MARKET RESEARCH

Benchmark Watch Competitive Airtable Tracker

Monitors named benchmark datasets in your vertical for new leaderboard-style releases and derivatives on Hugging Face, scores each against your current baseline.

CategoryMarket Research
Enginesim
Difficultyadvanced
Triggerschedule
Steps6
Setup~25 min

How it runs

The automated pipeline, trigger to output.

  • TriggerWeekly schedule
  • ActionQuery Hugging Face for benchmark versions and derivativesHugging FaceHugging Face
  • ActionFind related results and papers via ExaExa
  • LogicScore relevance and dedupe
  • ActionUpsert findings into Airtable trackerAirtableAirtable
  • OutputSend net-new summary to SlackSlack

What it does

Keeps a living record of the benchmarks your team competes on. It watches Hugging Face for new versions, splits, and derivative datasets tied to your tracked benchmarks, uses Exa to find any accompanying results or papers, scores each finding's relevance against your current baseline, and upserts a structured row into an Airtable tracker so the competitive picture stays current without manual upkeep.

When to use it

For ML teams that benchmark against specific public datasets and need to know the moment a new variant, harder split, or competing result appears — and want it captured in a shared, queryable table rather than a chat thread.

How it works

  1. 1A weekly cron triggers the watch.
  2. 2Hugging Face is queried for new versions and derivatives of each tracked benchmark.
  3. 3Exa pulls any related results, leaderboards, or papers.
  4. 4A logic step scores each finding's relevance and dedupes against existing rows.
  5. 5Relevant findings are upserted into the Airtable competitive tracker.
  6. 6A summary of net-new entries is sent to the team via Slack.

Set it up

What you configure once, before turning it on.

  1. 1
    Connect Hugging FaceModels, datasets, spaces — the open-source hub.
  2. 2
    Connect ExaNeural search across the web.
  3. 3
    Connect AirtableBases, tables, views, automations.
  4. 4
    Connect SlackChannels, DMs, threads, mentions.
  5. 5
    Set each agent's modelWe leave models unset so you pick the tier — fast + cheap, or top-quality.
  6. 6
    Tune it to your dataEdit the prompts, filters, and field mappings so it matches how your team works.
  7. 7
    Test, then turn it onRun once against a sample, confirm the output, then enable the trigger.

Run this workflow in your colony.

14-day trial. No DevOps. No Sales call. Provisioned in under a minute.