Agent assessment
We map a process with your team and find where an agent removes work, not just where it speeds it up.
- Process map and prioritised use cases
- Impact and feasibility for each case
- Proposed architecture, data and risks
AI agent implementation · Barcelona
AI agents that do real work inside your processes.
In production, measurable, with people in control.
01 · What we do
From an idea to an agent in production.
We find where an agent makes a measurable difference, build it with product discipline and stay to keep it improving. Clear scope, clear outcomes.
Pilot in production
We build one agent for one real process, connected to your tools and used by your team.
- An agent running on real work
- Evaluations and guardrails from day one
- Before-and-after metrics
Operate and evolve
Agents need care after launch. Models change, processes change and new use cases appear.
- Monitoring and quality reviews
- Continuous improvement
- New agents on the same foundations
Where agents usually help first
- Back-office operations
- Finance and reporting
- Customer support triage
- Sales and proposal preparation
- Internal knowledge and documentation
- Data entry and reconciliation
02 · How we work
Product discipline, applied to agents.
01
Start from the process
We look at the work before the technology. Sometimes the best agent removes a step entirely.
02
Production, not demos
A pilot only proves something if it runs on real work with real users.
03
Measure it
Evaluations, metrics and running costs are part of the delivery, not an extra.
04
Keep humans in control
Automation is useful. Abdication isn’t.
03 · What we believe
Most software was designed first. AI was added later.
We start from the opposite direction.
When software can understand context, generate, reason, converse and act, the product itself should change.
Not just the feature set. The architecture. The interface. The workflow.
AI is a system capability, not an API bolted on afterwards.
Models should shape the architecture of the product, not simply sit behind a button.
Interfaces should change when software can understand and respond.
AI-native interfaces can explain, ask, adapt and reveal different levels of detail.
AI-native products should remove steps, not just make old ones faster.
The opportunity is not only automation. It is redesign.
04 · The lab
We build to learn.
Alongside client work we run our own experiments. What we learn there goes straight into the agents we build.
AI-native finance
What changes when financial planning starts from conversation, context and live data instead of static spreadsheets?
Reasoning interfaces
What should an interface look like when software can explain not only an answer, but how it got there?
Decision memory
Can software preserve why a decision was made, not just what was decided?
05 · Notes from the lab
What we’re learning.
Notes, essays and observations from building with AI.
Featured note / 01Coming soon / 02 Coming soon / 03 Coming soon / 04 Coming soon / 05
Explore all notes AI-native, not AI-enabled
Why adding AI to existing software isn’t enough.
What should software become because AI exists?
A product question for a new capability.
The problem with adding AI to old workflows
Why the workflow itself deserves a second look.
What should remain human?
Designing for control, inspection and correction.
A citation is not proof
What traceability can and cannot tell us.
06 · The question
The interesting question isn’t where to add AI.
It’s which work should change because AI exists.
Coral Data Lab designs and implements AI agents for teams, from Barcelona. Tell us which process you would like to change.
Talk to us