Inferable.ai

Start by dropping Inferable.ai in alongside your services and put it to work the same day. You don’t rebuild pipelines or learn a new framework: point the agent at functions you already trust and describe their inputs/outputs. Use your stack—Node.js, Go, or C#—and register handlers that read logs, hit internal APIs, run migrations, or update configs. The agent calls those functions directly, returns typed results, and chains them into reliable procedures. Everything executes inside your network, so credentials and data never leave your environment.

Automate operations with guardrails. Create an incident responder that diagnoses a spike, steps through a plan, and only acts when you say so. It can fetch metrics, compare recent deploys, tail logs, trigger a rollback, and open a ticket. Insert an approval checkpoint with a single API call; the agent waits indefinitely, resumes where it left off, and records the full trail. Apply the same pattern to change requests, access grants, or costly batch jobs—any workflow that sometimes needs a human to review before moving on.

Accelerate day‑to‑day engineering. Wire the agent into your repo to triage pull requests: summarize diffs, run targeted tests via your existing scripts, query the flake database, and comment with actionable fixes. For releases, have it assemble notes from commits and issues, generate a checklist, and call deployment functions to stage, verify, and promote. For data work, let it backfill a table, validate row counts, compare checksums, and notify owners on completion—all using code you already have. more