Route AI within budget.
For teams shipping AI applications
Build a gateway that sends each request to a model suited to the job. Teams set their quality, latency, and budget rules, then see how routing choices affect the service they deliver.
Where AI helps. The product serves AI model requests. Routing policies use measured task performance and customer-defined constraints.
- Set routing policies for each task
- Follow the model choice behind a request
- Compare spend against observed performance
A way to earn: Charge by request volume, with paid routing controls and team reporting.
A focused first release: Connect two model providers with configurable policies and request-level traces.
The customer leaves with: Different model routes for different jobs, with cost, latency, and fallback decisions visible per request.
Find answers with sources.
For teams searching large internal document collections
Build a search product that gives staff the passages they need to resolve a question. Pair semantic retrieval with concise, cited answers so a useful result can be checked against the underlying document.
Where AI helps. AI retrieves relevant passages and drafts answers grounded in them. Missing or conflicting evidence stays visible.
- Search by meaning as well as keywords
- Check every answer against cited passages
- Respect the reader’s document access
A way to earn: Sell team subscriptions based on users and indexed document volume.
A focused first release: Search one profession’s document collection with permission-aware, source-linked results.
The customer leaves with: A concise answer with the relevant passages and links to the current source documents.
Fit AI to your hardware.
For machine-learning engineers
Build a workbench for comparing smaller model variants on the tasks a team actually ships. Help engineers decide which size and speed gains justify the measured change in quality.
Where AI helps. The workbench optimises and evaluates AI models. Measured task results guide the choice of deployable variant.
- Measure memory on the target setup
- Evaluate the tasks that matter to the customer
- Export the candidate and its comparison report
A way to earn: Sell team workspaces and charge for optimisation and evaluation runs.
A focused first release: Compare quantised variants of one model family against a customer-supplied evaluation set.
The customer leaves with: A comparison showing which variants fit the target hardware and what each changes in quality and latency.
