train your agents on complex tasks

Teach your agents how to use tools
Problem statement

Even frontier LLMs struggle to solve complex tasks that require tools. While the web offers an abundance of information, there are not that many datasets for training agents to solve problems with tools. Designing datasets at scale is not a trivial task

Approach

We have a unique team of diverse scientists and engineers who uses sophisticated GenAI processes to design datasets for training LLMs to solve difficult problems. Our datasets are 100% validated proven to lift performance

Results

We are providing high end datasets to the Frontier LLM companies. Our training data follow the terminal bench tbench.ai format and go through rigorous validation and testing. In a batch of 1000 tasks one problematic training point can ruin the results. We guarantee the quality of our data.

Tech Stack
  • Tbench.ai, OpenAI, Anthropic, Google, LLama, Docker
Problem Statement
  • —Create tasks that a language model can perform with the use of tools (databases, planners, scientific software, etc)
Solution
  • —Generate task descriptions and docker files that contain the tools. Provide a grader and a solution to the problem. Validate the difficulty of the problem and prove that the LLM cannot cheat in order to hack the reward function.
Outcomes
  • —High quality datasets and trained models that demonstrate the lift in the performance
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