Bring every AI agent trace, cost, and outcome together — and turn it into performance for the team building it and ROI for the team funding it. One system, in real time.
Same data. Two languages.
One source of truth. Your AI lead chases the broken span. Your CFO sees what it costs the company. Both are looking at the same session — through their own lens.
The shape of the problem
AI margin per account this month?
Why did escalations spike Tuesday?
Did the Tuesday prompt change ship?
ROI by cohort for the board deck?
Number of failed flows this week?
Customer-by-customer AI savings?
Every team — CFO, CS, sales, PM — pings the AI engineer for numbers about the AI. They drop their roadmap to write one-off SQL. The signal makes it to the boardroom days late, if at all.
One person should not be the company's question-answering API.
Today's release ships into a black box. By the time anyone notices the regression, three more releases have layered on top. Iteration is bottlenecked by the speed of your dashboards.
Faster iteration beats bigger models. That's the compounding advantage.
Your spans pass. Your error budget is green. And your AI just confidently invented a refund policy. The technical health of a request is not the business quality of the answer.
Three layers. Span status. Conversation outcome. Business quality. All required.
What sits inside the platform
Ledda is not a feature pile. It is a coherent system designed so the person debugging the trace and the person presenting the board deck are looking at the same numbers.
Cost, quality, and outcomes joined at session, account, and customer — not just per trace. The numbers your CFO defends and the numbers your AI lead chases come from the same row.
Any decision-maker can ask a question and receive a chart with a written interpretation in real time. No data team in the middle. No SQL. No queue.
Production signal lands fast enough to shape the next release, not the next quarter. Builders push, watch, adjust — inside a single week.
Signal reaches the people who never log in. Weekly digests for execs. Slack pings for on-call. Email summaries for ops. Each shaped to the audience.
Accept-any-format ingestion. UI-based OTEL attribute mapping. Whatever your stack emits — OpenInference, Traceloop, Vercel AI SDK, custom — Ledda speaks it.
Span status. Conversation outcome. Business quality. Hallucinations stop hiding behind a green "200 OK". You finally see which kind of failure you have, and who needs to act on it.
The AI-assisted insight layer
Anyone in the company can interrogate the production data. Ledda translates the question, runs it across every trace, and replies in real time.
Three accounts are net-negative on AI margin in April.
orbit-health, valley-credit and outpost are running 23% above target cost-per-session. Together they are −$4.8k against forecast — driven by retry loops in the verify_identity flow.
Who lives in Ledda
Dev tools sell to one buyer. Ledda sells to a team — and the buying committee is already aligned by the time procurement gets the deck.
Every team used to ping me for AI numbers. Now CS, finance, and the CEO have their own answers, in real time, on the same data I run. My calendar belongs to my roadmap again — and the loop from production signal to shipped fix is days, not weeks.
Margin per account, per cohort, per session — defended with the same numbers my AI lead chases.
We stopped arguing about whether the AI was working. We started arguing about which accounts to lean into.
Alerts ping us the moment outcomes drift. We open the trace, talk to engineering, and contain it before it hits a ticket.
Production data lands on the same canvas builders use to ship. The compounding advantage is speed.
Bring it together
Onboard any AI agent in hours. See your first signal in real time. Free during evaluation — no credit card.
OpenTelemetry native · accept-any-format ingestion · hours to onboard
Frequently asked
Something else. LLM observability is a developer-debugging frame that speaks only to the engineer. Ledda is an AI Operations Platform — the canvas where builders measure performance and execs measure ROI on the same data. Observability is one input; the platform is the entire team's working surface.
Those are dev tools — eval harnesses, prompt iteration, debugging. They serve the developer staring at their own code. Ledda serves every person responsible for the AI after it ships: the AI lead, the CTO, CS, ops, and the exec under pressure to defend ROI. Different category, different buyer, different daily user.
A unified data model where cost, quality, and outcomes are joined at session, account, and customer level. Ingestion that accepts whatever your stack emits. An AI-assisted insight layer for plain-language questions. Real-time alerts and reports as a distribution layer. And a three-layer error taxonomy — span status, conversation outcome, business quality — so silent failures stop hiding behind 200 OK.
Hours, not weeks. Accept-any-format ingestion plus UI-based OTEL attribute mapping means whatever your agent emits — OpenInference, Traceloop, Vercel AI SDK, custom — is parsed without code changes. Most teams go from sign-up to production traces in a single afternoon.
That is the entire point. CFOs ask Ledda 'which accounts are losing money on AI?' in plain language and get a chart with a written interpretation in real time. CS leads see hallucinations surface before tickets land. The platform is built so the AI engineer is no longer the company's human ETL.
Usage-based on ingested units, with a generous free tier so you can put production data in and feel the platform before you buy it. Talk to us if you have a high-volume stack — we shape pricing to your shape.
No. Most customers keep their dev-loop eval tooling and add Ledda for production operations. The two jobs are different. Ledda is built to live alongside the rest of your stack, not to replace your prompt iteration workflow.