AI will change how companies operate. Not by adding another chatbot.

Keelo builds AI systems for workflows where context, tools, approvals, exceptions, and judgment matter.

The model is not the strategy. The deployment is.

Discuss an implementation

[01] Thesis

General models are capable. Businesses are specific.

Models can draft, search, reason, summarize, classify, and call tools. The capability is real. But capability alone does not understand which system is authoritative, which approval matters, which exception is acceptable, or what risk the business can tolerate.

AI becomes useful when it is shaped around the workflow: grounded in context, connected to tools, constrained by permissions, measured with evaluations, and deployed where work already happens.

[02] What we believe

Context is the work

The hard part is not getting a model to answer. It is teaching the system what matters inside your business.

Autonomy needs a harness

Permissions, tools, evaluations, audit trails, escalation, and review are what make AI safe enough to operate.

Deployment creates leverage

AI becomes valuable when it enters the workflows people already use and improves through the work itself.

Model fit beats vendor loyalty

We are model-agnostic by design. We route each task — vision, reasoning, extraction, classification — to the model that fits, weighing quality against cost.

[03] Method

How the thesis becomes a working system.

Map: Understand the workflow, systems, data, approvals, exceptions, and cost of the current process.
Design: Decide what should be agentic, deterministic, human-reviewed, or left alone.
Build: Ship the model harness, workflow surface, tool integrations, evals, logs, and review paths.
Operate: Review misses, tighten controls, improve the workflow, and expand only where trust is earned.

We operate as a forward deployed team. We do not hand off documentation and leave. We build, deploy, and — where it fits the engagement — run the system with you.

[04] Systems in production

Not every system we have built. A few that illustrate the pattern.

Catalog intelligence: Vision pipeline processing 20+ vendor catalogs per quarter. Recovers several days of buying labor per cycle. Cost per catalog run: under $2.
Pre-factory validation: Catches bill of materials discrepancies before production starts. Each missed discrepancy previously cost a week of development time. Revision cycles cut by more than half.
Freight rate intelligence: Live rate routing replacing stale historical averages. Prior estimation gap: over 4× between projected and actual unit cost. Every margin calculation downstream was built on the wrong number.
Order and fraud intelligence: Real-time risk scoring with agentic review. Manual review rate reduced by more than two-thirds. Fraud patterns surface and adapt without rule rewrites.
Duty optimization: Automated classification across thousands of SKUs. A small composition shift can move a duty bracket by several percentage points. Opportunities identified before production locks.
Replenishment conflict detection: Sub-5-second validation on weekly allocation submissions. Caught a single batch error that would have cancelled thousands of units before it reached the warehouse floor.

These are a handful of the production-deployed agentic systems currently running inside one enterprise. Each one live, actively used, and improving through the work itself.

[05] Work with Keelo

Bring us the workflow that matters too much to leave as a prompt experiment.

We will help decide what should be automated, what should remain human, what must be governed, and what system needs to exist around the model.

We take on a small number of engagements. The work has to matter — to the business, to the people doing it, and to us. We are not interested in pilots that sit in a deck. We are interested in systems that change how a company operates.

Discuss an implementation