Lower inference costs without changing your model.
Every prompt, retrieved document and chat turn your product sends costs tokens. Blackbox trims them before they reach your LLM — up to 71% fewer in our current test workflow, with the same output.
Your AI bill grows with every token you send.
As usage grows, so does the context behind every request — and most of it is repetition the model doesn’t need.
Stuffed RAG context
Retrieval returns overlapping chunks, old versions and near-duplicates. You pay for all of them.
Chat history re-sent every turn
Long conversations resend everything said so far, on every single message.
Agent loops and tool output
Agents pass bulky tool results between steps, and each step pays for the full payload again.
Bloated system prompts
Instructions grow over time, with the same rules repeated in different words.
Same answer. A fraction of the tokens.
Three common workloads — retrieval, support chat and an agent step — before and after Blackbox.
- Repeats removed✓
- Structure kept✓
- Signal kept✓
Illustrative example data. Character counts are measured on the text shown here. The 71% figure comes from our internal test workflow, not this demo.
Better margins without rebuilding anything.
Margins that grow with usage
Lower cost per request means more of each new customer’s revenue stays with you.
Keep your model and stack
Blackbox isn’t tied to any provider. Keep the model, vendor and prompts you rely on.
Output preserved
In our current testing, answers stayed the same. Your benchmark shows it on your own data.
Room to grow
Leaner inputs leave more of the context window for what actually matters.
From sample to production in three steps.
No long procurement before you see a number. Start with a free benchmark on your own data.
Free benchmark
Send a representative sample of prompts or conversations. We show your token reduction and a side-by-side of the outputs.
Pilot on one workflow
Run Blackbox on a single live workflow, agreed with your engineers, and measure it against your baseline.
Roll out
Extend to more workflows once the pilot proves out. Pricing is agreed after the benchmark, based on your volume and the savings we show.
Blackbox sits between your application and your model.
Your app sends its request as usual. Blackbox prepares a lean version, the model answers, and the response returns to your app. The exact integration is scoped with your engineers.
- Prompts
- Retrieved documents
- Chat history
- Tool outputs
- Removes repetition
- Keeps what matters
- Preserves structure
- Hosted AI APIs
- Open-source models
- Your own models
- Same answer
- Fewer tokens paid for
Questions teams ask first.
Do we have to change our model or provider?
No. Blackbox prepares the input for whichever model you already use.
Will answer quality drop?
In our current test workflow the output was the same. Your benchmark includes a side-by-side of outputs with and without Blackbox so you can judge on your own data.
What do you need for the benchmark?
A representative sample of the requests your product sends — prompts, retrieved context, conversations or agent steps. Remove or mask anything sensitive first.
How is it priced?
Pricing is agreed after the benchmark, based on your volume and the savings it shows. You’ll know the numbers before you commit.
Is our data kept?
Data handling, confidentiality and deletion are agreed with you before anything is shared.
What would Blackbox save on your workload?
Send a sample. Get your own token reduction and a side-by-side of the outputs — free.