Services / 01
Put your knowledge to work.
Turn documents, information, and repeatable decisions into useful tools with clear limits and human oversight.
The starting point
AI implementation
Typical problems
- Finding answers across scattered documents
- Reviewing or classifying unstructured information
- Testing whether an AI approach is reliable enough
What we can deliver
- A defined use case and evaluation criteria
- A working prototype connected to relevant data
- Evaluation results, review controls, and operating documentation
Fit & constraints
Define the work.
Understand the limits.
A good fit when
You have a specific information problem, access to the underlying data, and people who can judge the quality of an answer.
What to consider
AI output needs evaluation. We design for uncertainty, sensitive data, and the cost of an incorrect answer.
The process
Make each step reviewable.
Define the task and its failure modes
Connect data and establish a baseline
Evaluate, refine, and plan operation
Related reading.
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Anthropic's Opus 5.5 release lowers token prices. Its preserved-thinking rules also make conversation history a migration concern for custom agents.
GPT-6 Sol and Luna Bring Lower Prices to Agent Work
OpenAI releases GPT-6 Sol and Luna with lower prices and a shared million-token context window. Compare costs, caching and migration constraints.
Anthropic's Agent Metrics Put Review Delays in Focus
Anthropic proposes metrics for AI research and agent oversight. What coverage, review latency and escalation rates can tell an engineering team.
Have something in mind?
If you’d like to work with us, tell us a little about the problem and what you’re exploring.
