From idea to production‑ready agent — then watch it get smarter. Automatically.
Owlgebra AI gives you everything to go from a blank slate to a production agent that observes, evaluates, and upgrades itself — without you lifting a finger after launch.
Discover who your users really are — infer behavioral personas from interaction data to personalize agent behavior at scale.
Grade every agent response against structured rubrics, not vibes. Objective, auditable quality scores at every step.
Map the full arc of every conversation — where agents shine, where they stumble, and what to fix next.
Close the loop: agents learn from rubric scores and journey signals to get better in production, automatically.
Infer rich behavioral personas from interaction data — so your agents respond like they've known the user for years, from the very first message.
Get StartedAutomatically group users by interaction patterns, intent signals, and vocabulary fingerprints.
Assign human-readable persona labels — power user, explorer, skeptic — enriched with LLM-generated trait descriptions.
Classify incoming users into personas within the first two turns, with confidence scores and fallback handling.
Steer agent tone, depth, and tool selection based on the active persona — no prompt engineering required.
Define what "good" looks like for your use case, then let structured rubrics and LLM-as-judge scoring deliver objective, reproducible quality scores at every step.
Start EvaluatingAuthor multi-dimension rubrics with weighted criteria, pass/fail thresholds, and domain-specific descriptors.
Run scalable, calibrated evaluation using frontier models as judges — with chain-of-thought rationales included.
Route low-confidence samples to human reviewers and use their feedback to continuously recalibrate the judge.
Track rubric scores over time, detect regressions automatically, and get notified before users notice.
Map the full arc of every user interaction — where your agent delights, where it loses the thread, and exactly what to fix next.
Explore AnalyticsVisualize turn-by-turn conversation paths as Sankey diagrams — spot common routes and unexpected detours.
Identify the exact turns where users disengage, repeat themselves, or escalate — and why.
Define success states and measure how often, how fast, and via which paths users actually reach them.
Generate shareable reports segmented by persona, time range, or rubric score band.
Close the loop between evaluation signals and agent behavior — so your agents in production get measurably better with every interaction, without constant intervention.
View on GitHubCombine rubric scores, journey drop-offs, and explicit user signals into a unified improvement queue.
Kick off targeted fine-tuning runs when quality dips below threshold — no manual babysitting required.
Shadow-test updated policies against your live agent and promote automatically when rubric scores improve.
Every self-improvement cycle runs through safety checks and rollback triggers before reaching real users.
We publish our methods and release code to advance the broader AI research community.
A family of domain-specialized text encoders trained on 2.3T+ tokens that outperform general-purpose encoders 2–3× their size on e-commerce benchmarks. Released with Ecom-niverse, a 350B token open corpus.
Read paper on arXiv →Deep dive into the data curation pipeline, three-phase training recipe, and benchmark results behind the RexBERT encoder family.
Read on Hugging Face →State-of-the-art rerankers for e-commerce product relevance, released with Amazebay (6M query–product pairs) and ERESS evaluation suite.
Read on Hugging Face →A family of 17M–400M parameter encoders trained on 2.3T+ tokens, outperforming models 2–3× their size on e-commerce tasks.
February 2026Full walkthrough of the data curation pipeline, three-phase training recipe, and benchmark results.
September 2025State-of-the-art rerankers for e-commerce with open-source Amazebay dataset and ERESS evaluation suite.
January 2026Join teams shipping agents that get smarter every day.
