Multi-model AI delegation
LogK gives users one dashboard to select ChatGPT, Claude, Gemini, and other models together, preview expected spend, dispatch the task, and compare or aggregate the results.
Live workspace Expected spend 18.4 credits The homepage now leads with an actual software surface instead of an abstract mockup.
Solution
Current products mostly optimize for developer gateways, single-chat access, or observability. LogK is built as the decision layer above the models: choose them, compare them, price them, and verify them in one product.
Users can choose one model or several model cards at once, depending on whether they want a single answer, side-by-side comparison, or aggregated synthesis.
LogK estimates expected spend before the request runs so users can make an intentional tradeoff between price, breadth, and answer quality.
The system can ask clarifying or verifying follow-up queries when models disagree, confidence is low, or the answer needs one more pass.
Product surfaces
The interaction shifts with the task: choose your model stack, dispatch work across providers, then inspect and verify the result.
Choose one model for speed, several models for comparison, or a recommended stack for writing, coding, reasoning, or research-heavy queries.
LogK fans out the request across selected models, tracks cost centrally, and keeps the run structured even when providers, models, or follow-up steps differ.
Instead of forcing users to trust one output, LogK shows model differences, confidence gaps, and verification passes so the final answer is easier to trust.
Highlight disagreement across providers before synthesis
Trigger verifier prompts when confidence is low
Produce one aggregated answer with cited model traces
Technology
The system is not just another provider switch. It is a routing and decision layer that helps users choose models intelligently, manage credit spend, and keep sensitive context out of unsafe paths.
Map the user request to a stack of candidate models, privacy rules, expected costs, and optional verification steps before a provider call is even made.
Suggest the right model or stack for the current prompt, then route the request based on price, quality, availability, or workflow presets.
Apply redaction or blocking rules before dispatch, and keep credit-based billing visible at the run, team, and account level.
Company
LogK exists because the model landscape keeps expanding while user workflows stay fragmented. We want interacting with many AI services to feel coherent, economic, and trustworthy.
Users should not need seven tabs and manual copy-paste to get the best answer from the current AI landscape.
The market already has developer gateways and observability tools. LogK is focused on the user-facing layer: model selection, delegation UX, answer comparison, and trust.
Contact
LogK is designed for teams and advanced users who want to delegate across models with clear pricing, privacy controls, and stronger trust in the final answer.
