Question 1

What is Broccoli™ Agentic AI Platform?

Accepted Answer

The enterprise-grade Broccoli™ platform is an Agentic AI Platform built specifically for regulated financial services. It orchestrates multiple AI agents across complete business processes, combining business rules, machine learning, LLMs and human oversight based on what each task requires. The platform is LLM-agnostic, secure by design and compliance-ready, with unified controls across capabilities such as Intelligent Extraction, Agentic RAG, Business Rules, Conversational Interface, Agentic Farm, Agentic Data Intelligence, Testify and Multi-Linguistic. Instead of replacing existing core banking systems, CRM or infrastructure, Broccoli™ works across them to orchestrate intelligence, automate processes and enable reusable agentic workflows at enterprise scale.

Question 2

What exactly is Broccoli™, and how is it different from an RPA tool or a chatbot bolted onto our systems?

Accepted Answer

RPA automates a click. A chatbot answers a question someone typed. Both are useful, and both stop at the edge of a single task.A use case like onboarding, a credit decision, or resolving a servicing ticket involves several of those tasks strung together, each one needing judgment about what happens next. The enterprise-grade Broccoli™ platform is built for that whole chain: multiple agents, each owning a piece of the process, working under business rules and, where the situation calls for it, human sign-off. Rules take the decisions rules can make cleanly. Machine learning takes the pattern-based ones. LLMs get involved only where language understanding or reasoning earns its cost.Put simply: RPA and chatbots automate a step. Broccoli™ runs the process around it.

Question 3

Does Broccoli™ replace our core banking systems, CRM, or the AI tools we've already deployed?

Accepted Answer

The "vendor and hyperscaler agnostic" claim on this page isn't there to sound flexible, it describes what happens at deployment. Your core banking platform stays the system of record. Your CRM stays where relationship data lives. Broccoli™ sits across both, orchestrating the intelligence layer: extraction, decisioning, routing, without requiring you to migrate data or rip out infrastructure that's already working.If your team has already deployed specific AI assistants or models, model-agnostic orchestration usually means those can sit inside the broader system rather than get replaced by it. Whether that's the right call for a specific deployment depends on what's already in place, worth a direct conversation rather than a generic yes.

Question 4

What does "Minimum Effective Intelligence" actually mean? Why not just use the most powerful model for everything?

Accepted Answer

Because the most powerful model isn't automatically the right one, and in a regulated environment, it's rarely the most auditable or the cheapest either.The approach asks a specific question at every decision point in a workflow: what's the simplest system that can make this call reliably? A business rule handles what's deterministic: validating a field, checking a threshold, fast and fully auditable. Machine learning takes over where the pattern is statistical rather than binary. An LLM gets involved only where a task genuinely needs language understanding or a judgment call a rule can't encode. And wherever the business has decided a human needs eyes on it before it goes out, that checkpoint doesn't move, no matter how capable the underlying model becomes.Complexity gets added when the problem demands it, not by default. It's also a large part of why deployments are built to show risk-adjusted ROI in quarters rather than years: LLM-level compute and latency get reserved for the decisions that need them.

Question 5

How is a use case actually structured underneath, once it's built on Broccoli™?

Accepted Answer

Three layers.Use Case sits at the top, the actual business outcome, like account opening or a KYC refresh. Agents sit underneath, each one responsible for a distinct piece of reasoning within that use case: one interprets documents, another runs a compliance check, another manages the conversation with the customer. Tools and Skills sit below the agents, the specific actions an agent calls on to get something done, like querying a database, calling an API, or triggering a downstream workflow.Keeping those as genuinely separate layers is what makes a build reusable instead of one-off. A Tool built for one use case can be called by an Agent working on a completely different one. That's the literal mechanism behind "build once, deploy across journeys," not a slogan, an architectural consequence.

Question 6

Is Broccoli™ locked into a single LLM provider?

Accepted Answer

Say a provider changes its pricing, a client's procurement policy only approves certain vendors, or a better model ships next quarter. None of those should force a rebuild, and on Broccoli™, they don't.Business rules, agent logic and orchestration sit above the model layer rather than inside it, so swapping which model handles a given task, or running two models against the same task to compare outputs, doesn't touch the workflow itself. The model is a component you can change, not a foundation the rest of the system is poured onto.For a regulated institution this is more than convenience. If a regulator, a data residency requirement, or an internal risk decision ever calls for a specific model to be swapped out, that's a configuration change, not a re-platforming project.

Question 7

How does Broccoli™ handle governance and compliance? Is that something we configure separately for each deployment?

Accepted Answer

Underneath every capability on the platform, not configured separately for each one.Intelligent Extraction, Agentic RAG, the Conversational Interface, all of it runs inside a single Controls layer, so access rules, audit visibility and compliance checks stay consistent whether an agent is pulling a field from a document or holding a conversation with a customer. Most agentic deployments end up with the opposite problem: one control framework for the chatbot, a different one for document processing, and someone tracking in a spreadsheet what the retrieval system is technically allowed to touch.Broccoli™ was built the other direction. The control layer came first. The capabilities were built to run inside it, not around it.

Question 8

How long does it actually take to get a use case live, and what does "production-ready" mean here?

