Introducing the Universal Managed Agents API.Read the announcement

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Our engineers sit with your team and build the outcome end to end, from the first eval to production.

How an embed runs

  1. Week 1

    Kickoff

    With your decision makers: the problem, the constraints, what a win looks like.

  2. Weeks 2–4

    Ontology

    We map your data, systems and vocabulary into a model the agent can reason over.

  3. Month 2

    Benchmarks

    An eval set built from your real cases, scored the way your team would score them.

  4. Month 2–3

    First agent

    The first version, running in your stack against the benchmarks.

  5. Month 3+

    Iterate

    Score, fix, ship. Every change runs the evals before it reaches production.

  6. Month 4+

    Handoff

    The agent, the evals and the playbook are yours. We stay on call.

What does a deployment look like?

Three example engagements, kickoff to handoff.

Week 1

Kickoff

Northgate Bank engaged Brainbase on May 24 to fix its KYC flow, turn it into an agent, and run it on-prem with us.

A KYC flow turned into an agent, running in the bank's own data centre, operated together.

Northgate is a mid-sized retail bank with about 1.4 million customers and a compliance team of sixty. Every new account, and every account that trips a periodic review, passes through a Know Your Customer check. In 2025 that check took a median of 4.2 days, a backlog of eleven thousand cases sat open at any time, and the team was rejecting roughly a fifth of applications on incomplete documents rather than on risk.

On May 24 the head of Operations, the Chief Compliance Officer and the CTO met with two Brainbase engineers and a solutions lead in Northgate's offices. The first day was not about agents. It was about the flow: who touches a case, in what order, which systems they open, and where the time actually goes. By the end of it the team had walked the floor with three analysts, sat through nine live reviews and mapped forty-one distinct steps between an application landing and a decision being recorded.

The second and third days turned that map into an outcome. Northgate did not want a chatbot for analysts. It wanted the median case closed in under a day, with analysts spending their time on the cases that carried real risk and the regulator seeing a clearer audit trail than before, not a murkier one. Those became the KPIs. The constraints came from Compliance and Security: nothing leaves the bank's own data centre, every decision is explainable to a human reviewer, and the agent never issues a final rejection on its own. Northgate had no platform team to run an agent, so the brief also said that Brainbase would deploy and operate it with them, on their hardware, rather than hand over a repo and leave.

The week closed with a one-page brief signed by all three executives. It named the outcome, the numbers, the constraints, the systems in scope, and a date for the first working version. Everything after this point was measured against that page.

The brief

Outcome
Median KYC case closed in under 24 hours
Baseline
4.2 days median, 11k open cases
Quality bar
No increase in false approvals; regulator-ready audit trail
Constraints
Data stays in Northgate's data centre; human makes every rejection
Deployment
On-prem, deployed and operated with Brainbase
First version
Six weeks from kickoff

What we help with

Speccing out the business outcome

The problem, the constraints, and what a win looks like in numbers.

Building the ontology

Your data, systems and vocabulary mapped into a model the agent can reason over.

Defining success

Evals built from real cases, and the metrics that tell you the agent is moving the business.

Setting up the first version of the agent

Running in your stack, against the benchmarks, from day one.

Iterating towards the business objective

Score, fix, ship. Every change runs the evals before it reaches production.

Training your team

Your engineers own the agent, the evals and the playbook. We stay on call.