ProductContext that stays accurate,
powering every team agent
SignalPilot drafts your context, metrics, and evals as code and keeps them accurate, then runs a sandboxed multiplayer agent on top. Every chat improves the context, and every change is checked before agents use it.
dbt models
SlackThe loop
Four stages, one loop.
No bandwidth to run it yourself? A SignalPilot engineer does, with a 90% precision guarantee. Managed →
Ingest and draft
- SignalPilot
- We ingest your BI tools, dashboards, dbt project, and agent traces, and draft your context, metrics, and evals as code.
- Your team
- Your team curates the draft where the evidence conflicts.
Ask and act
- SignalPilot
- The agent answers in Slack, Claude Cowork, Codex, or the web, with evidence on every number.
- Your team
- Your team acts on it, or hands the next step to its own agents.
Surface issues
- SignalPilot
- Every chat is parsed for gaps and inconsistencies, clustered into suggestions with a drafted fix.
- Your team
- Your team proposes fixes and bumps them up.
Check and merge
- SignalPilot
- We rerun the affected evals on every change to dbt models, semantic models, and skills.
- Your team
- Data context admins review and approve the change.
In good company
The best data teams already work this way.
The most AI-forward data teams converged on the same architecture: semantic context, continuous evals, lineage, and sandboxed agents. SignalPilot builds it and keeps it correct for you.
OpenAIInside our in-house data agent
An agent on shared context and continuous checks.
Read the post ↗
AnthropicSelf-service data analytics with Claude
Semantic context and evals took accuracy from 21% to 95%+.
Read the post ↗- Ramp
The shape and feel of the post-AI data stack
Semantic layers, lineage, and agents that run in sandboxes.
Read the post ↗
Open standards
Your contexts are yours. No lock-in.
Every definition SignalPilot drafts is written in an Apache Ossie compatible format and lives in your GitHub repo as Markdown and YAML. Anything that reads Ossie reads your context.
1- id: contract2 ai_context: one billing subscription per contract3 datasets: [int_customer_contracts]4 metrics:5 - id: active_contracts6 ai_context: count contracts, not customers
Also as Markdown beside the model, and as dbt. In your repo, versioned with your models.
Ossieopen YAML spec · Apache 2.0
- dbt Labs
- Snowflake
- Databricks
Salesforce
Cube
ThoughtSpot- + 58 more in the Ossie ecosystem