Accepted Answer

It means built to run in a live, regulated environment from the start, not proven in a sandbox and then quietly re-engineered for production months later.Most agentic AI pilots stall at a predictable point: the demo works, and then someone in risk or infrastructure asks how it behaves under real transaction volume, inconsistent data quality, and actual audit requirements, and the honest answer turns out to be "we'd need to rebuild it." Broccoli™ deployments are built against that bar from day one, which is also why they're designed to show risk-adjusted ROI within quarters rather than years. There's no separate bill for turning a proof of concept into something real.Exact timelines vary by use case and by how ready your underlying data and systems are, worth a direct conversation rather than a number we'd guess at here.

Question 9

Who owns the data and the agents once Broccoli™ is deployed inside our institution?

Accepted Answer

Your data doesn't move into a new store that Dailoqa controls as a condition of running the platform. Broccoli™ operates against your existing systems and your existing data residency and access setup, built to sit inside your controls, not create a new set of them elsewhere.What that means precisely for agent configurations, IP and commercial terms depends on jurisdiction and deployment model, particularly given how differently the UK, UAE, India and Switzerland each regulate this. That's genuinely a contractual conversation rather than something a public FAQ should answer in general terms. We'd rather point you to the right person than give you a version of the truth that only applies sometimes.

Question 10

Does Broccoli™ only work in English, or can it run in the languages our customers actually use?

Accepted Answer

A KYC conversation conducted in Dubai, a servicing call handled in Mumbai, a compliance disclosure issued in Zurich, each one carries regulatory weight in the language it happened in. A rough machine translation sitting on top of an English-only system isn't good enough when that transcript might end up in front of a regulator.Multi-lingual handling is a core capability on Broccoli™, not a layer added afterward. Speech-to-text, translation and text-to-speech work together, built for hyper-personalised engagement in the customer's actual language rather than a generic multilingual mode bolted onto an English-first core.

Question 11

What actually happens when an agent isn't confident, or gets something wrong?

Accepted Answer

It gets routed to a person. That handoff is a designed step in the sequence the platform runs on: rules first, then ML, then LLMs, then human oversight. Where that last checkpoint sits in a given use case is a decision your team makes, not something fixed by the platform itself.A low-confidence extraction, an edge case a business rule doesn't cover, a judgment call your institution has decided shouldn't be automated, each of those hands off rather than getting pushed through regardless. Combined with the Controls layer running underneath every capability, there's a visible trail of what an agent did, why, and where the handoff happened. That's the difference between explainable AI as something you can demonstrate to a regulator, and explainable AI as a phrase on a slide.

Question 12

How do you test and validate agentic workflows before they go live?

Accepted Answer

Traditional software testing assumes a fixed input produces a fixed output. Agentic systems don't work that way, the same input can produce a different reasoning path each time, and a test suite built for deterministic code won't catch that kind of failure.Testify was built specifically for this: test automation designed around how agentic systems behave, checking not just whether an agent reached the right output but whether it got there the right way, consistently, across the range of inputs a live financial services environment produces. That's a different discipline from adapting Selenium or a generic QA framework to a job it wasn't designed for.It's also part of why "production-ready" means something specific here. A use case isn't called that until it's been validated against how agents actually fail, not how traditional software fails.

The Model Is Not the Problem. The Architecture Is.

This Is What Broccoli™ Solves.

Minimum Effective Intelligence. Maximum Business Impact.

Production Ready

​Fully Customisable

Compliance First

Minimum Effective Intelligence. Maximum Business Impact.

Production Ready

​Fully Customisable

Compliance First

Legacy lock-in

Vendor and hyperscaler agnostic. No singular dependency.

Fragmented governance

Unified security and controls across every AI system.

Model limitations

Model-agnostic. Swap, combine and orchestrate without rebuilding.

Siloed deployments

Reusable by design. Build once, deploy across journeys.

Tech debt

Modernised architecture. Simpler, more resilient, ready to scale.

Intelligent Extraction

Agentic RAG

Business Rules

Conversational Interface

Agentic Farm

Agentic Data Intelligence

Testify

Controls

Multi-Linguistic

See Broccoli™ in a Real Environment.

Frequently asked questions

What is Broccoli™ Agentic AI Platform?

What exactly is Broccoli™, and how is it different from an RPA tool or a chatbot bolted onto our systems?

Does Broccoli™ replace our core banking systems, CRM, or the AI tools we've already deployed?

What does "Minimum Effective Intelligence" actually mean? Why not just use the most powerful model for everything?

How is a use case actually structured underneath, once it's built on Broccoli™?

Is Broccoli™ locked into a single LLM provider?

How does Broccoli™ handle governance and compliance? Is that something we configure separately for each deployment?

How long does it actually take to get a use case live, and what does "production-ready" mean here?

Who owns the data and the agents once Broccoli™ is deployed inside our institution?

Does Broccoli™ only work in English, or can it run in the languages our customers actually use?

What actually happens when an agent isn't confident, or gets something wrong?

How do you test and validate agentic workflows before they go live?

The Model Is Not the Problem.
The Architecture Is.

Minimum Effective Intelligence.
Maximum Business Impact.

Production
Ready

Fully
Customisable

Compliance
First

Minimum Effective Intelligence.
Maximum Business Impact.

Fully Customisable