/ solutions / ai & automation

TextQL for AI & Automation One Context for Every Agent

Claude, ChatGPT and your own agents answer from the same definitions and permissions — and every run is logged with who asked and what it touched.

Book a demo Explore solutions
in production at BlackstoneScale AI
soc 2 type iiruns in your cloudevery query logged

An Operating System for Enterprise AI

Put every assistant, agent and automation on one ontology your team owns — the same context and permissions for each, and a record of every run.

Connected solutions + automations

    • Context over MCP
    • Shared definitions
    • Permissions carried through
    • Every run logged
    • Signals on every thread
    • Audit export to S3 or OTLP
    • ACUs per person
    • Cost per playbook
    • Reclaimable tokens
    • Eval sets across models
    • Retired-model checks
    • Your cloud or air-gapped
[ agents in production ]

“I’ve been surprised often by its capability. I asked it to make me a “playbook” — give me a runbook for solving a problem. It turns out TextQL has a feature called Playbooks. It knew about its own capabilities and configured a scheduled playbook itself. To this day, every Monday morning, I get the results of that question in my email.”

Rob Wisniewski · CTO, Credit & Insurance Technology, Blackstone
Watch the story

  1. TextQL collapsed the gap between question and answer — across petabytes of data, in seconds.

    tables made queryable in plain English
    1000s
    of rows reasoned across by Ana
    Billions
    to schedule a recurring analytics workflow that runs itself
    1 prompt
    Read the Blackstone story
[ your first answer ]

Bring the Workflow You Want an Agent to Run

Pick one — the Monday report, the question every executive asks, the assistant that keeps guessing. We connect inside your cloud and show your team the agent, the context it reads and every run it logs.

Book a demo Talk to an expert
  1. 01 Pick the workflow A 30-minute call to choose the workflow and the sources behind it
  2. 02 Connect in your cloud We connect inside your cloud — your data is read in place, never copied out
  3. 03 Review every run Your team reviews the runs, the SQL and the context before anyone relies on it
questions ai teams bring first
  • Can Claude answer from our definitions instead of guessing? Enterprise AI Context
  • Which agent runs touched customer PII? Agent Governance & Audit
  • Which playbooks cost the most and go unread? AI Cost Management
  • Does the new model pass our eval set? Model-Agnostic Deployment
[ the platform ]

The Platform, in Your Team’s Terms

Three layers, one deployment. Each is a page of its own when you want the detail.

read in placeone contextcrm · erp · wikisTextQL · Your Ontologyagents + appsAGENTS + APPS · RUN BY ANA
/ connectors Every Source Your Agents Need Warehouses, SaaS apps, Slack and your docs — read in place, under the roles you already manage. / ontology Context as Code, in Your Repo Definitions, playbooks and permissions as reviewed files in your own repository — the one context every model and agent reads. / agents Agents You Can Audit Agents run on a schedule or on demand; every query is logged with who asked, and every thread is judged for signals.
[ security ]

Built to Pass Your Security Review

SOC 2 Type II Audited annually. The report is available on request through the trust center.
Your Permissions, Enforced Row- and column-level access from your identity provider — if a person can’t see it, their agent can’t either.
Your Cloud, or Air-Gapped Deploy in your VPC or fully disconnected. Your data never leaves your environment.
Never Trained on Your Data Your data is not used to train models — ours or anyone else’s.
/ soc 2 type ii/ hipaa/ gdpr/ your vpc or air-gapped
Visit the trust center
[ faq ]

What Teams Ask Before They Start

Talk to the team

Yes. Assistants reach TextQL over MCP and answer from the same ontology — definitions, context and permissions — as Ana and your own agents.

[ try textql ]

Bring Us Your Hardest Problem