Scoped, revocable permissions
Wired into your warehouse and tools, with access you control.
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- Agentic AI
Agentic AI & automation
AI agents that do the work, not just describe it.
An agent is only useful if it can read your real data, decide something and act inside your tools. We build that: grounded in your warehouse, scoped to actions we can audit, and monitored in production rather than demoed once and abandoned.
- Grounded in your data
- Audited actions
- Human in the loop
What you get
Agents you can let near real work.
Approval gates
The agent proposes, a human approves, or it acts within set limits.
Every decision logged
Inputs, reasoning and outcome logged; every change can be rolled back.
Quality as a metric
Evaluation harnesses, so better or worse is measured, not guessed.
What we build
Agents for real work.
AI bid & budget management
Agents that draft campaigns, analyze accounts and adjust bids inside your limits.
Learn moreRAG for enterprise data
Cited answers over your documents and data, respecting who can see what.
Learn moreFine-tuning for ad copy
On-brand ad copy at volume, with platform limits and claims checked in code.
Learn moreReport presenter agent
A meeting agent that presents your performance report on the call and captures follow-ups.
Learn moreThe problem
Why most AI pilots never reach production.
The pattern is consistent. A pilot impresses everyone in a controlled demo, then dies on contact with reality. It has no access to the systems where the work actually happens, so a human still does the last mile. It cannot explain why it did something, so nobody will let it touch a live budget. And there is no evaluation harness, so no one can say whether this week is better or worse than last. The blocker is almost never model capability. It is grounding, permissions and measurement.
What we build
What's included.
- Agents wired into your warehouse, ad platforms and internal tools with scoped, revocable permissions
- Retrieval layers, so every answer is grounded in your documents and data rather than model memory
- Action boundaries with approval gates: the agent proposes, a human approves, or it acts within defined limits
- Full audit logging of every decision, its inputs and its outcome
- Evaluation harnesses, so quality is a tracked metric, not an impression
- Cost controls and monitoring, because token spend behaves like infrastructure spend
How we work
How we run it.
Step 1
Pick a workflow with a measurable cost today, so success is provable
Step 2
Ground the agent in real data and run it read-only alongside your team first
Step 3
Compare its decisions against what your team actually did, and tune until they agree
Step 4
Grant limited write access with approval gates, then widen the boundary as it earns trust
What we use
Tools, platforms and methods.
- Anthropic Claude
- OpenAI
- LangGraph
- Python
- Vector databases
- Snowflake
- BigQuery
FAQ
Common questions.
What is the difference between an AI agent and an automation script?
A script follows a path you defined for conditions you anticipated. An agent decides which steps to take based on what it finds, which is valuable exactly where the decision space is too large to enumerate. If your process genuinely is a fixed decision tree, a script is cheaper, more predictable and the right answer. We will tell you when that is the case.
How do you stop an agent doing something expensive or wrong?
Hard limits in code rather than instructions in a prompt. Agents get scoped credentials, explicit caps on what they can change, and approval gates on anything above a threshold. Every action is logged with its reasoning and is reversible. A prompt asking a model to be careful is not a control.
Which of our processes are actually good candidates?
Work that is high-frequency, decision-heavy and already data-driven. Campaign pacing checks, anomaly triage, report assembly and commentary, and retrieval over internal documentation all qualify. Poor candidates are low-frequency, high-stakes judgment calls where the value is the human accountability.
Do we need our data in order before starting?
To a degree, and this is the honest constraint. An agent reading unreliable data will confidently produce unreliable output. You do not need a perfect warehouse, but the specific data the agent depends on has to be trustworthy. That is often the first phase of the work rather than a prerequisite you handle alone.
Go deeper
Guides from our engineers.
Guide
AI Agents for E-commerce: From Support Chatbots to Autonomous Ad Optimization
AI agents in e-commerce go far beyond chatbots. Here's a practical look at where autonomous agents genuinely help and where human judgment still wins.
Read the guideGuide
RAG Explained: How Retrieval-Augmented Generation Improves Business AI Search
RAG is the technique that stops AI assistants from making things up about your business data. Here's a clear, non-hype explanation of how it works.
Read the guideRelated
Related services.
AI readiness appraisal
Which AI use cases your data can support, which are worth it, and what to fix first.
Learn moreAI-led companies
RAG, agents, evaluation harnesses and LLM red teaming for production AI.
Learn moreEngineering AI workflows
Two weeks on Copilot, Cursor, agents and RAG, ending with a shipped project.
Learn morePick a workflow to automate.
Tell us about a workflow with a measurable cost today. We'll tell you whether an agent is the right answer, or a simple script.