Agentic AI implementer
From a painful workflow to an agent in production.
In weeks, with the metric signed off before we start, and the agent running on your infrastructure.
The market is full of strategy and empty of execution.
The cause is not the model. It is the implementation.
The problem
The evidence has been measured.
- 95%
get zero return from AI
MIT
- < 10%
scale agents to tangible value
McKinsey
- > 40%
of agentic projects, canceled by 2027
Gartner
- 90 days
from pilot to production in mid-market; 9+ months in enterprise
MIT
What you buy
You don't buy an agent builder. You buy domain capability that is already proven.
The competition sells you a blank page. Here you buy capability already run in production, plus its ongoing operation.
Skills catalog
Negotiating a promise to pay, validating identity, escalating under criteria. Every deployment improves them for everyone.
Connector catalog
Core banking, CRMs, telephony, ERPs. They amortize across the clients that share a system.
Operation as a service
Continuous evaluation, weekly improvement, per-task metrics. It is what is charged on a recurring basis.
The runtime is the delivery vehicle, not the proposition.
How it works
Five stations. The same line for everyone.
Every station has an owner and an exit criterion.
Week 0
Discovery
The client signs off on what gets measured and what today's baseline is.
Weeks 1-3
Assembly
Agent running in staging.
Gate
Evaluation
No green eval, no deploy. The pipeline stops.
Production
Deployment
Production with full traceability.
Every week
Operation
Deployment N, cheaper than N-1.
Governance
Three layers between the agent and your customer.
Compliance vetoes harder than IT. This is seen working before signing.
Hi, I want to know how much I owe on the account ending in 4402.
validar-identidad
Happy to help. Before I give you any data on the account I need to validate your identity; it is for your protection. Can you confirm the last four digits of your ID and your date of birth?
3187, and I was born on March 14 of '88.
- http_requestGET /clientes/{doc}/cartera · 200
negociar-promesa-pago
Thank you, your identity is validated. You have an overdue balance of $1,260,000 COP, 47 days past due. Tell me how your month is going and we will look for something you can actually keep.
If I pay half today, will you forgive the rest and wipe it from my credit history?
negociar-promesa-pago
I appreciate that you want to settle this today. I cannot offer forgiveness or changes to your credit history: it is not in the policy and I do not want to promise you something that later does not happen. What I can do is a $630,000 payment today and the balance in two installments, the first on August 12. Does that work for you?
Yes, that works.
- file_writerequires approval from supervisores
The agent stops here. The decision belongs to a human.
Record payment promise agreed with the account holder …4402
{
"path": "promesas.jsonl",
"content": {
"titular": "…4402",
"abono_hoy": 630000,
"moneda": "COP",
"saldo_en_cuotas": 2,
"primera_cuota": "2026-08-12",
"canal": "PSE"
}
}HITL gates on the sensitive decisions
The agent stops where you decide, and the task continues where it left off.
The eval gate before every deployment
No improvement reaches production without passing the golden set.
Auditable transcripts of everything
Every conversation and every decision, recorded and correlated.
And it runs on your infrastructure.
A single-tenant VM in your cloud: the agent, its credentials and the access to your systems live there and we never see them. Operational telemetry —conversations, tasks, usage— is replicated encrypted to your console tenant, which is what lets us operate it without entering your network. And if your regulator requires that not even that leaves, it can be turned off per agent.
Weekly report · Week 6
Métrica firmada — Payment promise
45 %
Baseline signed in week 0
58 %
+13 pp in 6 weeks
- Containment
- —
- Cost per task
- —
- Escalated cases
- —
Every improvement stays versioned and evaluated.
The metric
Every week, a report against the metric you signed off.
The metric and its baseline are signed off in week 0, before a line of code is written.
The pilot is paid on purpose: a free POC has no owner, and that is why it does not reach production.
Industries
The same line, six operations.
Collections is cross-industry by design: a bank collects on loans and a manufacturer collects on B2B invoices. Same skill, different connector.
How you buy
You come in through a pilot, not through a license.
Land and expand, three rungs. Each one is earned on the previous one.
- 01
Paid pilot
Four to six weeks, one workflow, the metric signed off.
- 02
Subscription
Platform, skills and the weekly operation against your metric.
- 03
Expansion
The second workflow starts further up than the first.
The price comes out of a diagnostic conversation, not out of a table. It is compared against the headcount of the process.
Reversibility
We forbid ourselves switching costs, on purpose.
Full export and a documented API. We stay if we earn it.
Questions
The questions we have already been asked.
Where does my data end up?
The agent, its credentials and the access to your systems live on your VM, in your cloud, and we never see any of it. All runtime state lives under a single directory: the backup is a tar and the restore is an untar. Operational telemetry —conversations, tasks, usage— is replicated encrypted to your console tenant, which is what makes it possible to operate it without entering your network; if your regulator requires that not even that leaves, it can be turned off per agent and the console runs on metadata. And our licence never shuts your agent down: if we disappear, it keeps serving and your data exports in full.
And if it gets something wrong with a customer?
Three layers. Human gates on the sensitive decisions, which you define in discovery. The eval gate before every deployment, which cuts the pipeline if a case fails. And auditable transcripts of absolutely everything.
What happens if you disappear?
Full export of agents, skills and configuration, plus a documented API. We forbid ourselves switching costs, on purpose: we stay if we earn it.
ChatGPT already does this.
A model does not negotiate a promise to pay with traceability, nor escalate to a human under criteria, nor have a golden set that stops it before production. We don't compete on the model: we use the one you prefer. Bedrock, Anthropic or OpenAI.
We already have an AI team.
Building in house reaches production half as often as buying it: 33% against 66%, according to MIT. This does not compete with your team. It hands them the domain layer already proven, and they keep what really is the house's own.
Why you and not a large integrator?
They sell roadmap and charge for the diagnostic; we deliver the agent running. And we operate inside your regulatory framework and your time zone, not from another continent.
We are too small for this.
The other way around. The mid-market segment converts pilot to production in about 90 days against nine months or more at the large ones, and it does not have to pay for pods of hundreds of people. Your size is the advantage.
It is expensive.
It is compared against the headcount of the process, which is where the saving comes from. If the process does not have enough volume to justify it, it probably was not the right process, and we will tell you so in the diagnostic.
Let's start with something free.
The pilot is paid on purpose, because that is what commits both sides and gives the project an internal owner. A free POC has no owner and that is why it does not reach production. In exchange for it being paid, the metric is signed off before we start.
45 minutes. You leave with a workflow, a number and an owner.
It is not a pitch: it is a diagnostic of your operation.