DataForge Platform
Talos
The AI Control Plane for Data Platforms
Talos allows teams to interact with DataForge through natural language while operating entirely within the constraints defined by Alloy and Ember.
It lowers the barrier to building and operating data pipelines without introducing new patterns, risks, or ambiguity into the platform.
When Automation Is Almost Right
AI works well when tasks are low risk or easily reversible. Data platforms are neither.
When an AI system is correct most of the time but wrong often enough, teams either ignore it entirely or over-trust it. In data systems, that gap creates silent errors, compounding complexity, and outcomes that are expensive to unwind.
"In data platforms, being wrong twenty percent of the time is not acceptable."
The problem is not the AI.
The problem is asking automation to operate inside systems that were never designed to be understood or constrained. When structure, intent, and execution are implicit, automation has no choice but to guess.
Unconstrained Platforms
- AI infers structure from incomplete context
- Errors propagate quietly through dependent pipelines
- No validation layer to stop bad output
- Outcomes depend entirely on prompt quality
- Hard to audit, trace, or reverse
DataForge + Talos
- Structure is enforced by Ember — not inferred
- Errors are caught immediately before anything is written
- Every action passes the same validation as human-written pipelines
- Outputs are deterministic and fully auditable
- Safe to operate at enterprise scale
Talos works because it operates inside the same constraints that govern every human-written change on the platform.
Natural language becomes a safe interface only when the system underneath is deterministic, explicit, and enforced by design.
Capabilities
Three Ways Talos Works
Talos routes every prompt to the right specialized agent automatically — no commands, no syntax to learn.
Build
Describe what you need. Talos creates it.
- Connects to any database table and builds a pipeline to read from it
- Adds calculated columns, table joins, and reporting tables in plain English
- Schedules pipelines on request
- Every change is validated by the platform before it takes effect
Find Data
Describe the data you are looking for. Talos finds it.
- Searches across all your connected databases by meaning, not just by name
- Matches what you are looking for to the actual tables and columns in your databases
- Shows you if the data you need is already being ingested
- Recommends how to join newly found tables together
Convert
Upload SQL or PySpark. Talos converts it.
- Reads every query and sub-query in your file
- Maps your tables to your existing database connections
- Plans the transformation logic column by column, step by step
- Builds all the pipelines, calculated fields, joins, and reporting tables needed
Talos in Action
Talos allows users to express requirements in natural language while the platform enforces structure, validation, and execution.
This short demo shows how a plain-English request is translated into platform changes and executed through the same enforced pipeline architecture — without bypassing any constraints.
Every step in the workflow surfaces for review before anything is written — so teams can see exactly what will be created and approve it before execution begins.
Talos succeeds because it operates inside the same system that keeps large teams aligned.
SQL & PySpark Migration
Migrate Existing Pipelines Without Rewriting Them
Upload a SQL or PySpark file. Talos reads every query column by column, maps your tables to your existing database connections, and builds the complete set of pipelines, calculated fields, joins, and reporting tables — in a structured, step-by-step sequence you review and approve before anything is written.
Your existing SQL — unchanged. Upload the file and Talos handles the rest.
Every column planned, validated, and reviewed before it's written to the platform.
The same validation checks that apply to human-written logic apply to every change Talos creates.
There is no shortcut path for AI. If a transformation is invalid, references a column that does not exist, or violates a structural rule, Talos catches it — and surfaces a correction — before execution begins.
Built on Structure, Not Guesswork
Talos works because the platform underneath it was designed to scale safely.
Alloy enforces a single execution architecture — every pipeline follows the same layered model, so there is never ambiguity about how something will run.
Ember defines all logic in explicit, prescriptive structures — so every transformation, join, and calculation is declared upfront, not improvised at runtime.
Talos builds on that foundation by lowering the interface cost, not the standards. Teams express intent in natural language. The platform enforces exactly the same constraints it would enforce on a human developer.
What this means in practice
- Talos goes through the same validation every human developer goes through
- When something does not conform, the platform rejects it — for AI and humans alike
- No special path, no bypass mode, no reduced validation for AI-generated changes
- Every change is traceable, reversible, and queryable like any other
Why this matters for AI
- AI cannot write logic that bypasses platform constraints
- Errors are caught before they reach production — not after
- Teams can review and approve every change before it runs
- The platform stays predictable no matter who — or what — made the change
This is what makes AI usable in environments where correctness matters more than novelty.
When logic is declarative and structure is enforced, automation becomes reliable. Talos is designed for platforms built on that foundation.
How Talos, Ember, and Alloy work together
Talos is not an autonomous system making decisions on its own.
It translates intent into structured actions that must pass the same validation applied to every change in the platform. This makes Talos practical in large, complex data environments where predictability and correctness matter more than novelty.