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OSO AI Agent

Quality-first, velocity-second.

Give everyone on your team an AI-powered data team, with answers you can trust.

Explore

High quality answers with custom evals

Speed and automation don't help if they just produce wrong answers. We work closely with your team to create evaluation datasets that make sure the agent answers consistently accurately in your domain.

Which projects have the best developer retention?

Top projects by 6-month retention:

ethereum/go-ethereum94%
rust-lang/rust91%
facebook/react89%
MARTcontributor_retention_v1

SELECT project_id, retention_rate, period FROM contributor_scores GROUP BY 1, 2, 3

48,291 rows0% nullsvalues 0–100%
built from
INTERMEDIATEgithub_contributors_agg

SELECT user_id, project_id, COUNT(month) AS active_months FROM events GROUP BY 1, 2

2.1M rowsdedupedno orphans
built from
SOURCEgithub_events_v1

14.2M rows · raw GitHub webhook events · updated hourly

schema validts in bounds14 event types

Track answer provenance

Don't just trust the answers that the agent gives you. Ask the agent to walk you through the stages of the data pipeline to understand how the answer was calculated.

Which projects had the fastest contributor growth last month?
Pipeline Trace4 stages
1

Raw Query

SELECT * FROM events WHERE ts > now() - INTERVAL 30 DAY

2

Transform

Normalise by project_id, remove bot activity (42 rows dropped)

3

Aggregate

GROUP BY project_id, ROLLUP over 7-day windows

4

Answer

ethereum/go-ethereum: +14.2% contributor growth

Powered by the OSO semantic layer

Our data agent is aware of your business abstractions and semantic entities, from users and leads to revenue and sales. Just ask questions about the things you care about, and let the system handle the rest.

Semantic Entity Graph

Single shot an entire data pipeline

OSO can handle complex tasks, breaking down complex strategic questions into data engineering plans that it can execute against autonomously. Go from raw data to models to answers in a single shot without breaking a sweat.

Strategic question

What drove our churn spike last month?

Executing autonomously…23s

Identify churn events

events.type = 'cancel' WHERE ts > now()-30d

Segment by cohort

JOIN users ON signup_month, plan_tier

Join usage signals

LEFT JOIN sessions, feature_flags, exports

Rank causal factors

Logistic regression on 14 behavioural features

↳ Pro users who hit the export limit drove 67% of churned MRR last month.

Let's Get Going.

The move to data-driven execution is happening.

Get Started