Auditable agent context.
60% fewer tokens.
Until now you had to choose: summaries are cheap but nobody can check them, full transcripts are checkable but expensive. PAIRL, the context format for business conversations between agents, gives you both — every excerpt copied word for word and verified, every line with its speaker, and around 60% fewer tokens than the raw history.
Officially recognized research project in Germany — certified and publicly funded under the German Research Allowance (Forschungszulage), assessed by the BSFZ federal certification body.
We run on it ourselves: our in-house agent stack and our own products plinio® GEO and plinio® TL route their LLM traffic through the PAIRL Gateway.
Summaries lose who said what. PAIRL keeps it.
What a summary does to an agent conversation, and how PAIRL makes the same context smaller by quoting instead of rewriting. Under a minute, no sound needed.
Try it with your own text60% smaller than the transcript. More faithful than a summary.
Summaries save tokens but rewrite what agents said — afterwards nobody can tell what is original and what the model filled in. PAIRL cuts just as much context by quoting instead of rewriting: checked verbatim excerpts with the speaker attached. Your bill goes down, and when someone asks “why did the agent do that?”, you still have the answer.
Nothing invented
The model decides what matters; plain code copies it from the source byte for byte and drops anything it can’t find. The guarantee lives in code, not in a prompt — it holds whichever model does the work.
Who said what, kept
Every turn carries its speaker, set from your own metadata — never guessed by a model. That is exactly where summaries fail in multi-party conversations: they mix up who committed to what.
60% fewer tokens
On long agent conversations, around 60% less context goes to your provider — with the same coverage as an LLM summary. Every response reports savings and fee, so the cost side is just as easy to audit.
We published the benchmark — including what failed
Every claim on this page comes from benchmarks we registered before running them, on agent business conversations and real multi-party interviews. Methods, prompts, data and the negative results are public.
Read the methods and dataWho said what, answered right
On 100 multi-party interviews, named speaker markers beat LLM summaries on attribution — and beat handing the model the full, uncompressed transcript by 11.9 points.
Tokens cut, coverage kept
60% fewer tokens end to end with the same coverage as an LLM summary — and about half as many invented details in the answers that follow.
Fewer errors than dropping tokens
At the same reduction, token-dropping compression (LLMLingua-2) loses negations, numbers and qualifiers. PAIRL produced 4.4× fewer material hallucinations.
Measurable cost of being checkable
At matched reduction, the checked format answers as well as an unchecked one. The difference: only the checked one can be audited afterwards.
Scope: one generated agent-business corpus (79 sessions) and one public interview corpus (100 transcripts), three runs each, one judge model. Limitations are listed in the paper.
A protocol for agent conversations that hold up in review
Your existing OpenAI and Anthropic workflows keep running. PAIRL standardizes how agents hand context to each other: names, numbers, dates and sources travel unchanged, history travels as marked verbatim excerpts, and a quote can always be told apart from a paraphrase. Threading, origin and cost ride along in every message.
Your keys. Your models. Your provider.
The protocol is open and published. We bill only a fraction of your documented, measured savings — and nothing at all when a request doesn’t compress profitably.
Three steps to auditable, smaller context
Connect PAIRL
Point your application at the gateway with a single URL change. Run with compression off to capture your baseline. No risk, no surprises.
Compression runs automatically
No configuration. PAIRL condenses history into checked excerpts where it pays off and passes requests through unchanged where it doesn’t. Always fail-safe — your calls never break.
See what you saved
Every response carries savings data. Your dashboard shows token reduction, cost saved, and the PAIRL fee — per request, per day, per month. Export anytime.
You only pay when we save you money
Keep your provider keys and models. PAIRL charges only a fraction of your verified savings — zero fee when a request doesn’t compress. No platform tax, no seat fees, no setup costs.
- Start immediately, no commitment
- Fair-use guardrails included
- Zero fee when no savings occur
- Everything in Basic
- A larger share of your savings stays with you
- Everything in Pro
- A larger share of your savings stays with you
- Everything in Scale
- Optional self-hosted deployment
- Custom onboarding and SLA
Credits are pre-purchased and consumed only on verified savings. No surprises on your bill.
In a regulated industry? Keep your AI traffic on EU soil.
For finance, healthcare, the public sector and other compliance-bound teams, we deploy a dedicated PAIRL gateway in sovereign EU infrastructure — full data residency, your own isolated environment, and the same one-line setup. Your data stays in your jurisdiction, and you can show what your agents based their answers on.
See what your agent gets to read
Paste a conversation and see what PAIRL keeps: verbatim excerpts tied to who said them — names, figures and decisions in, small talk out. Nothing is reworded. Real engine, running live.
The built-in example is free and unlimited. To run your own text, confirm your email and get 5 runs — once, no account needed. Demo engine: Google Gemini, routed through the PAIRL Gateway. In production PAIRL is provider-agnostic: Anthropic, OpenAI and Google, with your own keys. Inputs are forwarded to Google, so please don't enter personal or sensitive data. For production use, sign up at console.pairl.dev.
Open protocol. Drop-in libraries.
PAIRL is an open specification. Encode, validate, and decode messages natively in your own stack with the reference libraries — same format the gateway speaks, no lock-in.
The open libraries handle the PAIRL format itself. The hosted gateway adds advanced compression techniques of our own that go well beyond what a self-hosted implementation can reach — substantially more token savings, with the same one-line setup. See how it works.
Everything you need to evaluate PAIRL
Is PAIRL production-ready?+
What does “auditable” mean here, and what doesn’t it?+
Why not just summarize the history, or drop tokens?+
How long does integration actually take?+
How much can I realistically save?+
How is PAIRL different from TOON or Headroom?+
- TOON works at the syntax layer — a leaner, lossless way to spell JSON, best for uniform tables (~30–60% vs JSON). A data format you reach for in your own payloads.
- Headroom works at the runtime layer — a proxy/middleware that compresses whatever bytes flow to the model (tool outputs, logs, RAG chunks) and can fetch elided detail back on demand.
- PAIRL works at the semantic layer — it defines what’s actually being communicated. Facts that must stay correct (names, numbers, dates, sources) are kept losslessly, and conversation history is carried as verbatim quotes with marked omissions — compressed by cutting, not by rewriting. It also carries conversation threading, provenance, and cost as part of the message.
What if PAIRL can’t compress a request?+
Do you train on our data?+
Do I have to change my provider or models?+
The protocol is open — what does PAIRL add?+
Auditable context at 60% fewer tokens
One URL change. Verbatim excerpts and named speakers on every request — and a smaller bill. Nothing to pay until PAIRL saves you money.