UniversalBench connects your AI assistant to the tools and accounts you already use, including your own Google Search Console and Google Analytics, so it can read your data and take actions you approve.
UniversalBench moves computation, validation, and execution into a controlled runtime before results ever reach the model. Token reduction, safety enforcement, and verified accuracy all follow from this one architectural decision.
Every enterprise team, every funded startup, every developer paying for Claude or GPT-4 faces the same silent problem: LLMs hallucinate on data, hit token limits on context, and can't execute real code. UniversalBench pre-computes every answer before it reaches your model. Facts. Not guesses.
30 seconds. No credit card. 1,000 free executions every month , enough to run a real benchmark against your current workflow.
Claude, ChatGPT, Gemini, Cursor , any MCP-compatible client. One URL in your integration settings. Two minutes of work.
Code runs. Data queries. Search verifies. LLM routing activates. Every result pre-computed , your model gets facts, not prompts.
Connect one URL to your AI. It can execute code, query databases, search the web, call APIs, and process files before the answer reaches the model.
Every company connects UniversalBench for a different reason. The most valuable workflows are usually the ones nobody planned on day one.
UniversalBench connects your AI assistant to the tools and accounts you already use, so it can read your data and take real actions for you, only on your instruction. It works across many services.
As one example, you connect your own Google Search Console and Google Analytics, and your AI reads your search performance (queries, impressions, clicks, position) and your site traffic to diagnose issues and improve your site. At your instruction it can also submit sitemaps and request indexing.
We act only on accounts you own, only when your AI requests it. We never retain your Google data after returning it to your AI, and we never use it for advertising or to train any AI or ML model.
Exposing tools to the model and executing work inside a runtime are two different architectural choices. Here is what each one means in practice.
| Traditional MCP Server | UniversalBench Runtime |
|---|---|
| Exposes tools for the AI to call and reason over | Executes Python, search, database, and API operations before returning results |
| Raw data and intermediate steps are often sent back to the model | Computation happens inside the runtime, returning only the final result |
| Model processes most of the workload inside the chat context | Heavy work is completed outside the model, reducing token usage by up to 96.5% |
| Individual tools and workflows are exposed through MCP interfaces | One runtime provides a unified execution layer across multiple capabilities |
| Safety depends largely on tool implementation and agent behavior | Runtime-enforced limits validate code, spending, and network access before execution |
| Teams build, host, monitor, and maintain MCP infrastructure | Connect a single MCP endpoint and use managed execution services |
Traditional MCP servers expose tools to the model.
UniversalBench moves computation, validation, and execution into a controlled runtime before results ever reach the model.
Your AI sends one instruction. UniversalBench handles everything in between, auth, execution, and safety, before a single result token reaches the model.
We connect your AI assistant together, run a live execution, and make sure everything works. No technical knowledge needed.
Book a free setup call →30 minutes · Video call · Free forever
| Default ceiling | $0.50 / request |
| Hard platform cap | $50.00 / request |
| Over budget | Rejected before run |
| Surprise invoices | Impossible |
| Prompt-based guardrails | Runtime enforcement |
|---|---|
| AI asked not to break things | Broken deployments blocked before commit |
| AI asked not to overspend | Overspending is impossible |
| AI asked not to access internal systems | Internal systems unreachable by design |
Run on 20 May 2026 against the live Anthropic API using claude-opus-4-7, the current Anthropic flagship. Test data is published below. The "true" answer in every test is elementary math, verifiable in Excel, R, bash grep, or any calculator. We are inviting you to reproduce these tests yourself.
Model: claude-opus-4-7. Anthropic API direct. Input pricing $15 per million tokens, output $75 per million tokens. Each "with UB" call sends a Python-computed answer to Claude instead of raw data. Token counts pulled from the Anthropic API response usage field.
The "true" answer in every test is elementary mathematics, not a Python opinion. Sum, count, median, standard deviation, and "lines containing [ERROR]" are unambiguous mathematical operations. They produce the same result in Excel, R, MATLAB, bash grep, or any pocket calculator. Test data and reproducer recipes are saved in our published reproducibility receipts. Run the math in whatever tool you trust most. The number will match.
Token reduction depends on the workflow. Small queries save less. Bulk data tasks like the three above save 95% to 99.8%. The "up to 96.5%" claim is a conservative reference from our original public test. At Opus pricing, Test 03 alone saves roughly $40,000 per year at 1,000 queries per day, or up to $400,000 at 10,000 queries per day. Customers pay UB $0.008 per call, so the math is verifiable for their own volume.
Claude, ChatGPT, Gemini, Cursor, and any MCP-compatible client. One URL works across all of them. Switching AI providers does not require re-integrating UB.
Yes, on bulk data tasks. We have measured reductions of 95% to 99.8% across the three live tests above, all run against the current Anthropic flagship and reproducible from published data. Tasks that send small inputs to your AI will not see this scale of saving. The big savings show up when your AI is reading large datasets, log files, CSVs, or anything where Python can pre-process and send a one-number answer.
You stop, or you top up your wallet from $5 and keep going at $0.008 per call. Credits roll over. No subscription, no auto-renewal, no surprise charges.
Only what your AI explicitly asks UniversalBench to send. Credentials you save go into an encrypted vault scoped to your account. Your data is not used for training. Your AI cannot reach your internal network by default.
You see the exact reason. You fix it and try again. The validation is mandatory because that is what makes the safety claim real, but the error is always visible and actionable.
Yes. The default ceiling is $0.50 per call. You can raise it up to $50. The cap stays on. You control its size.
Stop topping up. There is no subscription. Unused credits get refunded if you ask.
Live results from the web, cited and structured. Billed per query, separate from your execution credits.
Connect any PostgreSQL-compatible database once via your vault. Read, write and search from any tool.
Built into every call by default. One URL into any MCP-compatible AI. Free to start.
Get your free API keyNo credit card. 1,000 free calls every month.
1,000 free executions/month. No credit card. Under 2 minutes to connect.
